euro-mediterranean economics and finance review

Transcription

Euro-Mediterranean
Economics and Finance
Review
ISSN 1967-502X
Editors
Mondher Bellalah and Jean-Luc Prigent
Aims and Scope
The Euro-Mediterranean Economics and Finance Review is a peer-reviewed
research journal of the Mediterranean Association of Finance Insurance and
Management (AMFAM). It is intended to develop research in economics,
finance and management with a special emphasis on the main issues and
problems regarding the Euro-Mediterranean zone. The journal is committed to
excellence by publishing high quality research papers in economics and finance
with theoretical and empirical contents as well as invited viewpoints (2000 to
4000 words) written by well-known experts.
The journal’s editorial policy is to publish original articles that obey the accepted
standards and to improve communications between academies practitioners and
policymakers at both national and international levels. While recognizing the
Euro-Mediterranean origins of the research papers, the journal is also open to
research that shows diversity in theoretical and methodological underpinning.
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2
THE EURO-MEDITERRANEAN ECONOMICS AND FINANCE REVIEW
Editors
Mondher Bellalah, University of Cergy-Pontoise, France
Jean-Luc Prigent, University of Cergy-Pontoise, France
Advisory Board
Harry Markowitz, Nobel Prize Laureate, University of California, San Diego, USA
Edward Prescott, Nobel Prize Laureate, Arizona State University, USA
Associate Editors
Michael Adler
Columbia University, USA
Rudy Aernoudt
Brussels Business School, Belgium
Aman Agarwal
Indian Institute of Finance, India
Gordon Alexander
UCLA, USA
Mohamed Arouri
University of Auvergne, France
Mohamed Ayadi
HEC Montreal, Canada
Giovanni Barone-Adesi
University of Lugano, Switzerland
Hatem Ben Ameur
HEC Montreal, Canada
Jean-François Boulier
CA Asset Management, France
Michael Brennan
UCLA, USA
Eric Briys
Cyberlibris, Belgium
Harvey R. Campbell
Duke University, USA
K.C. Chen
California State University, USA
Ephraim Clark
Middlessex University, UK
Georges Constantinides
University of Chicago, USA
Manuel José Da Rocha Armada
University of Minho, Portugal
Gabriel Desgranges
University of Cergy-Pontoise, France
Joao Duque, ISEG Portugal
Alain Finet
ULB, Belgium
Philippe Foulquier
EDHEC Business School, France
Bertrand Jacquillat
IEP, France
Frank Janseen
Catholic University of Louvain, Belgium
Cuong Le Van
PSE & University of Paris 1, France
Michel Levasseur
University of Lille 2, France
Patrick Navatte
University of Rennes 1, France
André de Palma
University of Cergy-Pontoise, France
Bernard Paranque
Euromed Management, France
Kuntara Pukthuanthong
San Diego University
François Quittard-Pinon
University of Lyon 1, France
Richard Roll
UCLA, USA
Olivier Scaillet
HEC, Genève, Switzerland
Stefan Straetmans
Maastricht University, Netherlands
Héracles Vladimirou
University of Cyprus, Cyprus
Jose Scheinkman
Princeton University, USA
Paul Willmott
Editor Derivatives, UK
Table of Contents
Volume 7
Number 4
2013
Page
Editorial
5
The performance of hybrid models in the assessment
of default risk
Sami Zouari
7
Does co-integration and causal relationship exist between
the non-stationary variables for Chinese bank’s profitability?
Empirical evidence
Omar Masood, Marc Bradford, Jean-Jacques Levy
25
Dependence structure between the equity market and
the foreign exchange market after the Greek crisis: Evidence
from France and Germany
Adel Boubaker, Jaghoubbi Salma
41
Financial development and income inequality in the MENA
region
Zied Saadaoui, Maher Gassab
53
Editorial
This special volume handles new emerging problems in Finance and comprises
some selected papers from the IFC international Finance conference, organized
by ISC Paris - the University of Cergy-Pontoise and REMEREG in Paris, in March
2013.
The first paper, written by Sami Zouari, is interested in the performance of
hybrid models in the assessment of default risk. The paper uses simultaneously
fundamental analysis and contingent claim analysis into a hybrid credit risk
model of French companies. It provides an answer to the following question:
how the combination of continuous market assessments and the values from
financial statements improve the estimation of the probability of default.
The second paper by Omar Masood, Marc Bradford and Jean-Jacques Levy
provides an answer to the co-integration and causal relationship between the
non-stationary variables for bank’s profitability. The empirical analysis used the
co-integration and other tests.
The third paper by Adel Boubaker and Jaghoubbi Salma studies the dependence
structure between the equity market and the foreign exchange market after the
Greek crisis. The study investigates the dependence structure and tests the degree
of dependence between financial returns. It uses two measures: correlations and
copula functions. The findings could have some impact for global investment
risk management.
The last paper, written by Zied Saadaoui and Maher Gassab handles the financial
development and income inequality in the MENA region. Using a sample of
11 Middle East and North Africa countries, the research tests the impact of
financial development on income inequality. Thanks for your interest in the Review.
Editors in charge:
Mondher Bellalah and Jean-Luc Prigent
The performance of hybrid models
in the assessment of default risk
Sami Zouari*,THEMA, University of Cergy-Pontoise
Abstract
In this paper, we have combined fundamental analysis and contingent claim analysis into a hybrid model of credit risk measurement with French companies listed on the Paris Stock Exchange
(Euronext Paris). Our goal is to assess how the combination of
continuous assessments provided by the market and the values
derived from financial statements improve our ability to forecast
the probability of default. During a first phase, the probability of
defaults are estimated using both methods separately, and subsequently, the probability of default of the structural model are
integrated at each point in time in the non-structural model as an
additional explanatory variable. The appeal of the hybrid model
allows the probability of default to be continuously updated by
integrating market information via the probabilities of default
extracted from the structural model. Our results indicate that default probabilities extracted from the structural model contribute
significantly to explaining default risk when included in a hybrid model with accounting variables.
Keywords: Default risk, structural model, non-structural model,
hybrid model, probit model, Probability of default.
JEL Classification: G21, G33
1. Introduction
Credit risk refers to the risk due to unpredicted changes in the credit quality of
a counter party or issuer and its quantification is one of the major frontiers in
modern finance. The creditworthiness of a potential borrower affects the lending decision and the credit spread, since it is uncertain whether the firm will be
able to perform its obligation. Credit risk measurement depends on the likelihood of default of a firm to meet it’s a required or contractual obligation and
on what will be lost if default occurs. When one considers the large number of
corporations issuing fixed income securities and the relatively small number of
actual defaults might regard default as rare event. However, all corporate issuer
have some positive probability of default. Models of credit risk measurement
have focused on the estimation of the default probability of firms, since it is
the main source of uncertainty in the lending decision. We may distinguish two
large classes of credit risk models on the basis of the analysis they adopt. The
first class, the set of traditional models assume the fundamental analysis, called
the non-structural model. The goal of these models that goes back to Beaver
(1966) and Altman (1968) is to distinguish which factors are more significant in
*
Sami Zouari is at THEMA, University of Cergy-Pontoise, 33 bd du port, 95011, Cergy, France,
email: [email protected]
8
assessing the credit risk of a firm. The second class, called structural models assume the contingency claim analysis. The philosophy of these models goes back
to Black-Scholes (1973) and Merton (1974) and assumes corporate liabilities as
contingent claims on the assets of the firm1.
In this paper, we investigate the hybrid contingent claim approach with French
companies listed on the Paris Stock Exchange (Euronext Paris). Our goal is to
assess how the combination of continuous assessments provided by the market
and the values derived from financial statements improve our ability to forecast
the probability of default.
The structural model of Merton has the advantage of being flexible, since the
probability of default can continually be updated with changes in the value of
corporate assets. Its main drawback is that it may over-or underestimate the
probability of default, since asset values are unobservable and must be extrapolated from the share prices. On the other hand, the non-structural model of Altman is more accurate because it uses the accounting data of companies, but it
is less flexible. Because the frequency of information is generally annual, the
probabilities of default cannot be updated during the fiscal year. The quarterly
financial statements can be found, but they are not always audited by an external accounting firm.
The Bank of England estimated the hybrid model with data from British companies and found some interesting results. During a first phase, the probability
of defaults are estimated using both methods separately, and subsequently, the
probability of default of the structural model are integrated at each point in time
in the non-structural model as an additional explanatory variable. The appeal of
the hybrid model allows the probability of default to be continuously updated
by integrating market information via the probabilities of default extracted from
the structural model. In this paper, we apply the hybrid model to French companies listed on Paris stock exchange (Euronext Paris).
This paper is organized as follows. Section 2 reviews the main models in the
literature. Section 3 presents the estimated structural model and describes the
data used and finally section 4 presents the estimation of the hybrid model and
summarizes the main results.
2. Review of key models for risk assessment of default
2.1 Non-structural models
Traditional non-structural models adopt fundamental analysis and try to find
which factors are important in explaining the credit risk of a company. They assess the significance of these factors, mapping a reduced set of financial ratios,
accounting variables and other information into a quantitative score. The latter,
can be interpreted as a probability of default and can be used as classification
system2.
1 Another widely used category of credit risk models is the reduced form approach where the dynamics of default are
given exogenously by an intensity or compensator process. For a review of these models see Jarrow, and Turnbull
(1995), Jarrow, Lando and Turnbull (1997), Duffie and Singleton (1999).
2 For a review of traditional models see: Jones (1987) and Cauette, Altman and Naraynan (1998), Saunders (2002).
VOLUME 7, NUMBER 4, 2013
9
In 1966, Beaver has introduced the univariate approach of discriminant analysis
in the default risk of firm’s explanation. Altman in 1968 has extended it to a multivariate context and developed the Z-Score model. It weights the independent
variables (financial ratios and accounting variables) and generates a single composite discriminant score. In 1977 Altman, Haldeman and Narrayman have developed the ZETA model, which integrated some improvements to the original
Z-Score approach. Then the binary dependent variables models, known as the
logit and probit model, have been used in bankruptcy prediction3. Ohlson (1980)
used logit methodology to derive a default risk model known as O-Score. Probit
(Logit) methodology weights the independent variables and allocates scores in a
form of failure probability using the normal (logistic) cumulative function.
Mester (1997) have recognized the prevalent use of the binary credit risk models:
70 % of banks have used them in their non-listed firm lending procedure.
Several banks use this method for privately and publicly traded companies, either by buying a model, such as RiskCalc Moody’s, or by programming their own
estimate. One problem they often face is to build an appropriate proper database.
Very often, credit files are not computerized or do not contain historical data.
The main advantage of non-structural models is their accuracy in estimating
probabilities of default. In addition, they are easy to use for financial institutions equipped with solid management systems of database and may produce
very accurate default probabilities. Nonetheless, these models are not flexible,
because they need information from financial statements. Thus, it is very difficult to update the probabilities of default over a year. Some financial institutions may require reporting on a quarterly basis, but they are rarely audited by
accounting firms.
2.2 Structural Models
The original Merton model is based on some simplifying assumptions about the
structure of the typical firm’s finances. The event of default is determined by the
market value of the firm’s assets in combination with the liability structure of
the firm. When the value of the assets falls below a certain threshold, the firm is
considered to be in default. The main criticism leveled at Merton’s model is that
it does not account for the possibility that the firm may default before the debt
matures. To improve this basic model, several extensions have been suggested
in the literature.
Crosbie and Bohn (2002) summaries KMV’s default probability model. KMV’s
default probability model is based on a modified version of the Black-ScholesMerton framework in the sense that KMV allows default to occur at any point in
time and not necessarily at the maturity of the debt. In this model multiple classes of liabilities are modeled. There are essentially three steps in the determination of the default probability. The first step is to estimate the market value and
volatility of the firm’s assets, the second step is calculate the distance-to-default,
the number of standard deviations the firm is away from default, and the third
step is to transform the distance-to-default into an expected default frequency
(EDF) using an empirical default distribution.
3 Jones (1987) in his review of bankruptcy literature, concludes that binary dependent variable models do not led to
notable improvements in the predictive power of fundamental analysis when compared to the earlier LDA models.
10
Brockman and Turtle (2003) propose using barrier options. Thus, rather than
stockholders who wait for the debt to mature before exercising a standard European call option, we have a down-and-out option on the assets in which lenders
hold a portfolio of risk-free debt and a short put option combined with a long
down-and-out call option on the firm’s assets. The last part gives them the right
to place the company into bankruptcy when they anticipate that its financial
health can only deteriorate. Wong and Choi (2004) demonstrate that estimating
the parameters of the Brockman and Turtle (2003) model by maximum likelihood yields results that resemble those from the iterative estimation method
used in this literature when the theoretical model is Merton’s. The appeal of the
maximum likelihood method is that it allows for statistical inference or, more
specifically, calculating descriptive statistics for the estimated parameters, such
as the value of the firm.
Tudela and Young (2003) present an application of the hybrid model. This application uses barrier options with a down-and-out call option. The authors
estimate various models on data from non-financial English firms for the period 1990–2001. They use data on firms that did, and did not, default, for their
estimates of probabilities of default in the structural model. First, they verify
whether the two firm types represent different predicted probabilities of default.
Second, they compare their hybrid model with other non-structural models to
verify whether the additional probability of default (PD) variable is significant
for explaining probabilities of default. Third, they measure the performance of
their model with power curve and accuracy ratio type instruments.
3. Estimation of the probabilities of default with the structural model:
application of the tudela and young model (2003) (the bank of england
model)
3.1 Model description
In this model, the authors use the theory of barrier options4 and more precisely
the call option down-and-out, which vanishes when the underlying asset reaches the barrier. In this model we assume that the capital structure consists exclusively of debt and equity (as Merton). The level of debt is denoted B and (T-t)
represents the time remaining to maturity of the debt, the value of the firm is At
and the value, at time t, of the debt maturing at time T is V (A, T, t).
The share value at time t is f (A, t). Therefore the total value of the firm at time t is:
At = V (A, T, t) + f (A, t)
(1)
To derive the probability of default using a barrier option we suppose that the
value of the firm’s underlying assets follows the following stochastic process:
uA = A dA dt + σA A dz
(2)
4 Other equity-based models of credit risk that use the concept of barrier options are Black and Cox (1976), Longstaff and Schwartz (1995) and Briys and de Varenne (1997).
11
As to the liabilities, assume, on one hand, that the firm’s liabilities L are the sum
of short-term liabilities plus one-half of long-term liabilities. On the other hand,
we assume that L follows
a deterministic process:
dL = μL L dt (3)
We note the asset-liability ratio by k:
k=
A
L
(4)
~
A default occurs when k falls below the default point called k it at any time. To
estimate the probability of default we need to model how k changes over time.
Differentiate (4) and use (2) and (3) we get:
dk = (μA - μL) k dt + σA k dz
(5)
We define: μA – μL = μk
And σA = σk
The values of μk and σk are needed to calculate the probabilities of default.
Maximum likelihood techniques are used to obtain estimates of those two
parameters, but to build the maximum likelihood function, we need first
to derive an expression for the density function of k.
Given equation (5) we can derive the density function of ln
It can be shown that the defective density function is given by {ln
following expression:
.
} by the
(6)
12
Equation (6) represents the probability density of not crossing the barrier
and being at the point in
at time T. This expression is used to construct
the likelihood function that we must maximize in order to obtain estimates of
μk and σk. These estimates will be used to calculate the probability of default us
shown below. The probability of the firm is not defaulting until date T is given
by the probability of
~
kT > k conditionally
and N is the cumulative density function of the normal distribution. In the case
of a European call option, the probability of default equals N (u1). However, for
the barrier option we see that the term w [1- N (u2)] adjusts the probability of
default to take into account that the firm can default before the horizon date T.
~
The Bank of England set k = 1.We shall adopt this normalization. On the other
hand we assume that the ratio,
y=
X , where X represents the market capitalization of the firm and L is its
L
liabilities as a proxy for the ratio
k=
A since the value of the firm’s assets is unobservable.
L
13
We use Matlab to estimate μk and σk with the maximum likelihood method, then
we calculate the probabilities of default. Parameters μk and σk are estimated on
the basis of a 24-months window for all firms. (As starting value we take σk = 0,4
and μk =0,3). Finally, Tudela and young find that if they add some account variables in their model, the model performance increases slightly. The final model
of the Bank of England is as follows:
P D= f [probability of default (1 -2 years), profitability, Debt over assets, Cash
over liabilities Sales Growth, log number of employees, GDP]
This model will be the subject of our research, the authors have applied this
model to calculate the probability of default on data from non-financial English
firms. We will try to applying it to a sample of French listed companies but retaining other explanatory variables for the hybrid model.
3.2 Data
In this section we present the used data and explain how we built it to calculate
probabilities of default. This data is used also to estimate the hybrid model in
section 4. Our initial database contains 20 companies that did not default and 14
companies that did. The study period for the probabilities of default is from January 2004 to December 2005. The methodology we use to compute the probabilities of default with the structural model requires that our data window extend
24 months prior to the estimation period for the predicted probabilities of default in order to ensure statistical reliability. Market capitalization has a monthly
frequency while the values of debt are observed annually, thus the value of debt
is considered during the year.
3.2.1 Companies that have defaulted
Data on companies that have defaulted are from DIANE. However, 6 companies
that defaulted were removed from the database because of lack of data (accounting and / or market) or because too large a shift between the date of publication
of the last financial statement and effective date of default. Indeed many of these
companies have significant gaps between these two dates. This is explained by
the fact that most of the firms do not publish their financial statements during
the last year prior to bankruptcy. Another explanation lies in the slow process of
putting in default of certain companies. Thus we eliminated firms with a lag of
more than 18 months.
3.2.2 Companies that did not default
Accounting data on companies that did not default for the year 2005 and the
monthly market capitalizations are from Diane.
3.2.3 Various statistics
Financial firms were eliminated from the database because they do not generally
have the same structure of financial statements as non-financial firms. Thus the
final database contains a total of 23 non-financial companies, 8 of them have defaulted .The following table presents the descriptive statistics of firms retained
for analysis.
14
Table 1
Descriptive statistics of all firms retained for analysis (in million Euro)
NOT-DEFAULT
DEFAULT
Statistic
Market
value
Liabilities
Market
value
Liabilities
Mean
130,48
44,924
38,292
23,193
Median
101,128
38,714
17,073
9,248
Maximum
386,65
156,147
190,854
120,568
Minimum
27,65
3,955
11,473
7,492
Standard deviation
94,013
36,76
61,698
39,383
Skewness
1,464
1,829
2,259
2,2591
Kurtosis
4,7749
6,7184
6,122
6,1207
Number of observations
360
30
180
16
3.3 Estimation results
Estimating probabilities of default by the structural model allowed us to obtain
the following results: for companies that have defaulted, the mean of probabilities of default is 33.97% while for companies that did not default is 13.54%.
The following figures show the evolution of the probabilities of default predicted for several firms. Figure 1 show the evolution of the probabilities of
default for the ones defaulted.
Modeling the probabilities of default of these companies seems consistent with
the model since it takes the form of probabilities of default predicted higher
when approaching the year of default. Indeed, the most of the companies that
did default presents a similar evolution of the probabilities of default.
However, the results in Figure 2 are somewhat surprising. They represent an
extreme case and show an example of the overstatement of the probabilities of
default in the structural model. The model appears very sensitive to significant
fluctuations in the values of this firm’s stocks and provides the rationale for
using the hybrid model, which contains more information for conditioning the
estimates of the probabilities of default. Two smoother examples are featured
in Figure 3.
Figure 1
Monthly default probabilities (2 years) of defaulting firms
Figure 2
Monthly default probabilities (2 years) of non-defaulting firms
15
16
Figure 3
Other PDs (2 years) of non-defaulting firms
4. Hybrid model
4.1 Methodology
We did not estimate the model with a simple linear regression, since we know
that it must reflect non-linear behavior of the explanatory variables for defaults.
In addition, it is well documented that simple linear models are inappropriate
when the dependent variable is a probability. This model has the advantage of
being easy to estimate but he has the disadvantage that it leads to PDs estimated
to be out of the interval [0,1]. Thus, we must use other models which keep the
probability of default (PD) in the considered interval. This is particularly the
probit model. In this type model the dependent variable is a dichotomous variable taking the value 1 if an event occurs and 0 otherwise. In our case, the variable Yi assumes the following values:
Yi = 1 if firm I defaults, and
Yi = 0 otherwise.
17
The vector of explanatory variables (financial ratios and accounting variables…)
for firm i is denoted Xi, while β is the vector of weights of these variables.
The probit model assumes that there is a qualitative response variable (Yi*) defined by the following equation:
Yi* = β’ Xi + εi.
(7)
However, in practice Yi* is an unobservable latent variable. We rather observe the
dichotomous variable Yi such that:
Yi = 1 if Yi* >0;
Yi = 0 otherwise.
(8)
In this formulation, β’Xi is not E(Yi / Xi) as in the simple linear model, but rather
E (Yi* / Xi). From equations (7) and (8), we get.
Prob (Yi = 1) = Prob (εi > - β’Xi) = 1 - F (β’Xi)(9)
Where F is the cumulative distribution function of εi.
The functional form of F in equation (11) depends on the retained assumptions
regarding the distribution of the residual errors ( εi ) in equation (7). The probit
model is based on the assumption that these errors are independently and identically distributed (i.i.d.) and follow a standard normal distribution N(0,1). The
functional form can thus be written:
(10)
In this case, the observed values Yi are simply the realizations of a binomial process whose probabilities are given by (9) and vary from one observation to the
next (with Xi). The likelihood function can be defined as follows:
(11)
And the parameter estimates β are those that maximize i.
4.2 Variable selection
The main objective of this study is to verify whether combining the structural
and the non-structural model into a hybrid model yields a better measure of the
default risk than those obtained from structural and traditional non-structural
models estimated separately. To accomplish this, we aim to explain defaults deficiencies by estimating a probit model in which the explanatory variables are
the estimated probabilities of default from the structural model, financial ratios
and other accounting data.
The dependent variable is binary taking the value of 1 if the default occurs and 0
otherwise. Using the same methodology, we also estimate a model with only ac-
18
counting data as explanatory variables (non-structural model) and a third probit
model in which the only exogenous variable is the probability of default from the
structural model (the model that contains only structural information). Thus,
we examine the predictive power of the PD variable to explain corporate bankruptcy by integrating it in the non-structural model as an explanatory variable.
If we find that the estimated coefficient of the variable PD (resulting from the
structural model) is statistically different from zero, the probabilities of default
obtained by the structural model in this case would contain additional information that complements that of accounting data, and we will be able to use its
coefficient to update the probabilities of default when the PD from the structural
model changes.
As to the choice of accounting variables and financial ratios used in the nonstructural and hybrid models, we were faced with difficulties in the selection
of variables given the scarcity of accounting and financial data on French listed
companies that did default. To make a sound choice, we estimated the probit
model on each variable accounting separately. This enabled us to retain the most
significant ones.
4.3 Estimation results
4.3.1 Estimation of the probit model with different specifications
In this section we analyze the characteristics and performance of three models: the hybrid model, the non-structural model and the model containing only
structural information. We summarize the results of these estimations in Table 2.
In Model 1, we only use the information from the structural model by considering the mean PD(2 years) from the structural model as an explanatory variable.
The coefficient of PD is 0.15 per cent, and has the expected sign. It is a significant
factor for predicting probabilities of default, with a p-value of less than 5 per
cent and a high corrected pseudo-R2 (52.56 per cent).
In Model 2, we estimate the-non-structural model with 2 variables (the turnover
and profitability ratio). Examination of Model 2 reveals that the non-structural
specification largely outperforms the one using only information from the structural model (Model 1) in terms of its ability to explain corporate bankruptcy. The
likelihood ratio is 17.63 for the non-structural model, versus 15.62 for the structural model with only PD as an exogenous variable (the corresponding values of
R2 are 59, 34 per cent and 52.56 per cent).
In Model 3, we estimate the hybrid model by adding the probabilities of default
calculated from the structural model to the explanatory variables of Model 2. An
analysis of the results reveals that the probabilities of default from the structural
approach have an additional predictive power for corporate defaults than the
firms’ financial statements. We observe that the likelihood ratio increased from
17.63 for the non-structural model to 24.6 for the hybrid model (the corresponding values of corrected pseudo-R2 are 59.34 per cent and 82.77 per cent). Furthermore, to acquire to acquire a better understanding of the contribution of model
3 relative to that of Model 2, we repeat this analysis in Models 4 and 5, but this
time by changing the variables used in the non-structural model and in Model
19
5 we estimate the hybrid model by adding default probabilities calculated from
the structural model as an explanatory variables to Model 4.
Analysis of the results gives us the same findings confirming the predictive
power provided by incorporating the variable PD from the structural model to
the non-structural model. Indeed, the likelihood ratio has increased from 18.24
for the non-structural model (model 4) to 21.23 for the hybrid model (the corresponding values of corrected pseudo-R2 are 61.37 per cent and 71.43 per cent).
Table 2
Analysis of the maximum-likelihood estimators
Parameters
Model 1
Model 2
Model 3
Model 4
Model 5
Constant
-4,2571
(0,0245)
0,6808
(0,2001)
-7,91
(0,3719)
0,7242
(0,4283)
-3,8512
(0,2486)
PD (2 years)
0,1506
(0,0226)
0,2934
(0,3252)
Profitability
-0,0405
(0,1079)
-0,0229
(0,4589)
Turnover
-0,0161
(0,0518)
-0,0213
(0,1988)
0,1571
(0,1886)
-0,0069
(0,4615)
-0,0164
(0,3557)
Equity/Total assets
-0,0394
(0,0659)
-0,00709
(0,8033)
Debt/Equity
0,0029
(0,8891)
0,0055
(0,8328)
Number of observations
23
23
23
23
23
Number of Default
8
8
8
8
8
0,5256
0,5934
0,8277
0,6137
0,7143
Likelihood ratio
15,6209
<0,0001
17,6367
0.0001
24,6009
<0,0001
18,2408
0.0003
21,2300
0.0002
Log likelihood
-7,0496
-6,0416
-2,5596
-5,7396
-4,2450
McFadden’s R squared
*Into parenthesis are the p-value of estimated parameters
4.3.2 Various tests
In Figure 4, we reproduce the mean of the probabilities of default for companies that did default of the five models estimated so far. The mean of the probabilities of default for the model containing only the structural information is
71.41 per cent. This probability is maximized at 89.18 per cent for the hybrid
model with two accounting variables (turnover and profitability ratio) (model
3). The same model but without the probabilities of default from the structural
20
approach, comes in at 75.83 per cent. This confirms the results from the previous section.
Figure 4
Probabilities of default of firms that did default
In Figure 5, we reproduce the mean of the probabilities of default for companies
that did not default of the five models estimated. The mean of the probabilities
of default for the model containing only the structural information is 15.73 per
cent. This probability is minimized at 5.56 per cent for the hybrid model with
two accounting variables (turnover and profitability ratio) (model 3). The same
model but without the probabilities of default from the structural approach, has
a mean of 15.3 per cent. This also confirms the results from the previous section.
Figure 5
Probabilities of default of firms that did not default
To better visualize the performance of the hybrid models, the following figures
illustrate a comparison between the predictions of the probabilities of default
of two of the five models used (green curves) and the actual situation of firms
(red curves). This graphs clearly show that the hybrid model dominates the oth-
21
ers models. We again observed that the hybrid models are the best estimates of
probability curves of defects.
Figure 6
Accuracy of score models (Model 4)
Figure 7
Accuracy of hybrid models (models 3)
22
5. Conclusion
Credit risk measurement is an area of great and renewed interest for both academicians and practitioners. In this paper, our goal was to study a major component of credit risk, the probability of default using the methodology proposed
by Tudela and Young (2003) to a sample of French companies whose shares are
traded on the Stock Exchange Paris. This model has investigated the ability of
hybrid models to calculate the default risk of UK companies by verifying whether combining the structural and the non-structural model into a hybrid model
yields a better measure of the default risk than those obtained from structural
and traditional non-structural models estimated separately.
To accomplish this, we have aimed to explain defaults deficiencies by estimating
a probit model in which the explanatory variables are the estimated probabilities
of default from the structural model, financial ratios and other accounting data.
The dependent variable is binary taking the value of 1 if the default occurs and
0 otherwise. Using the same methodology, we have also estimated a model with
only accounting data as explanatory variables (non-structural model) and a third
probit model in which the only exogenous variable is the probability of default
from the structural model (the model that contains only structural information).
Thus, we have examined the predictive power of the probabilities of default
from the structural model to explain corporate bankruptcy by integrating it in
the non-structural model as an explanatory variable.
Our results indicate that the predicted probabilities of default (PDs) contribute
significantly to explaining default probabilities when they are included alongside the retained accounting variables. This confirms the results of the study of
Tudela and Young (2003) and those of Dionne and al (2005). We note that the main limitation of our work was to fixing the default barrier at
any level (equal to 1 in our study). Thus, it would be interesting in future work
to make endogenous default barrier and to estimate it by the maximum likelihood method may increase the predictive ability model.
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Does co-integration and causal relationship exist
between the non-stationary variables for chinese bank’s
profitability? Empirical evidence
Omar Masood*, University of East London
Marc Bradford†, ISC Paris
Jean-Jacques Levy‡, ISC Paris
Abstract
This study aims to give the analysis of the determinants of banks’
profitability in the Kingdom of China over the period starting
2003. The paper investigates the co-integration and causal relationship between total assets (TA) and total equity (TE) of Saudi
banks. The analysis employs Augmented Dickey Fuller (ADF)
test, Johansen’s cointegration test, Granger causality test. Analyzing the cointegration and other tests on Saudi Arabian banking sector over the study period, the relationships between the
two variables are examined. The empirical results have found
strong evidence that the variables are co-integrated.
Keywords: Banking, bank profitability, total assets, total equity,
co-integration.
JEL Classification: E50, F30, F17, F65, G01, G21
1. Introduction
In the last two decades economists have developed a number of tools to examine whether economic variables trend together in ways predicted by theory,
most notably cointegration tests. Cointegration methods have been very popular tools in applied economic work since their Introduction about twenty years
ago. However, the strict unit-root assumption that these methods typically rely
upon is often not easy to justify on economic or theoretical grounds. The multivariate testing procedure of Johansen (1988, 1991) has become a popular method
of testing for cointegration of the I(1)/I(0) variety, where I(1) and I(0) stand for
integration of orders one and zero, respectively. In the Johansen methodology,
series are pre-tested for unit roots; series that appear to have unit roots are put
into a vector auto regression from which one can test for the existence of one or
more I(0) linear combinations.
Utilizing the cointegration and error correction models on all Chinese’s banks
over the study period, various potential internal and external determinants are
examined to identify the most important determinants of profitability. Cointe*
Omar Masood is at University of East London, Docklands Campus, University Way, E16 2RD, UK
†
Marc Bradford is at ISC Paris, 22, Boulevard du Fort de Vaux, 75017 Paris, France
‡
Jean-Jacques Levy is at ISC Paris, 22, Boulevard du Fort de Vaux, 75017 Paris, France (Corresponding author),
E-mail: [email protected]
26
gration methodology has been extensively used as a convenient way of testing
for the weak-form of asset market efficiency, which states that no asset price
should be forecastable from the prices of other assets The Johansen (1988) method of testing for the existence of co-integrating relationships has become standard in the econometrics literature.
Since unit-root tests have very limited power to distinguish between a unit-root
and a close alternative, the pure unit-root assumption is typically based on convenience rather than on strong theoretical or empirical facts. This has led many
economists and econometricians to believe near-integrated processes. Near-integrated and integrated time series have implications for estimation and inference
that are similar in many respects. Cointegration, however, simply requires that
cointegrating linear combinations have lower orders of integration than their
parent series Granger (1986). Granger and Joyeux (1980) and Hosking (1981),
where continuous orders of integration from the real line are considered, the
case where there exists an I(d − b) linear combination of two or more I(d) series
has become known as fractional cointegration.
The cointegration approach is one of the recent methodologies employed to
identify the determinants of profitability in banking. It enables the estimation of
a relationship among non-stationary variables by revealing the long-run equilibrium relationship among the variables. This paper will help to determine the
most important factors of profitability in Saudi Arabian banks, and is supposed
to help banks’ stakeholders especially the managers and regulatory authorities
to improve the sector soundness by boosting the impact of positive factors and
lessening the impact of the negative factors.
A good econometric practice to always include tests on the cointegrating vectors to establish whether relevant restrictions are rejected or not. If such restrictions are not tested, a non-zero cointegrating rank might mistakenly be taken as
evidence in favour of cointegration between variables. This is particularly relevant when there are strong prior opinions regarding which variables “have to”
be in the cointegrating relationship. Unit root tests are performed on unvaried
time series in order to test the order or integration. If individual time series are
found to be integrated of same order after the unit root tests, then these variables
may be cointegrated. Cointegration deals with relationships among the group of
variables where each has a unit root. Application of cointegration test in the estimation of money demand were analyzed by Johansen and Juselius (1990) and
Dickey, Thansen and Thornton (1991).
The purpose of this paper is to investigate the effect of deviations from the unitroot assumption on the determination of the cointegrating rank of the system
using Johansen’s (1988, 1991) maximum Eigen value and trace tests. The paper
will contribute towards the existing literature by interrogating the determinants
of profitability of Chinese bank’s using a co-integration approach. First we test
for the stationary roots using augmented Dickey-Fuller test, then the Johansen’s
unit root test and granger causality test are applied to these variables.
The paper is divided into five sections. Section 2 will describe about the previous
existing literatures, Section 3 describes an overview of Chinese banking system,
Section 4 will give a complete description about the methodologies of the vari-
27
ous tests performed in this paper, and Section 5 contains the empirical results,
finally section 6 concludes with a short summary.
2. Literature review
Despite an extensive literature on savings behavior, there are not many studies, which focused primarily on the factors that determine the level of deposits
made by various categories of depositors at the commercial banks. These studies, however, concentrated mainly on private and household savings and not on
the business and government sectors. Lambert and Hoselitz (1963) were among
the first researchers to compile the works of others on savings behavior. They
extended the works of researchers who studied the savings behavior of households in Sri Lanka, Hong Kong, Malaysia, India, Philippines. Snyder (1974) and
Browning and Lusardi (1996) also presented a similar study which reviewed
micro theories and econometric models.
Masood, Akhtan & Chaudhary (2009) studied the co-integration and causal relationship between Return on Equity and Return on Assets of Saudi Arabia, they
found that there are stable long run relationships between the two variables.
They also argued that unidirectional causality from ROE to ROA implies that
sustainable development strategies with higher levels of ROE may be feasible
and fast economic growth of Saudi Arabia may be achievable. Loayza et al.
(2000b) listed papers and publications of the saving research project of a particular country and gave general reference in this area. Thereafter, lots of work
has been done on this area Cárdenas and Escobar (1998), Rosenzweig (2001), Kiiza and Pedreson (2001), Athukorala and Kunal Sen (2003), Dadzie et al. (2003),
Ozcan, et al. (2003), Athukorala and Tsai (2003), Qin (2003) and Hondroyiannis
(2004) have studied the savings behavior of a particular country. A large empirical literature was developed on the cross country comparison, which was
contributed by Doshi (1994), Masson et al. (1998), Loayza et al. (2000a), Agrawal
(2001), Anoruo (2001), Sarantis and Stewart (2001), Cohn and Kolluri (2003),
Ruza and Montero (2003).
The first works on cointegration methods were first studied by, Wallace and
Warner (1993), Malley and Moutos (1996) that cointegration-based tests of foreign exchange market efficiency, Cardoso (1998), Bremnes et al. (2001), Jonsson
(2001), Khamis and Leone (2001) and Bagchi et al. (2004). Studies arguing the
stationary of these variables include Song and Wu (1997, 1998), Taylor and Sarno
(1998), Wu and Chen (2001) and Basher and Westerlund (2006). Sephton and
Larsen (1991) showed that inference based on Johansen cointegration tests of
foreign exchange market efficiency suffers from structural instability. Cheung
and Lai (1993) argued that significant finite-sample bias in the performance of
the Johansen test statistics when asymptotic critical values are used for inference
in finite samples.
As unit-root tests have very limited power to distinguish between a unit-root
and a close alternative, the pure unit-root assumption is typically based on convenience rather than on strong theoretical or empirical facts Stock (1991), Cavanagh et al., (1995) and Elliott (1998) argued that near-integrated processes, which
explicitly allow for a small deviation from the pure unit-root assumption, to be
28
a more appropriate way to describe many economic time series. Phillips (1988)
concluded that spurious regressions are a problem when variables are nearintegrated as well as integrated and presented an analytical discussion Elliott
(1998) shows that large size distortions can occur when performing inference on
the cointegration vector in a system where the individual variables follow nearunit-root processes rather than pure unit-root processes.
The banks profitability is generally classified into two broad categories i.e. internal and external. The internals factors are in the control and framework of the
bank for instance number or employees, investments etc whereas the external
factors are out of control and framework of the bank for instance, market share,
competition, inflation etc.
Lots of literature has already been developed interrogates the profitability of
banks of the particular country in question. Hester and Zoellner (1966) argued
that the balance sheet structure has a significant impact on profitability. Smirlock
(1985) found a significant positive relationship between demand deposits and
profits. Lambert and Hoselitz (1963) were among the first researchers to compile
the works of others on savings behavior. Heggested (1977) interrogated the profitability of commercial banks and reports that time and savings deposits have
negative impact on profitability. Steiner and Huveneers (1994) found similar association while studying overhead expenditure. Bourke (1989), and Molyneux
and Thorton (1992) found that capital and staff expenses are positively related
to bank’s profitability.
Mullineaux (1978) found a positive impact for bank’s size on profitability. Studies of Pelzman (1968), Vernon (1971), Emery (1971), Mullineaux (1978) and Smirlock (1985) concluded that regulation have a significant impact on banks’ profitability. Emery (1971) examined the effect of competition on banks’ profitability
and find insignificant association between the two variables. Smirlock (1985)
further examined the effect of concentration on profitability and the findings
of these studies were mixed and inconclusive. Demirgüç-Kunt and Huizinga
(1998) concluded that that the well-capitalized banks have higher net interest
margins and are more profitable.
Keynes (1936), despite arguing the quantitative importance of the interest rate
effect, believes that in the long run substantial changes in the rate of interest
could modify social habits considerably, including the subjective propensity to
save. The importance of the rate of interest on consumption, many researchers using various methodologies tried to establish the strength of relationship
between these two elements. Wright (1967), Taylor (1971), Darby (1972), Heien
(1972), Juster and Watchel (1972), Blinder (1975), and Juster and Taylor (1975) in
their studies found an inverse relationship between interest rate and consumption. Modigliani (1977) based on his works and after seeing evidence on the effect of interest rate on consumption concludes that the rate of interest effects on
demand, including the consumption component, are pervasive and substantial.
Alrashdan (2002) found that the return on asset (ROA) is positively related to liquidity and total assets while ROA is negatively related to financial leverage and
cost of interest. Naceur (2003) examined the determinants of Tunisian banks’
profitability over the period 1980-2000, and found that the capital ratio, loans
29
and stock market development have positive impact on profitability while the
bank’s size has a negative impact. Hassan and Bashir (2003) stressed on the fact
that on the importance of customer and short-term funding, non-interest earning assets, and overhead in promoting profits. They also argued that profitability measures respond positively to increases in capital ratio and negatively to
loan ratios.
Haron and Azmi (2004) also investigated the determinants of Islamic Banks
across various countries using time series techniques of cointegration and errorcorrection mechanism (ECM). The study concludes that liquidity, deposit, asset
structure, total expenditures, consumer price index and money supply to have
significant impact on profitability while capital structure, market share and bank
size to have no impact. Alkassim (2005) examined the determinants of profitability in the banking sector of the GCC countries and found that asset have a negative impact on profitability of conventional banks but have a positive impact on
profitability of Islamic banks. They also observed that positive impact on profitability for conventional but have a negative impact for Islamic banking. Liu and
Hung (2006) examined the relationship between service quality and long-term
profitability of Taiwan’s banks and found a positive link between branch number and long-term profitability and also proved that average salaries are detrimental to banks’ profit.
3. An overview of chinese banking system
More than 85% of financial resources in China are allocated through the banking
system hence the economic reforms of China are dominated by Chinese commercial banks. The measurement of profitability and competitive conditions of
banking system depicts the economic growth of China. Since 1978, the Chinese
economy has experienced an impressive annual growth rate of about 10 percent.
China’s financial assets have grown at the annual rate of about 18 percent, or
more than twice the growth rate of GDP.
Since the People’s Republic of China was founded in 1949, the People’s Bank
of China (PBOC) has been the only bank in Mainland China until 1978. During
this period, the PBOC played a dual role in China’s financial system: as a central
bank and as a commercial bank. Upon nationalization of private banks in the
1949, bank management in China essentially followed the approach of centrallyplanned economy.
During the period of 1953-1978, four of the Chinese banks had operated intermittently as separate units as four Chinese banks had operated intermittently as
separate units or as a single entity. During the period 1978-1984, People’s Bank of
China (PBC), Agriculture Bank of China (ABC), Bank of China (BOC), and Construction Bank of China (CBC). During the period 1978-1984, the PBC retained
the central banking activities, while four other banks – ABC, BOC, CBC, and
ICBC, were carved out from the PBC to provide specialized services. The modern Chinese banking system comprises or four state-owned commercial banks,
as well as several joint-stock commercial banks, city commercial banks, rural
credit cooperatives, finance companies, and trust and investment companies,
foreign banks have been allowed to be an integral part of the banking system.
30
As the economy started shifting toward market-orientation, profitability and liquidity – if any – of state-owned enterprises suffered due to their inability to
adapt to the challenges imposed. State-owned banks have been hampered by
non-paying or delinquent loans to other state-owned enterprises. Both the stateowned and private banks had to extend credit to the inefficient, monopolist,
state-owned enterprises or public projects.
In 1994, three policy banks - China Development Bank (CDB), Agricultural Development Bank of China (ADBC), and the Export-Import Bank of China (China
Eximbank) - were established. They undertook most part of policy loans business
from the four national specialty banks. Meanwhile, the four national specialty
banks became state-owned commercial banks. In 2003, China Banking Regulatory Commission (CBRC) was established to take over most supervisory function
of PBOC, becoming a main regulator of China’s banking industry. By the end of
2007, there were 3 policy banks, 5 large state-owned commercial banks, 12 jointstock commercial banks, 124 city commercial banks, and 29 locally incorporated
foreign bank subsidiaries as well as other banking institutions.
Chinese authorities have adopted more flexible approach in seeking help from
foreign banks in rescuing weak banks. Foreign banks, when they are well-capitalized and have an access to external markets, are less likely to turn off the credit when monetary authorities pursue tight monetary policies. Foreign banks’
presence also provided needed competition to domestic banks unburdened of
non-performing loans. Competitive pressure in turn provided incentives to local
banks to improve management practices.
According to Commercial Banking Law of the People’s Republic of China which
began to be in effect on July 1st of 1995, one of the prerequisites to establish
commercial banks is “Having directors and senior management personnel with
professional knowledge for holding the post and work experiences”. In June
2002, the People’s Bank of China promulgated Guidance on Independent Directors and External Supervisors of Joint-Stock Commercial Banks, which aims to
establish and enhance the arrangement of independent directors.
For the Chinese banking, most crucial issues is to convert the four state-owned
commercial banks, with 70 percent of the nation’s financial assets and loans,
into share-holding companies. During system-wide crisis, state-owned banks
often become safe havens because the public perceives that their funds will be
fully guaranteed by the state. Chinese banks have suffered from bad loans and
high operating costs for a long time. A sound banking industry is essential for
development of efficient financial markets, in turn, efficient markets are crucial
for ensuring effectiveness of the market-oriented economy. Excessive bad debts
reflecting inefficient bank management are thus antithetical to the goal of market orientation for the Chinese economy.
They were underperforming their counterparts in other countries. In recent
years, Chinese government has carried out a series of reforms aiming at making
banks more market driven, more profitable, and well managed. One important
reform among these is to establish a board of directors system in existing banks
to improve corporate governance. In this context, how effective the role of board
of directors have played in the profitability of Chinese banks is up to close examination.
31
4. Methodology
The estimation of the long run relationship between the variables, time series
properties of the individual variables are examined by conducting Augmented
Dickey Fuller (ADF) stationary tests, then the short run dynamic and long run
co-integration relationship are investigated by using the multivariate Johansen’s
co-integration test and Granger Causality test.
4.1 Unit root tests
The Augmented Dickey-Fuller (ADF) unit root test method put forward by
American scholars Dickey and Fuller is widely used in the academia to examine the stationarity of the time series and determine the integration order of
non-stationary time series. Unit root tests are first conducted to establish the
stationary properties of the time series data sets. Stationary entails long run
mean reversion and determining a series stationary property avoids spurious
regression relations. It occurs when series having unit roots are regressed into
one another.
The presence of non-stationary variables might lead to spurious regressions and
nonobjective policy implications. Augmented Dickey Fuller (ADF) tests are used
for this purpose in conjunction with the critical values, which allows for calculation of critical values for any number of regressors and sample size. The ADF
model used is describes as follows:
(1)
Here Y: variable used for unit root test, α is the constant, T represents the trend,
ω = p-1 and ε is the white noise series. The null hypothesis is HO: ω =0. If the ADF
value of the lnY is bigger then the McKinnon value at 5% significant level, the
null hypothesis is accepted, which means lnY has unit root and is non-stationary.
If it is less then the McKinnon value then the H0 is rejected and lnY is stationary.
As for the non-stationary series, we should test the stationary of its 1st difference. If the 1st difference is stationary, the series has unit root and it is first order
integration I (1).
4.2 Johansen’s co-integration test
According to the co-integration theory, there may be co-integration relationship
between the variables involved if they are 1 order integration series, i.e. their 1st
difference is stationary. There are two methods to examine this cointegration relationship, one is EG two-step procedure, put forward by Engle and Granger in
1987, the other is Johansen cointegration test (Johansen(1988) and Juselius1990)
based on Vector Auto Regression (VAR).
32
For co-integration test, we will conduct the Johansen’s multivariate co-integration tests. The Johansen’s multivariate co-integration test involved testing
the relationships between the variables following vector auto-regression (VAR)
model:
(2)
Yt represents n*1 vector of I (1) variables. Γ and Π are n*n matrix of coefficients
to be tested. B denoted n*h matrix and Xt denoted h*1 vector of I(0) variables.
Π denoted the rank of the matrix and interrogates the long-run relationships in
the variable and is equal to the number of independent co-integrating vectors. If
rank of Π is 0, the variables in are not co-integrated.
Johansen developed two test statistics: the trace test and the maximum eigen
value test. λtrace statistic tests the null hypothesis that r= 0 (no co-integration)
against a general alternative hypothesis of r>0 (co-integration). The Kmax statistic
tests the null hypothesis that the number of co-integrating vectors is r against
the specific alternative of r+1 co-integrating vectors. The test statistics obtained
from λtrace and Kmax tests are compared against the asymptotic critical values of
the two test statistics by Johansen and Juselius.
4.3 Granger Causality test
The pair wise Granger causality tests are used to examine whether the past value
of a series Xt , will help to predict the value of another series at present Yt taking
into account the past value of the previous value of Yt. The two series are first
tested for stationary using the ADF unit root test, followed by the Johansen co
integration test before performing the Granger causality test. If the time series of
a variable is stationary or I(0) from the ADF test, or if the time series are found to
be I(1) and co integrated. The Granger causality test is as follows:
(3)
(4)
33
Where Xt is the log of the first variable at time t, and Yt is the log of the second
variable at time t. µx,t and µy,t are the white noise error terms at time t. αx,i is the parameter of the past value of X, which tells us how much past value of X explains
the current value of X and βx,i the parameter of the past value of Y, which tells
us how much past value of Y explains the current value of X. Similar meanings
apply to αy,i and βy,i .
4.4 Data
The data used in this paper was collected from 16 most significant banks of China, which includes banks like Agriculture Bank of China, Agricultural Development bank of China, China Development Bank, China Merchant bank, Bank of
Communication, Indrustrial and Commercial bank of China, China Everbright
bank, China Construction Bank, Bank of China, Hua Xia Bank, Export-Import
bank of China, Shen Zhen Ping An, Shen Zhen Development Bank, Xia Men International bank, Min Sheng, Shangai Pudong Development bank. The dataset
was developed by collecting the information from these banks.
The pooled data was made by combining the datasets from all the banks, and
the regression analysis was performed on this pooled data to obtain the results
which are mentioned in the next section. The mean square and double accounting techniques were also used on the dataset, wherever required.
5. Empirical analysis
5.1 Unit root test
We test for the presence of unit roots and identify the order of integration for
each variable using the Augmented Dickey–Fuller (ADF). The null hypothesis
is considered as non-stationary. The test on the variable total assets gave the following result.
The computed ADF test-statistic (1.331162) is greater than the critical values
(-8.033476, -4.541245, -3.380555 at 1%, 5% and 10% significant level, respectively), thus we can conclude that the variable total assets has a unit root i.e. it is a
non-stationary series.
Table 1
ADF test statistics Null Hypothesis: total assets has a unit root
Exogenous: Constant, Linear Trend. Lag Length: 1 (Fixed)
Augmented Dickey Fuller Test Statistic
Test critical values
1% level
5% level
10% level
t-statistic
Prob*
1.331162
0.9773
-8.033476
-4.541245
-3.380555
* MacKinnon (1996) or Augmented Dickey-Fuller Test Equation Dependent Variable: D(total assets).
Method: Least Squares
34
Augmented Dickey fuller test equation
Dependent variable - D(total assets)
Method – Least Squares
Variable
Total assets
Coefficient
0.523410
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid.
Log likelihood
F-statistic
Prob (F-statistic)
Standard error
0.393198
0.639249
0.278497
52281516
2.73E + 15
-55.92536
1.771993
0.410164
T-statistic
Prob.
1.331162
0.4102
Mean dependent variable
S.D. dependent variable
Akaike info Criterion
Schwarz Criterion
Hannan Quinn Criterion
Dublin Watson stat
1.29E + 08
615510168
38.61691
38. 01598
37.40897
2.997813
In order to eliminate the heteroskedasticity of total assets and total equity as, we
take their natural logarithm and define them as LnTA and LnTE. Similarly, ADF
tests were conducted on total equity and the logged variables of total assets and
total equity differentiated by their order of integration are reported in Table 2.
Table 2
Results of ADF unit root test
Variable
ADF-statistic Critical value
(5%)
Total assets
Total equity
LnTA
LnTE
1.331162
-0.831217
0.646288
0.599704
AIC
SC
Result
-4.541245
38.61691
38. 01598
non- stationary
-4.542245
32.61347
32.01255
non- stationary
3.850555 -2.032422-2.633347 stationary
3.380555 9.1059548.504869 stationary
The lag is added to make the residual be white noise, AIC is Akaike Info.Criterion and SC is the Schwarz Criterion.
As shown in Table 3, for the variables of total assets and total equity, the results
shows that it is evident that we found the presence of a unit root at conventional levels of statistical significance for the variables of total assets and total
equity. To see whether they are integrated of order one I(1) at the 1% level, we
performed augmented Dickey–Fuller tests on their first difference. The results
of the unit root test show that the first differences of both series are stationary
which are found to reject the null hypothesis of unit root. Therefore we can conclude that all series involved in the estimation procedure are regarded as I(1),
and it is suitable to make co integration test.
35
5.2 Johansen’s Cointegration test
As proved by previous test the variables under analysis are integrated of order
1 (namely I(1)), hence now the co-integration test is performed. The proper way
to test for the relationship between total assets and total equity is certainly to
test for a co-integrating equation. In testing co-integration relationships, we use
the Johansen and Juselius method of testing. For selecting optimal lag length for
the co-integration test, we adopt the Schwartz Information Criterion (SIC) and
Schwarz criterion (SC) Criterion. The co-integration tests results performed on
the variables gave the following result.
Table 3
Cointegration rank test Series: total_assets-total_equity
Lags interval (in first differences): 1 to 1
Unrestricted Cointegration Rank Test (Trace)
Hypothesized No. of
CE(s)
None**
At most 1**
Eigen
values
0.874563
0.598298
Trace
statistic
44.57638
8.746002
Trace test indicates 2 cointegrating eqn(s) at the 0.05 level
** denotes rejection of the hypothesis at the 0.05 level
* MacKinnon-Haug-Michelis (1999) p-values
0.05
critical value
12.64738
4.983491
Prob*
0.0010
0.0194
Unrestricted Cointegration Rank Test (Maximum Eigenvalue)
Hypothesized No. of
CE(s)
None**
At most 1**
Eigen
values
0.874563
0.598298
Max
Eigenstatistic
38.98723
8.746002
Trace test indicates 2 cointegrating eqn(s) at the 0.05 level
** denotes rejection of the hypothesis at the 0.05 level
* MacKinnon-Haug-Michelis (1999) p-values
0.05
critical value
11.56473
4.983491
Prob*
0.0010
0.0194
Therefore, by applying Johansen test on total assets and total equity series we
found the presence of two cointegration vectors. Therefore, by applying Johansen decision rule, we conclude that there are two co-integration vectors for the
model. Hence our findings imply that there are stable long run relationships
between the two variables i.e. total assets and total equity. The results for the
Johansen’s test are concluded in Table 4.
Table 4
Results of Johansen’s cointegration test
Eigen-value
t-statistic
0.874563
0.598298
44.57638
8.746002
Critical value
(0.05)
12.64738
4.983491
Trace test indicates 2 co-integrating eqn(s) at the 0.05 level.
Prob.
0.0010
0.0194
Null-hypothesis
r =0
r ≤1
36
5.3 Granger Causality Test
Granger causality test demands that the economic variables should be stationary series. So we need to examine the stationarity of the 1st difference. Hence we
test variables LnTA and LnTE so as to observe the causality between total assets
and total equity. As the sample of observation for this test is small, we take the
lag to be 1. The results of Granger Causality test are shown in table 5.
Table 5
Pairwise granger Causality test
Null Hypothesis
F-statistic
3.91826
0.1632
LN_TE does not granger cause LN_TA
6.02545
0.0984
LN_TA does not granger cause LN_TE
Prob
Hence by applying the granger causality test to the variables can interpret that
total assets is a granger cause to total equity and total equity is also a granger
cause to total assets . In other words total assets can affect total equity input, similarly total equity can also affect the total assets in the Chinese Banking sector.
Therefore, there exist a bi-direction cause-effect relationship between total assets
and total equity. The results of the Granger Causality are concluded in table 6.
Table 6
Results of Granger Causality Test
Lag
1
Ho
LnTA
F-value
P-value
0.1632
Accept Ho
1
LnTE
6.02545
0.0984
Accept Ho
5.4 Graphical Comparison
3.91826
Result
37
To further illustrate the relationship between total assets and total equity in the
Chinese Banking sector, we also conducted a graphical comparison of the two
variables over a four year period. The above figure depicts that both the variables show similar kind of trend till 2006. After 2006, the total assets observed a
positive growth while total equity experienced a small decerement.
6. Conclusion
In testing the co-integration and causal relationship between total assets and
total equity, the time series model of ADF unit-root test, Johansen co-integration
test, Granger causality test and graphical comparison model are employed. The
empirical results have found strong evidence that the variables are co-integrated
and feedback.
By applying Johansen decision rule, we found that there are two co-integration
vectors for the model. Hence our findings imply that there are stable long run
relationships between the two variables i.e. total assets and total equity. Furthermore after the granger causality test to the variables we found that there
exist a bi-directional cause-effect relationship between total assets and total
equity in the Chinese banking sector. By applying the granger causality test to
the variables we found that total assets are a granger cause to total equity and
total equity is also a granger cause to total assets. The evidences of long-run
bi-directional causality from total assets to total equity implies that sustainable development may be feasible and fast economic growth of China may
be achievable. Furthermore by graphical comparison we found that both the
variables were observed having similar kinds of trends over the period of last
four years.
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Dependence structure between the equity market and the
foreign exchange market after the greek crisis: evidence
from france and germany
Adel Boubaker*, University of Tunis el Manar, Tunis
Jaghoubbi Salma†, University of Tunis el Manar, Tunis
Abstract
The main objective of this study is to investigate the dependence structure and to test the degree of the dependence between
financial returns using two measures of dependence: correlations and copula functions. Five candidates, the Gaussian, the
Student’s t, the Frank, the Clayton and the Gumbel copulas, are
compared. Our empirical results imply that both of the degree of
the dependence and the dependence structure between each pair
of stock-FX returns vary when the financial Greek crisis occurs.
Our findings have important implications for global investment
risk management by taking into account joint tail risk.
Keywords: Greek financial crisis, Stock return, Foreign exchange
rate, Rank correlation, copula approach.
JEL Classification: G01, G12, F31, C1
1. Introduction
Understanding the dependence structure across international financial markets
remains a crucial issue for risk management and portfolio management. Many
researchers have focused on the co-movement among worldwide financial markets and have investigated the relationship of world exchange rates during a
worldwide financial crisis. Besides, some literature studies the co-movements
across markets of different asset types, such as the stock market and foreign
exchange rates.
As to the relation between equity prices and exchange rates, economic theory
insists two different approaches, namely, the ‘flow-oriented’ approach and the
‘stock-oriented’ approach. First, the flow-oriented approaches insist that FX rate
changes affect international competitiveness and trade balance. Hence local currency depreciation works to strengthen their competitiveness of domestic companies as their exports will be cheaper in international trade. As a result, the
flow-oriented approaches claim appositive linkage between equity prices and
the FX rate. Under the stock-oriented approaches, the portfolio balance models are often considered. (Frankel 1983, Branson and Henderson 1985, for example) These approaches consider an internationally diversified portfolio, and
these models suggest that the FX rate dynamics function to balance the demand
*
Adel Boubaker is at the University of Tunis el Manar, Tunis, B.P 248 El Manar II 2092 Tunisia
†
Jaghoubbi Salma is at the University of Tunis el Manar, Tunis, B.P 248 El Manar II 2092 Tunisia, (Corresponding
author), E-mail: [email protected]
42
and supply of domestic and foreign financial assets. Thus in these approaches,
an increase in domestic equity prices will lead an appreciation of the domestic
currency since investors’ demand for domestic currency increases in order to
purchase domestic equities. Therefore, these approaches suggest a negative relationship between FX rates and equity prices.
The empirical literature provides conflicting findings regarding the dynamic
linkage between Foreign Exchange market and stock prices. In this context, early
studies including Jorion (1990, 1991) suggest that foreign exchange rate changes
offer no predictive power for stock returns volatility, although others such as
Dumas and Solnik (1995) and Roll (1992) claim the existence of a strong linkage
between Foreign Exchange rate changes and stock market volatility. Moreover,
Yang and Doong (2004) investigate the asymmetries in the volatility transmission mechanism between stock prices and exchange rates for the G7 countries
over the period 1979–1999. Using a multivariate EGARCH model, they showed
that exchange rate changes have a direct impact on future changes of stock prices. Phylaktis and Ravazzolo (2005) study the long-run and short-run dynamics
between stock prices and exchange rates using cointegration approach and multivariate Granger causality tests for some pacific basin countries. Their results
indicate that stock prices and FX markets are positively linked. Recently, Aloui
(2007) explores the nature of mean, volatility and causality transmission mechanisms between stock and FX markets for the United States and some major European markets for the period pre- and post-euro, using a multivariate EGARCH
model. Results found show that movements of stock prices affect exchange rate
dynamics for the two periods pre- and post-euro. However, stock markets are
less influenced by exchange rates movements for the two periods. In a more
recent paper, Zhao (2010) analyzes the dynamic relationship between the real
effective exchange rate and the Chinese stock price, using a VAR with a multivariate GARCH model. The results show that there is no stable long run equilibrium relationship between the two financial markets. Furthermore, the paper
reveals that bidirectional causality exists between volatility on the two markets.
Using a copula based approach; Ning (2010) investigates the dependence structure between the equity market and the foreign exchange market for the period
pre- and post-euro for the G5 countries (US, UK, Germany, Japan, and France).
Ning (2010) finds significant and positive tail dependence between the foreign
exchange market movements and the stock market in each country for the two
sub-periods. Using Latin America countries, Diamandis and Drakos (2011) examine dynamic linkages between exchange rates and stock prices. Their empirical results show that there is a significant long run relationship between the
local stock market and the foreign exchange market but that the stability of the
relationship is affected by financial and currency crises such as the Mexican currency crisis of 1994 and the subprime crisis.
In this paper, the nature of the relationship and the dependence structure between exchange rates and stock prices is investigated for two developed European countries namely France and Germany, and also for Greece, the native
country of the crisis, during the period 2007-2011. These two states, France
and Germany, represent almost one quarter of the territory of the European
Union and third of the population and almost half of Gross Domestic Product of 27 countries. Both countries provide 36% of the EU budget, and in terms
43
of decision making, France and Germany have 31% of votes in the European Council. Besides, these two States represent the founding members of the
European Economic and Monetary Union which is created in 1999. On January
1, 2001, Greece became the twelfth member of the euro area. This Eurozone now
consists of seventeen countries with heterogeneous economic structures and sizes. The common monetary policy cannot be adapted to all. Therefore, real differences occur. In addition, the slowdown after 2001, as the financial crisis of 20072008, has not affected European countries in the same way. The Greek financial crisis has also highlighted the fragility of the functioning of the euro area. This crisis has revealed inconsistencies and macroeconomic policies that characterize the area, so an exit scenario for the euro area of one
or several countries is no longer regarded as fanciful.
Failure Greek may trigger a panic both on other highly indebted European countries like Italy, Ireland and Portugal, and also for the developed countries of the
area, triggering further deterioration of the agencies. This context has revived
current events to the traditional questions about the nature and the degree of the
dependence across markets of different asset types, such as the stock market and
foreign exchange rates.
The purpose of this paper is to investigate the dependence structure and to test
the degree of the dependence between financial returns, after the occurrence of
the current financial Greek crisis. For that, we use two measures of dependence:
correlations and copula functions. More precisely, our main research questions
are: 1) what is the dependence structure between European stock market and
exchange rates? 2) Is the dependence symmetric or asymmetric? 3) Are the comovements of stock-currency markets affected by the happen of the Greek crisis?
This present study contributes to the related literature in that we provide a
framework for understanding the co-movements and describing the dependence structure between the financial markets, in the context of the recent financial Greek crisis. This is important since the knowledge of the nature of the
dependence structure is very important in describing the reality of the behavior
of financial markets. Indeed, it indicates the potential of simultaneous extreme
events in both the stock and foreign exchange market. This property of dependence structure is important to international investment decisions in foreign
stock markets.
The remainder of the paper is organized as follows. In Section 2, we introduce
the methodology used in the empirical application. In Section 3, we present the
data and discuss our empirical results. Finally, section 4 summarizes our results
and concludes.
2. Methodology
Our methodology is based, primarily, on the calculation of linear and rank correlation coefficients between the European market returns. We get series of correlation coefficients between these markets and we study their dynamics changes. Secondly, such as measurements based on linear correlation may lead to
44
misspecification of the dependence structure with its nonlinear portion, copula
approach is employed to provide the robust measures of dependences based
on the entire joint distributions of variables and also to estimate dependence
focuses on the entire structure rather than correlation.
2.1 Correlations:
Correlations are the most familiar measures of dependence in finance. If properly specified, correlations tell us about average diversification opportunities over
the entire distribution.
2.1.1 Traditionnel correlation :
The Pearson correlation coefficient is the covariance divided by the product of
the standard deviations:
(2.1)
Although most studies have focused on measuring the dependence between financial markets have used the Pearson correlation, this coefficient is only reliable when the random variables are jointly Gaussian. Therefore, we consider
two other measures of dependence: the Kendall’s tau and the Spearman’s Rho,
which are measures of concordance, generalize the linear correlation, taking
into account the joint distribution (and not just marginal) and are dependent
on copulas.
2.1.1 The rank correlation:
The rate of Kendall and Spearman’s rho are two measures of concordance well
known in statistics. They provide a measure of the correlation between the ranks
of the observations, unlike the linear correlation coefficient which assesses the
correlation between the values of observations. They also offer the advantage to
be simply expressed in terms of the copula associated with the couple of random
variables.
2.1.1.1 The Kendall correlation coefficient:
Let (X, Y) a couple of random vectors and (X’, Y’) (a copy of (X, Y) that is to say
a pair of vectors in all respects identical to (X, Y) the Kendall’s tau is then:
(2.2)
The Kendall’s tau is simply the difference between the probability of concordance and of discordance.
2.1.1.1 The Spearman correlation coefficient:
Let X and Y are two random variables of marginal distributions Fx and Fg.
The correlation coefficient Spearman rank coefficient
between Fx (X) and Fg (Y):
45
is the Pearson correlation
(2.3)
The correlation coefficients of Spearman and Kendall have the property
of being based on copula functions. Thus, if X and Y are two random variables with continuous marginal distributions and the copula C associated,
their expressions in terms of copula is:
(2.4)
Like the linear correlation coefficient, the rank correlation coefficients reflect the
extent to which high (respectively low) of a variable are associated with high
(respectively low) for the other variable. However, the rank correlation has the
advantage of being preserved under strictly increasing transformations.
2.2 A copula model for asymmetry dependence:
Copulas are multivariate distribution functions with standard uniform marginal
distributions. Am-dimensional copula is represented as follows:
C (u) = C (u, …, um )
(2.5)
Where u, …, um are standard uniform marginal distributions.In such a context,
copulas can be used to link margins into a multivariate distribution function.
The copula function extends the concept of multivariate distribution for random
variables which are defined over [0,1]. This is possible due to the Sklar (1959)
theorem which states that copulas may be constructed in conjunction with univariate distribution functions to build multivariate distribution functions.
Sklar’s Theorem: Let FXY be a joint distribution function with margins and. Then there exists a copula C such that for all x, y in R,
(2.6)
If FX and Fy are continuous, then C is unique; otherwise, C is uniquely determined on Ran FX×Ran Fy and C is invariant under strictly increasing transformations of the random variables.
Here we study five copulas with different dependence structure: the Gaussian
copula, the Student-t copula, the Frank copula, the Clayton and the Gumbel
copula. From them, the Gaussian copula is the most popular in finance and used
as the benchmark.
46
• The Gaussian copula:
The multivariate Gaussian copula applied to a joint distribution
function with correlation
matrix R, is
defined
by:
(2.7)
WhereCR is the distribution function of joint variables, these variables are normal, standardized and have a correlation matrix R.
Since the majority of models in finance use this dependence structure, managers must adapt their model by modifying the structure of dependence if
they consider the extreme risks. The use of copulas consistent with the extreme
value theory is a modeling technique that allows analysis of rare events without being rigid methods based on the extension to several dimensions of a univariate distribution. This copula has no tail dependence and does not correlate extreme values. Modeling the dependence structure with a Gaussian copula is consistent with the extent of this dependence by the linear correlation
coefficient.
• The Student- t copula:
The Student-t copula is defined by:
(2.8)
Where Tv,m,∑ is the multivariate student distribution function with a degree of
freedom v and variance-covariance matrix ∑.
• Archimedean copula:
The Gaussian and Student copulas are called elliptic. They apply to the pattern
of symmetric distributions. However, the Clayton, Gumbel and Frank copulas
are called Archimedean copulas. They have the great advantage of being able to
describe a variety of dependence structures including the asymmetric dependencies, where the coefficients of the lower tail and upper tail are different. We
present as follow the characteristics of the best known models. The variables u
and v are cumulative distribution functions. The parametermeasures the degree
of dependence between risks.
(2.9)
(2.10)
(2.11)
47
3. Data and results:
3.1 Descriptive statistics:
We use daily market data from three stock market indices, which are France
(CAC40), Germany (DAX) and Greek (CYSE) and the foreign exchange rates
for a sample period of February 1, 2007 to December 21, 2011. We choose this
period to investigate the impact of the 2009 Greek crisis on the relationship
between these European returns with the exchange rates. The total number of
observations is 1253 for the full sample. We briefly overview summary statistics,
then discuss the correlation and copula estimates.
Table 1
Descriptive statistics of daily stock prices and foreign exchange rates
Stock
and FX returns
Mean
S.D
Skewness
Kurtosis
Jarque-Bera
CAC40
-0.021238
0.779427
0.139538 7.969773
1289.906 [0.000]
DAX
-0.005244 0.746523
0.118501 8.201018
1414.069 [0.000]
CYSE
0.018518
0.974732
-0.021023
7.124780
885.2031 [0.000]
EURO/USD
9.05E-05 0.316340
-0.192636 6.300257
577.9667 [0.000]
The returns are in national currencies. The sample contains daily market returns from February 1, 2007
until December 21, 2011. The values in parenthesis are the probability values.
The descriptive statistics for daily returns shown in table 1 suggest that all returns show excess kurtosis implying the properties of asymmetry, leptokurtosis,
and tail dependence; hence, the normality assumption has been severely challenged. Furthermore, Jarque-Bera tests on log returns data indicate that the normality hypothesis cannot be accepted for these stocks.
3.2 Empirical results:
3.2.1. Correlation estimates of dependence:
Table 2 presents linear correlations, the Kendall’s tau and the Spearman’s rho
rank correlations between the stock and the exchange rate return pairs, before
the financial Greek crisis. We observe that the pair wise correlations are positive
for both France and Germany, indicating that the increase (decrease) of the local
stock market is associated with the appreciation (depreciation) of the exchange
rate EURO/USD. However, it is not the same case for Athens which has a negative relationship with the FX market. The Kendal’s Taus for our pairs of stock
market returns and stock exchange rate are positive expect for Athens; showing
the probability of concordance is significantly higher than the probability of discordance. The Spearman’s Rhos for the pairs in each country are also positive,
with the exception for Athens. From these results, we can conclude that there
are strong rank correlations. The German pair has the strongest dependence,
followed by the French pair.
48
Table 2
Correlation measures (2007-2009)
Pairs
Pearson correlation
Kendall’s Tau
Spearman’s Rho
Pre-crisis
French pair
0.217*
0.120*
0.179*
German pair
0.241*
0.127*
0.191*
Greek pair
-0.038
-0.023
-0.029
This table gives different correlation measures for each stock-EUR/USD exchange rate daily return pair
over the period February 1, 2007 to October 15, 2009.
* denote significance level at the 1 %. Total observations are 691.
In table 3, we present these linear correlations and rank correlations measures for
each stock-exchange rate return pair after the current financial Greek crisis. The
linear correlation, Pearson coefficients, for our pairs of returns are all positive,
showing that, for these European markets, the increase (decrease) of the local
stock market is associated with the appreciating (depreciating) of the exchange
rate EURO/USD. The Kendall’s Taus for our pairs are all positive indicating that
the probability of concordance is higher than the probability of discordance. The
Spearman’s Rhos indicate strong rank correlations. The values of taus and Rhos
are consistent with each other and the linear correlation. The French market has
the strongest dependence with the EURO/USD exchange rate. Further, the correlation increase and became strong in the post-crisis period. Thus, the stockexchange rate returns become more dependent when financial extreme events
(Greek crisis) occurs.
Table 3
Correlation measures (2009-2011)
Pairs
Pearson correlation
Kendall’s Tau
Spearman’s Rho
Pre-crisis
French pair
0.399*
0.261*
0.389*
German pair
0.366*
0.241*
0.355*
Greek pair
0.356*
0.230*
0.345*
This table gives different correlation measures for each stock-euro/usd exchange rate daily return pair
over the period October 16, 2009 to December 21, 2011. Total observations are 562.
*Indicates statistical significance at the 1% level.
In order to see the dependence structure between European stock markets and
the exchange rate market, we present below the empirical copula.
49
Figure 1
The empirical copula for stock returns and exchange rate returns over the period
February 1, 2007 to December 21, 2011.
From the figure above, we can conclude that there exists symmetric upper and
lower tail dependence. Thus, the dependence between the stock returns and
exchange rate return is symmetric, implying both markets boom and crash together.
As follows, we will run the test of adequacy on copula Gaussian (Normal), Student, Clayton, Frank and Gumbel to select the best copula which is more appropriate to the dependence structure shown in the empirical copula.
Copula results:
To better assess the degree as well as the dependence structure, we will examine
the relationship between each pair of stock-FX returns separately, for the two
sub period.
Table 4.A bellow, reports parameters estimates of bivariate copulas for each pair,
before the occurrence of the financial Greek crisis.
We note that the parametermeasure the degree of dependence between returns
and DoF is the degree of freedom in the Student-t copula.
Table 4.A
Estimation of copula parameters for the pre-crisis period:
Parameters
Pairs
Copula
models
ρ
France
Student-t
0.187
German
Student-t
0.199
Athens
Student-t
0.210
DoF
Information criteria
q
SIC
AIC
HQIC
-32.08
-41.14
-37.65
6
-34.16
-43.22
-39.72
4
-47.92
-56.98
-53.48
5
50
As we see in the table above, the dependence between all pairs of financial returns used are described by the student-t copula. The dependence parameters
(the correlation coefficient ρ) are all positive in the pre-crisis period.
The correlation coefficient ρ from the Student-t copula is close to the usual
correlation coefficient. The DoF of the Student-t copulas are from 4 to 6,
indicating the presence of extreme co-movements and tail dependence. The tail
dependence parameter q for pre-crisis period is zero. Thus, we can conclude that
any pair has asymmetric tail dependence. The stock market returns have elliptical symmetric dependence structure (the case of the Student-t copulas) with the
foreign exchange rate.
In order to appreciate both, the dependence structure and the degree of this
dependence, after the Greek crisis; we estimate the copula parameters in the
post-crisis period.
Table 4.B bellow, reports parameters estimates of bivariate copulas for each pair
after the occurrence of the financial Greek crisis.
Table 4.B
Estimation of copula parameters for the post-crisis period:
Parameters
Information criteria
Pairs
Copula
models
ρ
DoF
q
SIC
AIC
HQIC
France
Gumbel
1.36
-85.73
-94.37
-91.01
German
Student-t
0.369
7
-73.82
-82.46
-79.10
Athens
Gaussian
0.359
-69.99
-74.32
-72.63
For all pairs, the dependence parameters; the correlation coefficient ρ in both
Gaussian and Student-t copulas, the degree of freedom DoF in the Student-t
copula and the asymmetric dependence parameter q in the Gumbel copula are
positive.
The German return has the highest correlation coefficient with ρ = 0.369. The
DoF of the Student-t copulas is 7 for German, indicating the presence of extreme co-movements and tail dependence. The tail dependence parameter q
for post crisis period is 1.36 for the French pair. Moreover, the dependence
structure between each stock index returns and exchange rate returns is largely changed from a symmetric structure with symmetric tail dependence to an
asymmetric structure with non-zero and asymmetric upper tail dependence.
From our results, we find The Gumbel copula which is limited to the description of a positive dependence structure. Thus, it allows only positive dependence structures or upper tail dependence, for which the parameter belongs to
the interval [1,+∞). Consequently, the degree of the dependence varies when
the financial Greek crisis occurs. Indeed, as we see in tables 4(A; B) above, it
increased after the crisis.
51
Despites France and Germany are two developed countries belonging
to the Economic and Monetary Union, their dependence after the Greek crisis
was different. The dependence of the French pair becomes asymmetric with tail
dependence. The significance of the tail dependence implies that the stock market
and exchange rate tend to experience concurrent extreme shocks. However, the
Germany pair remains symmetric which approves that German stock market
and currency market boom and crash together. Thus, this finding improves our
understanding of the market dependence.
4. Conclusion
This paper examines the dynamics relationship between foreign exchange rates,
Athens and the two developed stock markets namely German and French, after
the occurrence of the Greek crisis, using daily data from February 2007 to December 2011.
We employ five multivariate copulas; the multivariate normal, multivariate Student-t, multivariate Gumbel, multivariate Clayton and the multivariate Frank
to directly model the underlying dependence structure. We find that, during the
pre-crisis period, the stock-foreign exchange market returns have elliptical symmetric dependence structure. However, the degree of the dependence becomes
stronger when the financial Greek crisis occurs. Indeed, the dependence structure for the French pair becomes asymmetric with upper tail dependence after
the crisis. For both the German and Athens stock-FX, the dependence remains
symmetric however the degree of freedom DoF become higher.
Our findings may have important implications in the risk management. First,
symmetric dependence structure with zero tail dependence can specify different levels of correlation between the marginals; however, it must possess radial
symmetry which doesn’t allow to extreme values correlation. Thus, in this case,
the dependence has the linear correlation coefficient as measure of dependence.
Second, asymmetric dependence structure can have upper tail dependence,
lower tail dependence, or both; as such, they can better describe the reality of
the behavior of financial markets. Additionally, it indicates the potential of simultaneous extreme events in both the stock and foreign exchange market. This
property of dependence structure is important to international investors who
invest in foreign stock markets.
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Financial development and income inequality
in the mena region
Zied Saadaoui*, University of Sfax
Maher Gassab†, University of Manouba
Abstract
We study a sample of eleven Middle East and North Africa
(MENA) countries, observed between 1963 and 2002, to test the
impact of financial development on income inequality. Estimation results using system GMM, show that the development of
deposit and savings instruments that the financial systems of
MENA countries participate in reducing income inequality, by
stimulating the demand for real money balances. The results also
show that increasing the share of credits granted to government
is likely to complicate the access of poor households to credit. Finally, financial instability decreases, rather than increases, income inequality between rich and poor households.
Keywords: MENA countries, Income inequality, Financial development, Financial instability, System GMM.
JEL Classification: O15, O16
1. Introduction
Since the 1960’s, the majority of the Middle East and North Africa countries5
have adopted new policies to develop their financial sector. In the first stage
of development, governments were highly involved in these policies until mid
1980, with the liberalization of their financial systems, after the Washington consensus. But, while several studies focused on the causality between financial
development and economic growth, especially since the release of the World
Bank’s report in 1989, few works were dedicated to the relationship between
financial development and income inequality.
Moreover, in explaining income inequality, the literature often considered financial development as exogenous, assuming that financial systems remain unchanged with time (Demirgüç-Kunt and Levine, 2009). Academic researches focused mainly on the impact of fertility, education and redistributive policies on
inequality. However, financial systems are in continuous mutation, affecting the
decisions of economic agents and their incomes, by altering human and physical
capital, public politics, etc. Researches in this area are too recent and their number increased since the World Bank published its report in 2007 on the reduction
of poverty through “financial inclusion”.
*
Zied Saadaoui is at University of Sfax, Tunisia. Address : Route de l’Aéroport Km 4 Sfax 3018 Tunisie
(casier # 17). Corresponding author : [email protected] / [email protected]
†
Maher Gassab is at University of Manouba, Laboratoire de Recherche en Microéconomie Appliquée (LARMA)
5 Henceforth called MENA countries.
54
The recent international crisis revived the debate over economic and financial
policies that developing countries should apply in order to deal with income
inequality and to reduce vulnerability of poor people to cyclical downturns. Although MENA countries were relatively protected from financial crises, thanks
to their weak international financial integration, growing globalization of financial services will certainly exerts positive impacts on their economies but with
growing risk of financial crises that may aggravate poverty rate. In this context,
it is interesting to focus on the effects that financial development may exert on
income inequality in the MENA countries. The understanding of such effects
is crucial to make financial systems of these countries work more efficiently in
favour of the United Nation’s millennium development goals.
MENA countries don’t suffer from high poverty or inequality rates, because
many of them profit from important oil rents and are classified among upper
middle income countries. But economic indicators are not always good in this
region of the world, due to the fluctuation of oil prices. According to the United
Nation Development Program’s report published in 2009 and dedicated to the
Arab countries, this region grew very slowly since 1980, with an annual growth
rate less than 0,5% according to the World Bank’s data. The same report indicates that 40% of the population in this region was below the poverty threshold
and that inequality was increasing over time.
The purpose of this paper is to explore the determinants of income inequality
in the MENA region, by focusing mainly on the impact exerted by financial development and instability. The paper is organized as follow: Section 2 offers a
description on how financial development and instability can influence income
inequality. Section 3 presents the principal empirical works dealing with this
field of research, while the fourth section focus on the case of the MENA countries by studying a sample of 11 countries observed from 1963 to 2002. Finally,
section 5 concludes this paper.
2. Financial development, instability and income inequality
2.1. Financial development and the conduit effect
Financial development tends to reduce income inequality through two principal effects: a direct effect, by ameliorating access to financial services and
an indirect effect, by stimulating economic growth. Through the direct effect
financial development can influence the situation of the poor and the distribution of income. To better explain these arguments, let’s consider the opposite
case, i.e. the underdevelopment of financial system which is a major problem
for many developing countries. In this case, poor households will be excluded
from financial services, simply because they can’t pay the cost of these services
(Beck et al., (2006, 2007); World Bank, (2008)). Market imperfections, like moral
hazard and adverse selection, are likely to complicate access to credit for poor
people who don’t possess required collaterals, in contrast to the rich people
who possess sufficient assets to pledge (Greenwood and Jovanovic, (1990) ;
Claessen and Perotti, (2007); Beck et al., (2009)). This situation is likely to deepen income inequality.
55
Besides, during the two last decades, several studies tried to empirically demonstrate the existence of a positive relationship between financial development and economic growth (King and Levine, (1993); Arestis and Caner, (2004);
Levine, (2004)). In parallel, other empirical approaches tried to test the impact
of economic growth on income inequality and on poverty, like Dollar and Kray
(2002). These authors tested the relationship between per capita income and the
income share of the poorest quintile, using a sample of 80 countries observed
during four decades, they find a positive and significant relationship between
the two measures. According to these studies it is possible to hypothesise that financial development can be a robust determinant of income inequality, through
his effect on economic growth, as demonstrated empirically by Beck et al. (2007),
Clark et al. (2003) and also Guillaumont Jeanneney and Kpodar (2008). Beck et
al. (2007) make the assumption that financial development, by impacting economic growth, may have two opposite effects on the poor. On the one hand, by
reducing credit market imperfections, by enhancing transparency or lowering
transaction costs, and by allocating resources more efficiently, financial development facilitate access to credit for poor households who cannot pledge personal
collateral. This permits also to lower inequalities in the society given that poor
borrowers wishing to start their own business will not be deprived from credits.
But in the other hand, financial development can profit mainly to rich people,
since they are more likely to own collaterable assets in the first stage of economic
development, particularly when credit constraints are not reduced (Greenwood
and Jovanovic, (1990)). This situation may worsen if the rich minority have the
capacity to lobby for economic policies in order to keep safe its wealth, which
can be detrimental to the distribution of income and to economic growth (Li et
al., (1998)). Moreover, financial liberalization may help the poor to have better access to financial services, by encouraging competition between banks, by
creating new sources of capital and by cutting borrowing costs. But capital account opening may also profit to rich minority given their political relationships
that enable them to capture foreign capital allocations (Li et al., (1998); Arestis
and Caner, (2004)). However, there may be a problem of endegeneity between
finance and income inequality. Indeed, financial development can be influenced
by the level of poverty. For example, the reduction of poverty may stimulate the
demand of credit and enhance the development process of financial services.
Then, the reduction of inequality may constraint policy makers to make financial markets more transparent and more efficient, by preventing political relationships and favouritism in financial transactions (Beck et al., (2009)).
We now turn to another hypothesis that may explain how financial services can
be a solution to persistence of income inequality. According to McKinnon (1973),
in developing countries where credits are often not accessible to poor people,
investment is often confined to self-financing. In this case, deposit and saving
instruments become an attractive source of self-financing and the demand for
real money balances become positively related to real interest rates on savings
and deposits. This makes capital and money complementary rather than substitutable. Consequently, financial development can stimulate demand for money
and quasi-money balances which play the role of a supplementary financial conduit for capital accumulation and for economic growth (Kpodar, 2006 ; Guillaumont Jeanneney and Kpodar, 2008). This conduit effect will turn to be inefficient
56
in case of financial repression, i.e. interest rate restrictions, higher legal reserves
requirements, political interference, etc., which restrain investment opportunities, especially for poor households.
2.2. Financial instability and banking inefficiency
Despite its negative effect on poverty and inequalities, financial development
may be the resultant of economic instability with adverse effects on household’s
income. MENA countries have not experienced extreme episodes of financial
and economic recession these last years. But, if we suppose that there is a strong
linkage between finance and income inequality, it will become interesting to test
if credit and liquidity turbulence, even minimal ones, hurting the financial and
the payment system, can increase income inequalities. In fact, during recession
periods, several problems can hurt the payment system and lead to an accumulation of non-performing loans or even to a deposit freezing. In these circumstances, it is the households with limited budgets that will be the most affected
because they are unable to diversify their assets.
Furthermore, financial instability can occur through the accumulation over
time of non-performing loans, which is the Achilles’ heel of supervisors in
developing countries. Credit losses are a sign of inefficiency in banking which,
in the presence of market imperfections, can aggravate the problem of adverse
selection and credit crunch6 (Krueger and Tornell, (1999) ; Berger and DeYoung, (1997) ; Clair and Tucker, (1993)). This is likely to complicate the access
to financing for the poor and to prevent them to exploit business opportunities, especially during periods of recession. Furthermore, as demonstrated by
Hauner (2008), government involvement in banking activities and the directing of credits towards non-productive political projects, can explain the high
level of loans losses suffered by developing countries’ banks, including those
of the MENA region (Caprio and Honohan, 1999 ; Caprio and Martinez-Peria,
2000 ; International Monetary Fund, 2010). In other words, banking sector’s
health explains its dynamism, and the more this sector is dynamic the more the
economy benefits from financial openness and financial development. Thus, it
would be interesting to estimate the impact of financial instability and banking
inefficiency on income inequality.
On the empirical side, there are few studies dealing with financial development
effects on poverty and income inequality. In order to construct credible hypotheses, these studies relied mostly on earlier works examining the relationship
between financial development and economic growth. In the following section
we try to give a succinct survey on these studies.
3. Review of empirical studies
beck et al. (2007) investigate the impact of financial development on income distribution, measured by the Gini coefficient and by absolute poverty rate. According to the authors, financial system development must be accompanied by a
6 The credit market is not in a situation where the distribution of credits obeys to the law of supply and demand
(the equilibrium interest rate) but rather in a situation where the supply of credits is less than the demand (credit
rationing).
57
more efficient capital allocation and by a reduction of market imperfections. The
study focuses on a sample of 72 countries observed between 1960 and 2005. To
take into account the endogeneity problem, the authors proceed to and estimation of a dynamic panel using the generalized method of moments (GMM). The
results show a significant negative relationship between the indicator of financial development and growth of the Gini coefficient. The results also show the
existence of a significant negative relationship between financial development
and the income share of the poorest quintile7.
By studying the case of India during the period going from 1951 to 2004, Ang
(2008) ask whether the financial development and financial liberalization had a
significant impact on the poor’s situation in this country. Financial development
is measured by the ratio of credit to the private sector, by the ratio of quasimoney (M3 - M1) to GDP and by the ratio of commercial bank assets to commercial bank plus central bank assets. While financial liberalization is measured
by three indicators: interest rate restriction, requirements of cash reserves and
directed credit programs. The estimation of an error correction model shows
that there is a long-term positive relationship between financial development
and the Gini coefficient. In addition, per capita GDP growth reduces inequality
while financial openness produces the opposite effect. Ang (2008) incorporates
also a measure of bank density (number of branches per population) to measure
financial development, not in terms of depth, but in terms of wideness. He finds
a significant negative relationship between this indicator and Gini coefficient
growth.
Li et al. (1998) analyze the determinants of inequality for a sample of 49 developed and developing countries from 1947 to 1994. They regress the Gini coefficient (averaged over 5 years) on a set of financial indicators: financial system
liquidity, M2/GDP and a set of control variables. Using the OLS and the instrumental variables to estimate the model, the authors conclude that financial development exerts a negative impact on inequality and this impact is the largest
among those exerted by the selected variables.
Following the theoretical work of Kuznets (1955) and Greenwood and Jovanovic (1990), Clark et al. (2003) test the hypothesis of non-linearity of the relationship between financial development and inequality. The sample includes
44 developed and developing countries observed during the period from 1960
to 1995. The authors regress the natural logarithm of the Gini coefficient on the
ratio of credit to private sector to GDP and on the value added in industry and
services sectors over GDP to measure the degree of modernization of the economy. An interaction term between this variable and the indicator of financial
development is also introduced into the model to test the hypothesis of Kuznets
(1955), namely that financial development of the economies based on modern
productive sectors, i.e. excluding agriculture, reinforces the unequal distribution
of income. Other control variables were also introduced in the model specification estimated by the ordinary least squares (OLS) and GMM techniques. The
results show that there is a negative linear relationship between financial de7 The authors decompose the effect of financial development on income growth in two distinct effects: a growth
effect and a distribution effect. They found that 40% of financial development effect is a distribution effect, while
60% of this effect is a direct effect on income growth.
58
velopment and inequality. In contrast, the model estimation rejects the hypothesis of a nonlinear relationship between financial development and inequality.
Estimations results show, however, that there is a positive and significant effect
exerted by the interaction term on inequality, confirming the existence of a nonlinear relationship derived from the hypothesis of Kuznets (1955)
Arestis and Caner (2009) focus on whether capital account openness had a significant impact on poverty. They study a panel of 64 developing countries, observed annually from 1985 to 2005. The authors regress on poverty8 on an index
of capital account openness (KAOPEN), developed by Chinn and Ito (2002)9. Estimation of a dynamic panel model, using the system GMM technique, shows
that capital account liberalization paradoxically deteriorates the situation of the
poor. Indeed, capital account openness is negatively related to the income share
of the poor and positively related to poverty gap.
Guillaumont Jeanneney and Kpodar (2008) identify two channels through which
financial development can affect poverty: the conduit effect and financial instability (cf. supra). They regress poverty indicators on two indicators of financial
development: M3/GDP, measuring the supply of savings and deposit instruments in the economy and the ratio of credit to the private sector. They introduce
also a variable of financial instability measured by the volatility of the two forementioned indicators of financial development. The sample includes 75 developing countries observed between 1966 and 1999. Estimation of the Model by
OLS and system GMM techniques showed a significant negative relationship
between M3/GDP and the different measures of poverty. Financial instability,
measured by M3/GDP, tends to aggravate the situation of the poor and to increase poverty. In contrast, credit to the private sector is not significantly related
to poverty indicators.
In the next section, the main hypotheses of this study will be exposed. These
hypotheses were inspired from the different theoretical and empirical studies
presented throughout this paper. We also present the selected variables, the estimation technique and the selected sample.
4. Empirical study
4.1 Hypotheses, variables and model
4.1.1 Hypotheses
There are four hypotheses to test:
• Financial development and better access of households to financial services reduces income inequality through the stimulation of economic growth
and the creation of new sources of income for the poor, like entrepreneurship.
8 Three measures of poverty have been selected: the index of poverty, measured by the number of households living
below the $ 2.15 per day threshold (PPP $ of 1993), the poverty gap index, which measures the intensity of poverty,
i.e. distance from the poverty line, and finally, the income share of the poorest quintile.
9 This index is the first principal component of four binary variables selected from the IMF Annual Report on
foreign exchange restrictions (Annual Report on Exchange Arrangements and Exchange Restrictions).
59
• In cases where access to credit is difficult for poor households, financial development induces banks to offer more profitable financial opportunities for
savings, which under the hypothesis of the conduit effect, can stimulate accumulation of monetary deposits and constitute an alternative solution for
low-income households to invest and get rich.
• Financial development may be accompanied by episodes of instability
which may worsen the situation of poor households and aggravate income
inequality.
• Increasing the proportion of bank loans granted to the government, leads to
the accumulation of non-performing loans and to more inefficient allocation,
which may complicates the access of poor households to credit, especially
in times of crisis.
The second and the third hypothesis, namely the effect of financial system liquidity and financial instability on income inequality have been inspired by the
study by Guillaumont Jeanneney and Kpodar (2008). The main contribution of
this study lies in the fourth hypothesis, which we consider as more appropriate
to the context of MENA countries, whose banking and financial systems have
not experienced remarkable episodes of instability. Therefore, it is useful to assess the fragility of these systems through their relationship with their government which could impede their efficiency and depth.
4.1.2 Income inequality indicator
The indicator of income inequality used in this study is the natural logarithm
of the Gini coefficient (GINI), observed annually from 1963 to 2002. This coefficient, derived from the Lorenz curve, measures inequality of income distribution within a country. The larger its value, the higher inequality is. To have a
sufficient number of observations, we used the Estimated Household Income
Inequality (EHII) database developed under the University of Texas Inequality
Project. This database measures inequality using the average income by group
of population, eg. by industry, sector or region. These semi-aggregated data
may contain sufficient information on the evolution of inequality and income
distribution. The advantage of this method, based on the Theil index, is that it
overcomes two important obstacles, in contrast to the database developed by
Deninguer and Squires (1996): data availability and imprecision of measures of
income. Wage inequality, derived from the Theil index, is converted in order to
adequately approximate inequality of household’s incomes, resulting in a dense
and consistent data set of income inequality measures in Gini format (Galbraith ,
2008).
4.1.3 Financial development indicators
The measures of financial development should reflect the consolidation of the
financial system over time and the reduction of market imperfections, which
make finance more beneficial for the poor. Financial development variables
used in this study are extracted from the International Financial Statistics database (IFS CD-ROM, (2007)). Following previous studies, we use the indicator
of credit to the private sector on GDP (CSP) to estimate the improvement of
households and private sector access to financial services offered by banking
60
and non-banking financial institutions in the MENA region. This indicator is
a more accurate measure of market imperfections and of development of payment instruments. Indeed, CSP does not take into account credits granted by
the central bank and development banks, credits to the public administration
and interbank loans. This indicator is considered more efficient than the ratio
of commercial bank assets to commercial bank plus central bank assets, proposed by King and Levine (1993), for two reasons: first, because the mission of
the central bank can change from one country to another and can concentrate
on directing bank loans toward political development goals and toward privileged sectors and firms; Then, because commercial banks are not the unique
financial actor able to grant loans, other non-bank financial intermediaries
may also offer this service (Beck et al. 2007). In addition, the indicator of credit
to private sector is often found to be positively and significantly related to
economic growth (Clark et al., 2003). We also introduce a second indicator of
financial development (M2), whose interest is to measure the development of
savings instruments and demand for real money balances. This indicator is
measured by the ratio of M2 (cash items, demand and savings deposits, etc.)
on GDP. Using the M3 indicator would be more accurate because it takes into
account also money market instruments (mutual funds, certificates of deposit,
commercial papers, etc.), But the database used in this study (International Financial Statistics) does not provide these data for the MENA region. However,
M2 indicator remains quite relevant in estimating the financial conduit effect
and its impact on income inequality.
4.1.4 Financial instability indicators
To measure financial instability, we follow Guillaumont Jeanneney and Kpodar
(2008) which calculate the volatility of each financial development indicator (CSP
and M2) by assuming that these variables do not follow a stochastic trend. Thus,
we regress a variable of financial development, denoted x, on its lagged value
and a time trend (t). If we denote Vx the measure of financial instability, then:
(1)
Where εt is the error term of the following equation estimated by OLS:
(2)
Thus, the absolute value of the residuals measure the volatility of the variable
x, assuming that this variable follows a time trend (t), contrary to other measures of volatility such as the standard deviation. Financial instability indicators,
VCSP (for CSP) and VM2 (for M2), are obtained following two steps: first, we
estimate equation (2) for each selected country, in order to obtain the residual series for each country. Then, at each point of income inequality data we associate
a five-year average of the residuals absolute value (the year of observation plus
the four precedent years ), i.e. we suppose that n = 5 in equation (1).
61
4.1.5 Banking inefficiency
As noted by Honohan (2004a), macroeconomic indicators, like CSP and M2, may
not provide accurate estimates, as these indicators do not integrate all the elements that would promote financial development in a country, like the legal and
judicial system efficiency, information transparency, regulatory mechanisms,
etc. In China, for example, the indicators CSP and M2 can not reflect, with a high
confidence level, the depth of the financial system, given the strong government
involvement in the banking system (Honohan, 2004b)10. Consequently, we propose to add a measure of loans granted to the government relative to total loans
granted by the banking system (GOV)11. We make the assumption that in economies where the government capture a significant share of bank loans, the financial system would suffer underdevelopment and liquidity problems. This result
was empirically demonstrated by Hauner (2008) on a sample of 142 developing
countries’ banks. Results show that the increase of credit to the government has
a negative and significant impact on liquidity of the banking system, i.e. the
depth of the financial system and its productive efficiency. This problem is due
to a misallocation of these credits, often directed to finance unproductive projects (Boyreau-Debrey and Wei, 2005). The dominance of credit to government
may also involve higher political interference, often associated with a lower efficiency of the banking sector, and thus to an underdevelopment of the financial
system that could hamper economic growth and increase inequality (King and
Levine, 1993; Claessens and Perotti, 2007).
4.1.6 Control variables
The model also takes into account a set of control variables obtained from the
World Development Indicators database (WDI CD-ROM, 2007). This vector of
variables includes the natural logarithm of per capita GDP in constant 2000 U.S.
dollars (GDP), inflation measured by the natural logarithm of the consumer
prices index (base 100 in 2000), denoted CPI; trade openness (TRADE), measured by the sum of exports and imports of goods and services divided by GDP;
and government consumption (GOVEXP), measured by the ratio of government
consumption expenditure as a percentage of GDP. The evolution of per capita
GDP is supposed to reflect the growth of national income. It is expected that the
higher the GDP per capita, the higher is the income of poor households and the
lower is income inequality. It is also assumed that the poor and middle class
population is more vulnerable to inflation than the class of wealthy households
who benefit from easier access to finance that allow them to smooth their consumption through time. So, it is expected that inflation is positively related to
income inequality. We also expect that trade integration is associated with an
increase in inequality (Barro, 2000). However, the relationship between government consumption and the Gini coefficient is less obvious, since it can be positive or negative. It depends on the government redistribution policies (the tax
system and social transfer). If State interventions are pro-poor, in this case this
variable will be negatively related to income inequality. However, if rich minority exerts its lobbying power to profit from redistributive policies in order to
10 In 2004 the four biggest state banks in China owned more than 50% of banking assets (Jia, 2009).
11 Loans granted by commercial banks and other credit institutions, such as savings banks, development banks,
saving and loans associations, offshore banks, etc.
62
get richer, then the relationship between government consumption and the Gini
coefficient may be positive.
4.2. Econometric methodology
The model specifications adopted in this study are as follows:
(3)
(4)
The subscripts i and t refer, respectively, to the countries and the year of observation. The error terms ε and μ are assumed to follow a random walk. This
model admits that the lagged values of the dependent variable affect current
values. We use the dynamic panel Generalized Method of Moments estimator
(system GMM) developed by Arellano and Bover (1995) and Blundell and Bond
(1998), used also by Guillaumont Jeanneney Kpodar (2008) and Arestis and Caner (2009). Compared to the OLS, system GMM is more efficient to control for the
endogeneity between financial development and inequality, and between the
dependent variable and the other explanatory variables (Beck et al. 2007, 2009).
Panel techniques are advantageous, since they consider both the individual dimension and the temporal dimension of data. Moreover, the dependent variable
appears to the right of equations (3) and (4), which may result in a correlation
between specific individual effects and explanatory variables. To get more efficient estimates, it is possible to estimate the model using first differences to
remove the countries’ unobserved specific effects and to instrument explanatory
variables using their lagged values. This method is called the first-differenced
GMM proposed by Arellano and Bond (1991). System GMM technique consists
of a combination between a set of first-differenced equations, whose instruments
are lagged levels with a second set of equations in levels using lagged first differences as instruments. According to Blundell and Bond (1998), this methodology
provides more efficient estimators than first-differenced GMM because, even
if the variables are very persistent, the instruments used in the level equation
adequately predict the endogenous variables. In addition, Monte Carlo simulations give evidence that the system GMM method is more efficient than the firstdifference GMM, when using a small sample size, which is the case in this study.
Finally, to test the validity of the model, we use the Sargan test of over-identifying restrictions to check the validity of instruments and the Arellano and Bond
serial correlation test to verify if errors exhibit second order serial correlation.
4.3. Selected sample
The study focuses on a sample of eleven MENA countries, observed annually
between 1963 and 2002. Sample selection was mainly constrained by data availability. Table 1 shows the number of times the dependent variable Gini was observed for each selected country.
63
Table 1
Selected sample
Country
Period
Observations
Morocco
1967-2000
26
Algeria
1967-1997
28
Tunisia
1963-2001
28
Libya
1964-1980
17
Egypt
1964-2002
36
Syria
1963-1998
36
Iran
1963-2000
38
Jordan
1963-2002
37
Kuwait
1967-2001
35
Oman
1993-2002
10
Qatar
1986-2002
11
Total observations
302
4.4. Empirical results 4.4.1. Descriptive statistics
Table 2 shows that for the overall sample, GINI has averaged 44.7. But this variable
is quite volatile with a standard deviation of 6.04. The average annual per capita
GDP for the whole MENA region amounted to almost U.S.$ 1980, evolving from
a minimum of U.S.$ 482.63 and a maximum of U.S.$ 44018.7, with a standard deviation approximately equal to 1.06.The CPI index is measured by setting the year
2000 as base year, which may explain its moderate value (expressed in logarithm)
and the low values observed especially at the beginning of the observation period.
In fact, inflation changed considerably over the past three decades in MENA countries, which explains its high volatility throughout the observation period, with
a standard deviation of 2.13. The variable TRADE amounts to 67.7% on average
for the entire sample, with a maximum value of 154.6%. GOVEXP variable, measuring the share of government spending in GDP, is characterized by relatively
low volatility (standard deviation equal to 0.089) and amounting approximately
to 21%. Financial development indicators are relatively stable, characterized by
a standard deviation of 0.24 and 0.23 for CSP and M2 respectively. Credits to the
private sector (CSP) are on average 35% of GDP for the whole sample, while the
liquidity of the banking system (M2) reached nearly 55% of GDP. The variable
GOV, measuring the share of lending to the government, averaged 16.5% and is
characterized by a relatively stable variation, with a standard deviation of 0.15.
64
Table 2
Descriptive statistics – Overall sample
Variable
Observations
Mean
SD
Min.
Max.
GINI
302
44.69
6.04
27.21
58.39
GDP per capita
309
1979.36
1.06
498.63
44018.70
M2 (%)
333
0.55
0.24
0.14
1.92
CSP (%)
325
0.35
0.21
0.04
1.04
CPI (log)
301
2.63
2.13
-3.61
4.65
TRADE (%)
334
0.67
0.27
0.13
1.54
GOV (%)
323
0.16
0.15
0
0.89
GOVEXP (%)
334
0.21
0.09
0.07
0.76
Study period extends from 1963 to 2002. The observed sample includes eleven MENA countries.
SD = Standard deviation. GINI = Gini Coefficient. Annual per capita GDP is expressed in constant
dollars of 2000. M2 = (Money + quasi-money)/GDP. CSP = Credits to the private sector/GDP.
CPI = Consumer Price Index (base year : 2000). TRADE = (Importations + exportations)/GDP.
GOV = Credits to gouvernement/Total banking loans. GOVEXP = Governement consumption/GDP.
The comparison of statistical properties of the selected variables between Arabian Peninsula countries (Kuwait, Qatar and Oman) and the other selected MENA
countries is useful given the structural differences between the economies of
these two groups of countries. According to the tables 3 and 4, we note, first, the
significant difference in the average GINI between the two groups of countries,
which explains in part the high volatility of this variable for the overall sample. Table 3
Descriptive statistics – Arabian Peninsula countries
Variable
Observations
Mean
SD
Min.
Max.
GINI
56
52.18
2.61
48.42
58.39
GDP per capita
52
16261.99
0.48
7541.12
44018.70
M2 (%)
61
0.58
0.28
0.18
1.92
CSP (%)
61
0.42
0.23
0.09
1.04
CPI (log)
57
4.36
0.25
3.67
4.62
TRADE (%)
62
0.91
0.12
0.65
1.42
GOV (%)
54
0.28
0.27
0
0.89
GOVEXP (%)
62
0.25
0.11
0.07
0.76
Study period extends from 1963 to 2002. The observed sample includes Kuwait, Qatar and Oman. SD
= Standard deviation. GINI = Gini Coefficient. Annual per capita GDP is expressed in constant dollars
of 2000. M2 = (Money + quasi-money)/GDP. CSP = Credits to the private sector/GDP. CPI = Consumer
Price Index (base year : 2000). TRADE = (Importations + exportations)/GDP. GOV = Credits to gouvernement/Total banking loans. GOVEXP = Governement consumption/GDP.
65
Table 4
Descriptive statistics – MENA countries (excluding Arabian Peninsula)
Variable
Observations
Mean
St D
Min.
Max.
GINI
246
42.99
5.25
27.21
55.34
GDP per capita
257
1292.58
0.49
489.62
6075.13
M2 (%)
272
0.54
0.23
0.14
1.37
CSP (%)
264
0.33
0.20
0.03
0.77
CPI (log)
244
2.23
2.18
-3.61
4.65
TRADE (%)
272
0.62
0.27
0.13
1.54
GOV (%)
269
0.14
0.09
0
0.58
GOVEXP (%)
272
0.19
0.07
0.10
0.50
Study period extends from 1963 to 2002. The observed sample includes Morocco, Algeria, Tunisia,
Libya, Egypt, Jordan, Syria and Iran. SD = Standard deviation. GINI = GINI Coefficient. Annual per
capita GDP is expressed in constant dollars of 2000. M2 = (Money + quasi-money)/GDP. CSP = Credits
to the private sector/GDP. CPI = Consumer Price Index (base year : 2000). TRADE = (Importations +
exportations)/GDP. GOV = Credits to gouvernement/Total banking loans. GOVEXP = Governement
consumption/GDP.
For the Arabian Peninsula countries the Gini coefficient reached an average of
52.18, much higher than that observed for the rest of the MENA countries (43 approximately). For this group of countries, the volatility of GINI is still high with
a standard deviation of 5.25. The large difference in the level of GINI recorded between the two groups of
countries can be explained by the calculation method of this indicator by Galbraith’s team (UTIP), based on wages distribution and not on income, unlike the
methodology used by the World Bank (Deininger and Squire, 1996). On the other
hand, per capita GDP varies considerably between the two groups of countries,
with an average of U.S. $ 16262 in the Arabian Peninsula countries and U.S. $
1292.5 in the remaining MENA countries. This result is not surprising given that
the first group of countries are oil producers and exporters and are much less
populated than the first group. It appears also that Inflation, in the Arabian Peninsula countries, is almost two times higher than in the other selected countries. Arabian Peninsula countries seem to be more integrated economically, with an
average TRADE ratio equal to 91% against 62% for the other MENA countries.
Public spending recorded a higher average in the Arabian Peninsula countries,
with a ratio GOVEXP evolving around an average of 25%, whereas this ratio is
only 19% in the rest of the MENA countries. In terms of financial development,
the statistics do not show a significant difference in the level of M2, which is respectively 58% and 54% for the Arabian Peninsula countries and the remaining
MENA countries. In this group of countries, however, the average level of credit
to the private sector, amounting to 32% of GDP, is less than that recorded in the
Arabian Peninsula countries, which amount to 42%. The share of loans to finance
public administration in this latter group of countries stands at 28% of GDP and is
on average two times higher than in the other selected countries (14%).
66
4.4.2. Estimation results
Table 5 shows the system GMM estimation obtained by using the “xtabond2”
command in STATA 9.2. P-values for over-identification and serial correlation
tests are quite high, which does not reject the null hypotheses of no correlation
between the instrumental variables and error terms (Sargan test) and of no second order correlation between errors (Arellano and Bond test). Columns (1) and
(2) present the estimation results of equations (3) and (4) respectively.
Column (1) shows that per capita GDP is positively and significantly related
to GINI. Although contrary to our expectations, this result can be interpreted
in two ways. First, it is possible that economic growth is more beneficial to the
wealthy class of the population, than to the poor. Then, according to Clark and
al. (2003), who found a similar result using the initial per capita GDP, this positive relationship can be explained through the theoretical arguments of Kuznets
(1955), namely that it is possible to find a non-linear (concave) relationship between income inequality and GDP. More specifically, to a certain level of economic development, agriculture, which is a less productive but more egalitarian
productive sector, will be gradually dominated by modern, but less egalitarian,
sectors, like services and industry sector. So that at the early stage of economic
development, which is the case of MENA countries, inequality is likely to increase. Inflation has a positive but non-significant effect on GINI. This result
was already found by Clark and al. (2003) and Guillaumont-Jeanneney and
Kpodar (2008). Moreover, trade liberalization in MENA countries is positively
and significantly related to the Gini coefficient, which is in accordance with our
expectations. An increase in TRADE by 1 percentage point (pp) causes an increase in GINI by 0.08465 pp at the 0.01 level. This result therefore confirms
the allegations of Barro (2000), namely that economic integration of countries
induce a worsening of income inequality. The parameter related to the variable
GOVEXP is negative and significant at 95 percent confidence level. An increase
in government consumption expenditure by 1 pp reduces inequality by 0.18671
pp. This confirms our initial hypothesis that the redistribution policies adopted
by the MENA countries governments are more oriented toward limited-income
households.
Let’s turn now to the main hypotheses of this study. Column (1) shows a significant relationship between credits to the private sector and the Gini coefficient. Contrary to the results found by Beck and al. (2007) and Clark and
al. (2003), the measure of financial development through access to credit does
not appear to influence the situation of poor households and income inequality
in MENA countries. This result is against our expectations of a negative relationship between CSP and GINI, that can be induced either directly through
the easing of credit constraints and the elimination of market imperfections, or
through stimulating economic growth. Furthermore, a surprising result appears
in column (1). In fact, VCSP is negatively related to the dependent variable GINI,
suggesting that the instability of credit availability reduce rather than aggravate
income inequality. In their study, Kpodar Jeanneney and Guillaumont (2008)
found that credit volatility does not enter significantly. However, the negative
relationship between financial instability and inequality can be explained by the
differences between countries in terms of redistribution policy. VOLUME 7, NUMBER 4, 2013
67
Table 5
Estimation results
Dependent variable: GINI
GINIt-1
GDP per capita
CPI
TRADE
GOVEXP
GOV
CSP
(1)
(2)
0.65882***
0.68827***
(0.000)
(0.000)
0.01511**
0.00620
(0.046)
(0.440)
-0.00140
-0.00189
(0.694)
(0.564)
0.08465***
0.12176***
(0.004)
(0.001)
-0.18671**
-0.19683**
(0.031)
(0.030)
0.10980**
0.14413***
(0.027)
(0.009)
-0.03212
(0.327)
M2
-0.07874**
(0.033)
VCSP
-0.03730**
(0.036)
VM2
0.11243
(0.560)
Constant
1.17426***
1.11242***
(0.000)
(0.000)
228
227
214.678
216.529
p-value (Sargan)
0.307
0.27
Arellano-Bond AR(2)
0.77
0.67
p-value (AR(2))
0.44
0.50
Observations
Sargan statistic
* significant at 10%; ** significant at 5%; *** significant at 1%
This table shows the estimated parameters α and β of equation (3) and (4). P-values Statistics in brackets. Study period extends from 1963 to 2002. Observations were made on a sample of 11 countries in
the MENA region. GINI = GINI Coefficient. Annual per capita GDP is expressed in constant dollars of
2000. M2 = (Money + quasi-money)/GDP. CSP = Credits to the private sector/GDP. CPI = Consumer
Price Index (base year : 2000). TRADE = (Importations + exportations)/GDP. GOV = Credits to gouvernement/Total banking loans. GOVEXP = Government consumption/GDP. VCSP = volatility of CSP.
VM2= volatility of M2.
68
In times of economic and financial downturn, governments can adopt strategies
to protect the poor, while rich households bear a heavier income losses (Christiaensen et al., 2003), inducing a decrease of income inequality. Finally, we note
from column (1) that the share of bank loans for the government and public
entities is positively and significantly related to GINI. An increase of 1 pp of the
variable GOV is associated with an increase in GINI by 0.1098 pp. This result
is consistent with our expectations because it shows that in economies where
the government maintain a strong relationship with the banking system, the
financial sector may suffer more from underdevelopment, which may reduce his
beneficial impact on economic growth and income distribution.
Column (2) of Table 4 shows the estimation results of equation (4). We note,
first, that per capita GDP loses its significance, but it is still positively related to
GINI. Then, as showed previously, inflation is still non-significant. Moreover, we
find a positive and significant relationship between TRADE and the Gini coefficient, at 99 percent confidence level. Whereas, the variable GOVEXP is negatively and significantly related to GINI, which confirms previous results, namely
that government spending in the MENA countries appear to be directed more
towards poor households in order to reduce income inequality.
Column (2) also shows results on the financial conduit effect that may be enhanced through the development of deposits and savings instruments. It appears that M2 is negatively and significantly related to GINI. An increase by 1 pp
in M2 is induce a reduction in the Gini coefficient by 0.07874 pp. This confirms
the conduit effect hypothesis, according to which greater supply of profitable
savings opportunities accelerates the demand for real money balances and allows low-income households and entrepreneurs to accumulate capital to make
indivisible investments. This is likely to induce convergence of incomes between
rich and poor classes, especially in developing countries such as the MENA
countries, where poor households’ access to credit remains difficult. The results
show, however, that the second financial instability indicator (VM2) measuring
the volatility of M2, does not enter significantly. Finally, we note from column (2)
the existence of a negative and significant correlation between GOV and GINI,
at 99 percent confidence level. The value and the significance of the coefficient
related to the variable GOV are higher than those recorded previously. This confirms that increasing the share of loans to government, which may be also a
sign of growing political interference, impede the financial system to reach an
advanced stage of development that can allow it to reduce income inequality,
through a negative effect on allocative efficiency and productivity of the banking system.
5. Conclusion
Through this work we tried to emphasize the importance of financial development in dealing with the problem of income inequality. Indeed, it goes without
saying that considering the financial system as a frozen system is probably an
important limitation suffered from several works dealing with the issue of income inequality. Following previous studies that have attempted to examine the
impact of financial development on poverty and income inequality, we tested
69
such an impact on a sample of MENA countries. During recent decades, this region has recorded profound economic changes and significant macroeconomic
fluctuations that may exacerbate poverty and inequalities. Our empirical results
show, first, that under the conduit effect hypothesis (McKinnon, 1973; Kpodar,
2006), financial development, through the stimulation of demand for real money
balances, significantly reduces income inequality in the MENA region. Then,
the increase in the share of banking loans granted to government, which may
reflects a growing political interference, restricts the ability of MENA countries”
financial systems to reduce income inequality. Finally, there is a negative relationship between financial instability and income inequality. This paradoxical
result can be explained by the fact that MENA countries’ redistributive policies
are more directed to protect poor people in times of recession.
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Sections/subsections: they must be numbered consecutively such as 1., 1.1, 1.2, 2., 2.1, 2.2, etc.
Math formulas: Formulas should be centered and numbered consecutively throughout the manuscript as (1), (2), etc. They must be edited with equation editor in Word.
Footnotes: Footnotes should be numbered consecutively throughout the manuscript using superscript Arabic numbers and should be used sparingly.
Figure captions: Please make sure that each illustration has a brief title and a description of the illustration (if any).
Tables: Tables should be numbered consecutively in accordance with their appearance in the text.
Place footnotes to tables below the table body.
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in the Références section and that Références are in journal style. There is no limitation on the number of Références, but it is preferable that it does not exceed 35 in general. Citations in the body text
should be as follows: Fama (1991) or (e.g., Fama, 1991).
References should appear as follows:
• Books: Jorion, P., 2007. Financial Risk Manager Handbook. 4th Edition, John Wiley & Sons.
• Journals: Fama, E.F., French, K.R., 2004. The capital asset pricing model: theory and
evidence. Journal of Economic Perspectives 18, 25-46.
• Forthcoming papers and papers in press: Bekaert, G., Harvey, C.R., Lundblad, C., 2010. Financial openness and productivity. World Development, forthcoming.
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