t - Institut des Actuaires
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t - Institut des Actuaires
Théorie du risque et corrélations Stéphane Loisel ISFA, Univ. Lyon 1 [email protected] Plan de l’exposé • Solvabilité II: à qui la faute? • Motivations • Résultats de maths (sieste) Solvabilité II (S2): ce qu’en pensent les universitaires • Une mesure de risque liée à Bâle II et aux agences de notation: la VaR à 99,5% • Mathématiquement et statistiquement impossible à quantifier • Une intention louable au début du processus • Qui a contribué à diffuser la culture du risque dans le secteur de l’assurance • MAIS une formule standard bien trop complexe • Calendrier intenable de manière à ne pas avoir trop de modèles internes à auditer en 1ère vague • De nouvelles incitations à abandonner certaines pratiques de bon sens, notamment en gestion actif-passif, en stratégie d’investissement, en réassurance, … • Nécessité de garder ses pratiques saines de gestion des risques, tout en les adaptant partiellement en fonction de S2. 2 99,5 % 3 Gestion des risques 1 Scénarios 2 3 VaR à 90%, TVaR à 80% Modèle interne (partiel ou non) adapté au pilotage et à la gestion des risques (stratégies de réassurance et d’investissement, risk limits, …) VaR à 99,5% Modèle interne pour Solvabilité II: si une grippe (type 1918) se produisait, quel serait le choc sur les actifs? Risques NBC, risques terroristes, rachats, taux, contrepartie: VaR à 99,5% Corrélations? Corrélations des extrêmes? Solution proposée (trop tard!) • Ne pas parler de VaR à 99,5% mais réfléchir aux scénarios adaptés • Prise en compte de la réass • Prise en compte du risque endogène • Prise en compte des autres crises de corrélation • Construire les scénarios: le + dur! • Que faire des chiffres issus des scénarios? Risque endogène, crises de corrélation • Risque endogène et cercles vicieux • Crises de corrélation • Incertitude de modèle, hétérogénéité et corrélation • Mesures de corrélation: 1 piège révélateur Endogenous uncertainty • Uncertainty is generated/modified by response of individual entities to events • Feedback loop: outcomes -> forecasts -> decisions -> outcomes -> revised forecasts -> revised decisions -> … (Millennium Bridge) • Statistical relationships are endogenous to the model, and may undergo structural shifts (Goodhart’s Law) • Relevant when individual entities are similar in terms of forecasts and likely reactions to events • Relevant when outcomes are sensitive to concerted actions Le risque endogène est implémentable assez facilement • Travaux de Rama Cont • Travaux avec X. Milhaud sur les crises de corrélation et risque de rachat When diversification fails: correlation, contagion and endogenous risk Rama CONT Joint work with: Lakshithe Wagalath (Univ. Pierre & Marie Curie, Paris-VI) & Laurent PICHARD (Columbia University, New York) The economic origin of correlations in returns Two different origins: Correlation in fundamentals: common factors in returns (usual explanation)→ correlation in ”fundamentals”, should not vary strongly in time Correlation from trading: generated by systematic supply/demand generated by specific (often rule-based) trading strategies → depends on market liquidity 8 Price impact from trading Portfolio allocation of a fund 𝜙𝑎 (𝑡) = (𝜙𝑎1 (𝑡), ..., 𝜙𝑎𝑛 (𝑡)) Variation in portfolio from trading: Δ𝜙𝑎 (𝑡) = 𝜙𝑎 (𝑡) − 𝜙𝑎 (𝑡 − ℎ) Aggregate excess demand of institutional investors/ fund managers: ∑ Δ𝜙(𝑡) = (Δ𝜙𝑖 (𝑡), 𝑖 = 1..𝑛) = Δ𝜙𝑎 (𝑡) (3) 𝑎∈𝔸 We model the evolution of the price as the sum of a random component reflecting “fundamental” volatility + a price impact term due to institutional trading: price impact function Δ𝑆𝑡𝑖 𝑆𝑡𝑖 = 𝜇𝑖 ℎ Expected return Δ𝜙𝑖 (𝑡) ) + 𝜎𝑖 𝜖𝑖 (𝑡) (4) 𝜆𝑖 random component + 𝑓( market depth 9 0.5 0.4 0.3 λ=10 λ=100 λ=2 0.2 0.1 0 −0.1 −0.2 −0.3 −0.4 −0.5 −10 −5 0 5 Figure 1: Price impact function 10 10 Example 1: fixed-mix strategies A large proportion of mutual funds follow fixed-mix strategies. Fund manager has a (medium term) target allocation in terms of proportions allocated to each asset class 𝑖. To maintain the target allocation, the fund manager needs to rebalance periodically his/her portfolio as prices moves. For a long-only portfolio this leads to buy if price decreases, sell if price increases, until strategic allocation is revised. 11 Simulation example 3 asset classes 100 funds Input noise covariance (“fundamental” ⎛ 0.2 0 ⎜ Σ=⎜ ⎝ 0 0.1 0 0 13 covariance) ⎞ 0 ⎟ 0 ⎟ ⎠ 0.3 240 220 200 180 160 140 120 100 80 0 1 2 3 4 5 Figure 2: A joint evolution scenario for 3 asset classes. 14 Covariance of fundamentals: ⎛ 0.2 ⎜ Σ=⎜ ⎝ 0 0 Realized correlations: ⎛ 1 ⎜ ˆ 𝐶=⎜ ⎝ 20% 21% 0 0 ⎞ ⎟ 0.1 0 ⎟ ⎠ 0 0.3 20% 21% ⎞ ⎟ 1 52% ⎟ ⎠ 52% 1 Simulation of a 5- year scenario for 3 asset classes: realized vs fundamental correlations: 𝜆 = 0.5. Zero fundamental correlation, significant realized correlation: effect entirely due to price impact of trading. 15 Proposition 1 (Continuous-time limit). Consider the above model with an “aggregate” fund with allocation 𝑋 = (𝑥𝑖 , 𝑖 = 1..𝑛). As the time step ℎ → 0, the Markov chain (𝑆 ℎ , Φℎ ) converges weakly to a diffusion limit (𝑆 ℎ , Φℎ ) ⇒ (𝑆, Φ) where ) 𝑥𝑖 𝑉𝑡 (( ′ 𝑑𝑆𝑖 (𝑡) = 𝜇𝑖 𝑑𝑡 + 𝑋 𝜇 − 𝜇𝑖 𝑑𝑡 𝑆𝑖 (𝑡) 𝜆𝑖 𝑆𝑖 (𝑡) ) 𝑥𝑖 𝑉𝑡 ( ′ ( 𝑋 𝜇 − 𝜇𝑖 ](𝐴𝑑𝑊 )𝑖 + 𝑋 ′ 𝐴𝑑𝑊𝑡 +[1 − 𝜆𝑖 𝑆𝑖 (𝑡) 𝑛 ∑ 𝑉𝑡 𝑋𝑖 with 𝑑𝑉𝑡 = 𝑉𝑡 𝑑𝑆𝑖 (𝑡) 𝜙𝑖 (𝑡) = 𝑥𝑖 𝑆𝑖 (𝑡) 𝑆 (𝑡) 𝑖=1 𝑖 where 𝐴 = Σ1/2 is a positive square root of the (fundamental) covariance matrix and 𝑉𝑡 is the value of the aggregate fund portfolio. 20 Rachats: mélange de GLM et corrélations (X. Milhaud) Rachats: mélange de GLM et corrélations (X. Milhaud) Mesures de corrélation: 1 piège révélateur 1421 1680 1736 2639 2674 4046 5306 6524 9499 82250 1064 1253 693 2639 1883 5929 2072 4109 5166 59850 Corrélation entre sinistres (journaliers) de 2 branches (4500 points) Avec la dernière ligne : corrél. égale à 99% Sans la dernière ligne : corrél. égale à 79% Tau de Kendall plus robuste mais corrél. beaucoup plus faible ( 10% ) Prise en compte de corrélation des extrêmes… Some explicit formulas for ruin probabilities in models with dependence among risks Stéphane Loisel ISFA, Université Lyon 1 Seminario CMM, Abril 2011 Con H. Albrecher y C. Constantinescu http://dx.doi.org/10.1016/j.insmatheco.2010.11.007 Mixing → Pareto distribution Given a r.v. X ∼ Exp(Θ) distributed, with Θ ∼ Γ(α, β) distributed with density fΘ (θ) = β α α−1 −βθ θ e , Γ(α) θ > 0, the resulting mixed survival function of X is given by −α Z ∞ x −θx 1 − FX (x) = e fΘ (θ)dθ = 1 + , x ≥0 β 0 Mixing → Weibull distribution If we mix an exponential with a stable distribution of order 1/2, we obtain a Weibull distribution with fixed shape parameter p = 1/2. Namely, if X ∼ Exp(Θ), with α 2 e −α /4θ , fΘ (θ) = √ 3 2 πθ then the resulting mixed distribution for X is 1 − FX (x) = exp{−αx 1/2 }, x ≥ 0. Classical ruin model R(t) = u + ct − N(t) X Xk , k=1 • u ≥ 0: initial surplus • (N(t))t≥0 claim counting process (Poisson or renewal process) • Xk iid random variables: claim amounts, E {X } < ∞ • c > 0: constant premium intensity • ct > E {N(t)}E {X }: net profit condition Ruin Time of ruin Tu = inf (R(t) < 0 | R(0) = u) t≥0 Probability of ruin ψ(u) = P (Tu < ∞) Classical result (Cramer, 1930) for compound Poisson model with X ∼ Exp(θ) claim amounts ψ(u) = min nλ o λ e −(θ− c )u , 1 , θc u ≥ 0. Mixing idea Denote by ψθ (u) = P (Tu < ∞ | Θ = θ) Then, the ruin probability is given by Z ∞ ψθ (u)dFΘ (θ). ψ(u) = 0 Claims: dependent Pareto • Compound Poisson risk model ( τ ∼ Exp(λ)) • Claims X ∼ Exp(Θ), where Θ ∼ Γ(α, β) Ruin probability is Z λ/c β α α−1 −βθ Ψ(u) = 1· θ e dθ Γ(α) Z0 ∞ λ −θu λ u β α α−1 −βθ + e ec · θ e dθ θc {z } Γ(α) λ/c | | {z } Ψθ (u) fΘ (θ), Θ∼Γ(α,β) Claims: dependent Pareto Ψ(u) = 1 − + Γ(α, βθ0 ) Γ(α) Γ(α − 1, (β + u)θ0 ) β (βθ0 )α−1 e −βθ0 (u + β)−1 | {z } ((β + u)θ0 )α−2 e −(β+u)θ0 Γ(α) {z } →u→∞ 0 | →u→∞ 1 One can see that the probability of ruin decays to a constant lim Ψ(u) = 1 − u→∞ Γ(α, βλ ) c >0 Γ(α) as fast as u −1 !! Compared to the independent case... Claims: dependent Pareto At u = 0, Γ(α, βλ ) βλ Γ(α − 1, βλ ) c c Ψ(0) = 1 − + , Γ(α) c Γ(α) where Z Γ(α, x) = ∞ w α−1 e −w dw x is the incomplete Gamma function. Mixing dependence structure For X ∼ Exp(Θ), with Θ ∼ FΘ , consider the classical compound Poisson risk model with exponential claim sizes that fulfill, for each n, P (X1 > x1 , . . . , Xn > xn | Θ = θ) = n Y e −θxk . k=1 That is, given Θ = θ, the Xk (k ≥ 1) are conditionally independent and distributed as Exp(θ). (1) Net profit condition Since for θ ≤ θ0 = λ/c the net profit condition is violated and consequently ψθ (u) = 1 for all u ≥ 0, this can be rewritten as Z ∞ Z ∞ ψ(u) = ψθ (u)dFΘ (θ) = FΘ (θ0 ) + ψθ (u)dFΘ (θ). 0 θ0 An immediate consequence is that in this dependence model lim ψ(u) = FΘ (θ0 ), u→∞ which is positive whenever the random variable Θ has probability mass at or below θ0 = λ/c (probability of net profit condition not being fulfilled). Claims: dependent Weibull For the classical Cramér Lundberg risk model with claims X ∼ Exp(Θ), where Θ has a stable distribution of order 1/2, α 2 e −α /4θ , fΘ (θ) = √ 2 πθ3 the probability of ruin is Z θ0 α 2 1· √ e −α /4θ dθ 3 Z0 ∞ 2 πθ λ −θu λ u α 2 e ec · √ e −α /4θ dθ + 3 θc {z } 2 πθ θ0 | | {z } Ψ(u) = Ψθ (u) fΘ (θ) Here the net profit condition holds for any θ > θ0 = λc . Claims :dependent Weibull Ψ(u) = + − Erfc α √ 2 θ0 √ p √ √ 1 α θ0 u −α u √ √ π 1 + α u + (1 + α u)Erf − uθ e {e 0 πα2 2 θ0 √ √ p √ √ α 2α −θ u− α2 √ + uθ0 e α u π 1 − α u + (−1 + α u)Erf − √ e 0 4θ0 } 2 θ0 θ0 θ0 √ Since Erf(x) = 1 − Γ 1 2 2, x √ π equivalent to Γ 1 2 ,x 2 = √ π(1 − Erf(x)), one can write the result in terms of incomplete Gamma functions. Interarrival times: dependent Pareto If one considers the classical Cramér Lundberg risk model with exponential claims, where the parameter Λ of the exponential inter-arrival time is a r v. Λ ∼ Γ(α, β) one obtains Pareto inter-arrival times and the probability of ruin can be calculated explicitly as well. Similarly as before, for λ0 = θc, one can write Z λ0 Ψ(u) = 0 λ −θu λ u β α α−1 −βλ e ec λ e dλ |θc {z } |Γ(α) {z } Ψλ (u) Z ∞ α 1· + λ0 fΛ (λ), Λ∼Γ(α,β) β λα−1 e −βλ dλ Γ(α) Interarrival times: dependent Pareto u Γ(α + 1) − Γ(α + 1, cβθ − uθ) Ψ(u) = e −uθ β α (β − )−α−1 c cθΓ(α) Γ(α, βcθ) + Γ(α) Thus, the probability of ruin decays again as fast as u −1 to lim Ψ(u) = u→∞ Γ(α, βcθ) Γ(α) the probability of net profit condition not being fulfilled. At u = 0 Ψ(0) = Γ(α + 1) − Γ(α + 1, cβθ) Γ(α, βcθ) + βcθΓ(α) Γ(α) Proposition The dependence model characterized by P (X1 > x1 , . . . , Xn > xn | Θ = θ) = n Y e −θxk k=1 can equivalently be described by having marginal claim sizes X1 , X2 , . . . that are completely monotone, with a dependence structure due to anArchimedean survival copula with −1 generator ϕ = FeΘ for each subset (Xj , . . . , Xjn ) (for 1 j1 , . . . , jn pairwise different), where FeΘ denotes the Laplace-Stieltjes transform of FΘ . Archimedean generator −1 ϕ(t) = FeΘ (t) As the inverse of a Laplace-Stieltjes transform of a cdf, • ϕ : [0, 1] → [0, ∞] is a continuous strictly decreasing function • ϕ(0) = ∞ and ϕ(1) = 0 • ϕ−1 is completely monotone Thus, the Archimedean copula is well-defined for all n (see e.g. Nelsen, 2006). Message: X is completely monotone. Dubey 1977 For N(t) ∼ Poisson(Λ) Z t λ̂(s)ds − R(t) = c 0 N(t) X Yj , t≥0 j=0 Estimates for λ • λ̂(t) = E {Λ | N(t)} → exact form for the ruin probability • λ̂(t) = • λ̂(t) = N(t) → moments of ruin t a+N(t) → approximation b+t of the probability of ruin, considering λ̂ to be the “credibility”estimate. Note: This model was theoretical basis for the Bonus-Malus system for the Swiss obligatory car insurance. Thank you for your attention! [email protected] Proof The joint distribution of the tail of X1 , . . . , Xn is Z P (X1 > x1 , . . . , Xn > xn ) ∞ P (X1 > x1 , . . . , Xn > xn | Θ = θ) dFΘ (θ) = 0 Z = ∞ e −θ(x1 +···+xn ) dFΘ (θ) 0 = FeΘ (x1 + · · · + xn ). At the same time, the representation with survival copula Ĉ is given by P (X1 > x1 , . . . , Xn > xn ) = Ĉ (F X (x1 ), . . . , F X (xn )), where F X (xi ) = 1 − FX (xi ) is the tail of the marginal claim size Xi (note that the Xi ’s are all identically distributed). If the survival copula is Archimedean with generator ϕ, then it admits the representation Ĉ (F X (x1 ), . . . , F (xn )) = ϕ−1 (ϕ(F X (x1 )) + · · · + ϕ(F X (xn ))), which due to Z F X (xi ) = ∞ e −θxi dFΘ (θ) = FeΘ (xi ), i = 1, . . . , n, (2) 0 exactly matches with (2) when the generator is chosen to be −1 ϕ(t) = FeΘ (t).