THE EXPECTED SHORTFALL OF QUADRATIC PORTFOLIOS WITH HEAVY‐TAILED RISK FACTORS

Computable expressions are derived for the Expected Shortfall of portfolios whose value is a quadratic function of a number of risk factors, as arise from a Delta–Gamma–Theta approximation. The risk factors are assumed to follow an elliptical multivariate t distribution, reflecting the heavy‐tailed nature of asset returns. Both an exact expression and a uniform asymptotic expansion are presented. The former involves only a single rapidly convergent integral. The latter is essentially explicit, and numerical experiments suggest that its error is negligible compared to that incurred by the Delta–Gamma–Theta approximation.

[1]  C. Klüppelberg,et al.  Asymptotic behavior of tails and quantiles of quadratic forms of Gaussian vectors , 2004 .

[2]  Robert C. Blattberg,et al.  A Comparison of the Stable and Student Distributions as Statistical Models for Stock Prices: Reply , 1974 .

[3]  Philippe Artzner,et al.  Coherent Measures of Risk , 1999 .

[4]  C. Chester,et al.  An extension of the method of steepest descents , 1957, Mathematical Proceedings of the Cambridge Philosophical Society.

[5]  Jules Sadefo Kamdem Value-at-Risk and Expected Shortfall for Quadratic portfolio of securities with mixture of elliptic Distributed Risk Factors , 2003, ArXiv.

[6]  Paul Glasserman,et al.  Portfolio Value‐at‐Risk with Heavy‐Tailed Risk Factors , 2002 .

[7]  Norman Bleistein,et al.  Uniform asymptotic expansions of integrals with stationary point near algebraic singularity , 1966 .

[8]  Jun Pan,et al.  Analytical value-at-risk with jumps and credit risk , 2001, Finance Stochastics.

[9]  Nonlinear Value-At-Risk , 1999 .

[10]  D. Tasche,et al.  Expected Shortfall: a natural coherent alternative to Value at Risk , 2001, cond-mat/0105191.

[11]  E. Seneta,et al.  The Variance Gamma (V.G.) Model for Share Market Returns , 1990 .

[12]  Meng-Lan Yueh,et al.  Analytical VaR and Expected Shortfall for Quadratic Portfolios , 2010, The Journal of Derivatives.

[13]  Roderick Wong,et al.  Asymptotic approximations of integrals , 1989, Classics in applied mathematics.

[14]  S. Rice,et al.  Saddle point approximation for the distribution of the sum of independent random variables , 1980, Advances in Applied Probability.

[15]  Augustine C. M. Wong,et al.  Computation of value-at-risk for nonlinear portfolios , 2000 .

[16]  Enrique Sentana,et al.  Maximum Likelihood Estimation and Inference in Multivariate Conditionally Heteroscedastic Dynamic Regression Models With Student t Innovations , 2003 .

[17]  R. C. Geary Extension of a Theorem by Harald Cramer on the Frequency Distribution of the Quotient of Two Variables , 1944 .

[18]  H. Daniels Saddlepoint Approximations in Statistics , 1954 .

[19]  D. Tasche,et al.  On the coherence of expected shortfall , 2001, cond-mat/0104295.

[20]  H. E. Daniels,et al.  Tail Probability Approximations , 1987 .

[21]  K. Nyström Harmonic Analysis, Quadratic Forms and Asymptotic Expansions of Risk Measures , 2008 .

[22]  A. Harvey,et al.  Unobserved component time series models with Arch disturbances , 1992 .

[23]  P. Praetz,et al.  The Distribution of Share Price Changes , 1972 .

[24]  Samuel Kotz,et al.  Multivariate T-Distributions and Their Applications , 2004 .

[25]  William Fallon Calculating Value-at-Risk , 1996 .

[26]  T. Bollerslev,et al.  A CONDITIONALLY HETEROSKEDASTIC TIME SERIES MODEL FOR SPECULATIVE PRICES AND RATES OF RETURN , 1987 .