Value at risk for a mixture of normal distributions: the use of quasi- Bayesian estimation techniques

This article proposes a methodology for measuring value at risk for fat-tailed asset return distributions. Simulation-based results indicate that this approach provides better estimates of risk than one based on the assumption that asset returns are normally distributed.

[1]  Gregory P. Hopper.  Value at risk: a new methodology for measuring portfolio risk , 1996 .

[2]  R. C. Merton,et al.  Option pricing when underlying stock returns are discontinuous , 1976 .

[3]  A. F. Smith,et al.  Statistical analysis of finite mixture distributions , 1986 .

[4]  Stanley J. Kon.  Models of Stock Returns—A Comparison , 1984 .

[5]  C. Robert.  Mixtures of Distributions: Inference and Estimation , 1996 .

[6]  E. L. Lehmann,et al.  Theory of point estimation , 1950 .

[7]  James D. Hamilton,et al.  Long Swings in the Dollar: Are They in the Data and Do Markets Know It? The American Economic Review , 1990 .

[8]  Dongcheol Kim,et al.  Alternative Models for the Conditional Heteroscedasticity of Stock Returns , 1994 .

[9]  James D. Hamilton.  A Quasi-Bayesian Approach to Estimating Parameters for Mixtures of Normal Distributions , 1991 .

[10]  Tanya Styblo Beder,et al.  VAR: Seductive but Dangerous , 1995 .

[11]  Philippe Jorion.  Predicting Volatility in the Foreign Exchange Market , 1995 .

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

[13]  D. Hendricks,et al.  Evaluation of Value-at-Risk Models Using Historical Data , 1996 .

[14]  L. Glosten,et al.  On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks , 1993 .

[15]  G. Casella,et al.  Explaining the Gibbs Sampler , 1992 .

[16]  David Hsieh.  Nonlinear Dynamics in Financial Markets: Evidence and Implications , 1995 .