The choice of the distribution of asset returns: How extreme value theory can help?

Abstract One of the issues of risk management is the choice of the distribution of asset returns. Academics and practitioners have assumed for a long time (for more than three decades) that the distribution of asset returns is a Gaussian distribution. Such an assumption has been used in many fields of finance: building optimal portfolio, pricing and hedging derivatives and managing risks. However, real financial data tend to exhibit extreme price changes such as stock market crashes that seem incompatible with the assumption of normality. This article shows how extreme value theory can be useful to know more precisely the characteristics of the distribution of asset returns and finally help to chose a better model by focusing on the tails of the distribution. An empirical analysis using equity data of the US market is provided to illustrate this point.

[1]  F. Longin The Asymptotic Distribution of Extreme Stock Market Returns , 1996 .

[2]  P. Phillips,et al.  Testing the covariance stationarity of heavy-tailed time series: An overview of the theory with applications to several financial datasets , 1994 .

[3]  J. Hüsler Extremes and related properties of random sequences and processes , 1984 .

[4]  J. Corcoran Modelling Extremal Events for Insurance and Finance , 2002 .

[5]  W. Sharpe CAPITAL ASSET PRICES: A THEORY OF MARKET EQUILIBRIUM UNDER CONDITIONS OF RISK* , 1964 .

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

[7]  F. Longin,et al.  From value at risk to stress testing : The extreme value approach Franc ß ois , 2000 .

[8]  Sidney I. Resnick,et al.  Extremal behaviour of solutions to a stochastic difference equation with applications to arch processes , 1989 .

[9]  Dennis W. Jansen,et al.  On the Frequency of Large Stock Returns: Putting Booms and Busts into Perspective , 1989 .

[10]  E. Fama The Behavior of Stock-Market Prices , 1965 .

[11]  S. James Press,et al.  A Compound Events Model for Security Prices , 1967 .

[12]  B. Gnedenko Sur La Distribution Limite Du Terme Maximum D'Une Serie Aleatoire , 1943 .

[13]  E. J. Gumbel,et al.  Statistics of Extremes. , 1960 .

[14]  R. Engle Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .

[15]  B. Mandelbrot The Variation of Certain Speculative Prices , 1963 .

[16]  C. B. Tilanus,et al.  Applied Economic Forecasting , 1966 .

[17]  L. Haan,et al.  Residual Life Time at Great Age , 1974 .

[18]  L. Haan,et al.  On the Estimation of the Extreme-Value Index and Large Quantile Estimation , 1989 .

[19]  R. Huisman,et al.  Tail-Index Estimates in Small Samples , 2001 .

[20]  C. Klüppelberg,et al.  Modelling Extremal Events , 1997 .

[21]  B. M. Hill,et al.  A Simple General Approach to Inference About the Tail of a Distribution , 1975 .

[22]  J. D. T. Oliveira,et al.  The Asymptotic Theory of Extreme Order Statistics , 1979 .

[23]  PAUL EMBRECHTS,et al.  Modelling of extremal events in insurance and finance , 1994, Math. Methods Oper. Res..

[24]  J. Pickands Statistical Inference Using Extreme Order Statistics , 1975 .

[25]  F. Longin,et al.  Extreme Correlation of International Equity Markets , 2000 .

[26]  L. Haan,et al.  Using a Bootstrap Method to Choose the Sample Fraction in Tail Index Estimation , 2000 .

[27]  S. Berman Limit Theorems for the Maximum Term in Stationary Sequences , 1964 .

[28]  R. C. Merton,et al.  Theory of Rational Option Pricing , 2015, World Scientific Reference on Contingent Claims Analysis in Corporate Finance.

[29]  F. Black,et al.  The Pricing of Options and Corporate Liabilities , 1973, Journal of Political Economy.