TY - JOUR

T1 - Regular variation and extremal dependence of GARCH residuals with application to market risk measures

AU - Laurini, Fabrizio

AU - Tawn, Jonathan A.

PY - 2009/1/31

Y1 - 2009/1/31

N2 - Stock returns exhibit heavy tails and volatility clustering. These features, motivating the use of GARCH models, make it difficult to predict times and sizes of losses that might occur. Estimation of losses, like the Value-at-Risk, often assume that returns, normalized by the level of volatility, are Gaussian. Often under ARMA-GARCH modeling, such scaled returns are heavy tailed and show extremal dependence, whose strength reduces when increasing extreme levels. We model heavy tails of scaled returns with generalized Pareto distributions, while extremal dependence can be reduced by declustering data.

AB - Stock returns exhibit heavy tails and volatility clustering. These features, motivating the use of GARCH models, make it difficult to predict times and sizes of losses that might occur. Estimation of losses, like the Value-at-Risk, often assume that returns, normalized by the level of volatility, are Gaussian. Often under ARMA-GARCH modeling, such scaled returns are heavy tailed and show extremal dependence, whose strength reduces when increasing extreme levels. We model heavy tails of scaled returns with generalized Pareto distributions, while extremal dependence can be reduced by declustering data.

KW - Declustering

KW - Expected shortfalls

KW - Extremal dependence

KW - Generalized Pareto distribution

KW - Regular variation

KW - Value-at-Risk

U2 - 10.1080/07474930802387985

DO - 10.1080/07474930802387985

M3 - Journal article

AN - SCOPUS:58149137006

VL - 28

SP - 146

EP - 169

JO - Econometric Reviews

JF - Econometric Reviews

SN - 0747-4938

IS - 1-3

ER -