GARCH Models, Tail Indexes and Error Distributions: An Empirical Investigation

Roman Horvath and Boril Sopov

No 2015/09, Working Papers IES from Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies

Abstract: We perform a large simulation study to examine the extent to which various generalized autoregressive conditional heteroskedasticity (GARCH) models capture extreme events in stock market returns. We estimate Hill's tail indexes for individual S&P 500 stock market returns ranging from 1995{2014. and compare these to the tail indexes produced by simulating GARCH models. Our results suggest that actual and simulated values differ greatly for GARCH models with normal conditional distributions, which underestimate the tail risk. By contrast, the GARCH models with Student's t conditional distributions capture the tail shape more accurately, with GARCH and GJR-GARCH being the top performers.

Keywords: GARCH; extreme events; S&P 500 study; tail index (search for similar items in EconPapers)
JEL-codes: C15 C58 G17 (search for similar items in EconPapers)
Pages: 35 pages
Date: 2015-05, Revised 2015-05
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-rmg
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Journal Article: GARCH models, tail indexes and error distributions: An empirical investigation (2016)
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