Publication detail

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

Author(s): prof. Roman Horváth Ph.D.,
Type: IES Working Papers
Year: 2015
Number: 9
Published in: IES Working Papers 9/2015
Publishing place: Prague
Keywords: GARCH, extreme events, S&P 500 study, tail index
JEL codes: C15, C58, G17
Suggested Citation: Sopov B., Horvath R. (2015). “GARCH Models, Tail Indexes and Error Distributions: An Empirical Investigation” IES Working Paper 9/2015. IES FSV. Charles University.
Grants: DYME – Dynamic Models in Economics
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.
Downloadable: wp_2015_9_sopov_horvath




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