GARCH models, tail indexes and error distributions: An empirical investigation

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: IES Working Paper ; No. 9/2015

Classification and Topics

• Classification: Wirtschaft ; Statistical Simulation Methods: General ; Financial Econometrics ; Financial Forecasting and Simulation
• Subject: GARCH ; extreme events ; S&P 500 study ; tail index

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Šopov, Boril ; Horváth, Roman
• Event: Veröffentlichung
• (who): Charles University in Prague, Institute of Economic Studies (IES)
• (where): Prague
• (when): 2015

Further information

• Handle: http://hdl.handle.net/10419/120429
• Last update: 10.03.2025, 11:43 AM CET