Estimating stock market volatility using asymmetric GARCH models

Dima Alberg, Haim Shalit, Rami Yosef

Research output: Contribution to journalArticlepeer-review

100 Scopus citations

Abstract

A comprehensive empirical analysis of the mean return and conditional variance of Tel Aviv Stock Exchange (TASE) indices is performed using various GARCH models. The prediction performance of these conditional changing variance models is compared to newer asymmetric GJR and APARCH models. We also quantify the day-of-the-week effect and the leverage effect and test for asymmetric volatility. Our results show that the asymmetric GARCH model with fat-tailed densities improves overall estimation for measuring conditional variance. The EGARCH model using a skewed Student-t distribution is the most successful for forecasting TASE indices.

Original languageEnglish
Pages (from-to)1201-1208
Number of pages8
JournalApplied Financial Economics
Volume18
Issue number15
DOIs
StatePublished - 1 Aug 2008

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