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Estimating Stock Market Volatility Using Asymmetric GARCH Models

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  • Dima Alberg

    (BGU)

  • Haim Shalit

    (BGU)

  • Rami Yosef

    (BGU)

Abstract

A comprehensive empirical analysis of the return and conditional variance of Tel Aviv Stock Exchange (TASE) indices is performed using 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 EGARCH model using a skewed Student-t distribution is the most successful in forecasting the TASE indices.

Suggested Citation

  • Dima Alberg & Haim Shalit & Rami Yosef, 2006. "Estimating Stock Market Volatility Using Asymmetric GARCH Models," Working Papers 0610, Ben-Gurion University of the Negev, Department of Economics.
    • Handle: RePEc:bgu:wpaper:0610
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    References listed on IDEAS

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    1. Neenu Chalissery & Suhaib Anagreh & Mohamed Nishad T. & Mosab I. Tabash, 2022. "Mapping the Trend, Application and Forecasting Performance of Asymmetric GARCH Models: A Review Based on Bibliometric Analysis," JRFM, MDPI, vol. 15(9), pages 1-23, September.

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