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Estimating stock market volatility using asymmetric GARCH models

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  • Dima Alberg
  • Haim Shalit
  • Rami Yosef

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.

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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Applied Financial Economics.

Volume (Year): 18 (2008)
Issue (Month): 15 ()
Pages: 1201-1208

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Handle: RePEc:taf:apfiec:v:18:y:2008:i:15:p:1201-1208

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Cited by:
  1. Piotr Wdowinski & Marta Malecka, 2010. "Asymmetry in Volatility: A Comparison of Developed and Transition Stock Markets," CESifo Working Paper Series 2974, CESifo Group Munich.
  2. John Galbraith & Dongming Zhu, 2009. "Forecasting Expected Shortfall With A Generalized Asymmetric Student-T Distribution," Departmental Working Papers 2009-01, McGill University, Department of Economics.
  3. Guidi, Francesco, 2008. "Volatility and Long Term Relations in Equity Markets: Empirical Evidence from Germany, Switzerland, and the UK," MPRA Paper 11535, University Library of Munich, Germany.
  4. Shcherba, Alexandr, 2012. "Market risk valuation modeling for the European countries at the financial crisis of 2008," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 27(3), pages 20-35.
  5. Výrost, Tomáš & Baumöhl, Eduard, 2009. "Asymmetric GARCH and the financial crisis: a preliminary study," MPRA Paper 27939, University Library of Munich, Germany.
  6. Zhu, Dongming & Galbraith, John W., 2010. "A generalized asymmetric Student-t distribution with application to financial econometrics," Journal of Econometrics, Elsevier, vol. 157(2), pages 297-305, August.
  7. Zhu, Dongming & Galbraith, John W., 2011. "Modeling and forecasting expected shortfall with the generalized asymmetric Student-t and asymmetric exponential power distributions," Journal of Empirical Finance, Elsevier, vol. 18(4), pages 765-778, September.

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