Article2008

Estimating stock market volatility using asymmetric GARCH models

274 citations0/0 files storedApplied Financial Economics

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Financial Risk and Volatility Modeling Financial Markets and Investment Strategies Stochastic processes and financial applications

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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References

1986Generalized autoregressive conditional heteroskedasticity 1982Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation 1991Conditional Heteroskedasticity in Asset Returns: A New Approach 1993On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks 1963The Variation of Certain Speculative Prices 1997The variation of certain speculative prices 1998Answering the Skeptics: Yes, Standard Volatility Models do Provide Accurate Forecasts 1993A long memory property of stock market returns and a new model

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