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2006•Unpublished venueRequires access

Modeling Volatility of the KLCI Daily Returns

Mohamed I.B.

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Abstract

Volatility is a central concept in financial engineering. It may be simply defined as the standard deviation of return values. A frequent modeling assumption is that volatility is constant. Unfortunately in many financial time series volatility appears to be anything but constant. This paper reports the results of an effort in modeling stock market volatility as a Generalized Autoregressive Conditional Heteroscedastic (GARCH) process. KeywordsGARCH, Stationarity, Heteroscedasticity, Volatility Clustering, Simulation.

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What this paper is about

Volatility is a central concept in financial engineering. It may be simply defined as the standard deviation of return values. A frequent modeling assumption is that volatility is constant. Unfortunately in many financial time series volatility appears to be anything but constant. This paper reports the results of an effort in modeling stock market volatility as a Generalized Autoregressive Conditional Heteroscedastic (GARCH) process. KeywordsGARCH, Stationarity, Heteroscedasticity, Volatility Clustering, Simulation.

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Available abstract

Volatility is a central concept in financial engineering. It may be simply defined as the standard deviation of return values. A frequent modeling assumption is that volatility is constant. Unfortunately in many financial time series volatility appears to be anything but constant. This paper reports the results of an effort in modeling stock market volatility as a Generalized Autoregressive Conditional Heteroscedastic (GARCH) process. KeywordsGARCH, Stationarity, Heteroscedasticity, Volatility Clustering, Simulation.

Key concepts: Volatility clustering, Volatility (finance), Econometrics, Heteroscedasticity, Forward volatility, Financial models with long-tailed distributions and volatility clustering, Autoregressive conditional heteroskedasticity, Economics

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