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VOLATILITY MODELING OF THE JSE ALL SHARE INDEX AND RISK ESTIMATION USING THE BAYESIAN AND FREQUENTIST APPROACHES

VOLATILITY MODELING OF THE JSE ALL SHARE INDEX AND RISK ESTIMATION USING THE BAYESIAN AND... This paper focuses on volatility modeling of the Johannesburg Stock Exchange (JSE) all share index and risk estimation using the Bayesian and frequentist approaches. A Bayesian Autoregressive Moving Average-Generalized Autoregressive Conditional Heteroskedasticity (BARMA-GARCH-t) modeling of the All Share Index (ALSI) at the Johannesburg Stock Exchange (JSE) under the assumption of Student-t innovations is presented. The ALSI data is for the years 2002 to 2013. Uncertainty about the true values of the GARCH parameters is incorporated into the analysis through a non-informative joint prior distribution. A comparative analysis is done with a standard GARCH model with Student-t innovations (ARMAGARCH- t) using the maximum likelihood (ML) method. Empirical results from this study show that BARMA-GARCH-t model captures well both the conditional and unconditional volatilities of the ALSI share index at the JSE. The BARMAGARCH- t model provides a better fit to the data compared to the bench mark model which is the ARMA-GARCH-t model. The results are important to stock brokers, risk and investment managers. JEL codes: C4; C53; G1 Keywords: Bayes; GARCH; portfolio management; Student-t distribution; risk management http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Economics, Management, and Financial Markets Addleton Academic Publishers

VOLATILITY MODELING OF THE JSE ALL SHARE INDEX AND RISK ESTIMATION USING THE BAYESIAN AND FREQUENTIST APPROACHES

CASTON SIGAUKE
Economics, Management, and Financial Markets , Volume 11 (4): 16 – Jan 1, 2016
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Publisher
Addleton Academic Publishers
Copyright
© 2009 Addleton Academic Publishers
ISSN
1842-3191
eISSN
1938-212X
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Abstract

This paper focuses on volatility modeling of the Johannesburg Stock Exchange (JSE) all share index and risk estimation using the Bayesian and frequentist approaches. A Bayesian Autoregressive Moving Average-Generalized Autoregressive Conditional Heteroskedasticity (BARMA-GARCH-t) modeling of the All Share Index (ALSI) at the Johannesburg Stock Exchange (JSE) under the assumption of Student-t innovations is presented. The ALSI data is for the years 2002 to 2013. Uncertainty about the true values of the GARCH parameters is incorporated into the analysis through a non-informative joint prior distribution. A comparative analysis is done with a standard GARCH model with Student-t innovations (ARMAGARCH- t) using the maximum likelihood (ML) method. Empirical results from this study show that BARMA-GARCH-t model captures well both the conditional and unconditional volatilities of the ALSI share index at the JSE. The BARMAGARCH- t model provides a better fit to the data compared to the bench mark model which is the ARMA-GARCH-t model. The results are important to stock brokers, risk and investment managers. JEL codes: C4; C53; G1 Keywords: Bayes; GARCH; portfolio management; Student-t distribution; risk management

Journal

Economics, Management, and Financial Markets – Addleton Academic Publishers

Published: Jan 1, 2016

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