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This paper is concerned with the estimation, forecasting and evaluation of Value-at-Risk (VaR) of Karachi Stock Exchange before and after the global financial crisis of 2008 using Bayesian method. The generalized autoregressive conditional heteroscedastic (GARCH) models under the assumption of normal and heavy-tailed errors are used to forecast one-day-ahead risk estimates. Various measures and backtesting methods are employed to evaluate VaR forecasts. The observed number of VaR violations using Bayesian method is found close to the expected number of violations. The losses are also found smaller than the competing Maximum Likelihood method. The results showed that the Bayesian method produce accurate and reliable VaR forecasts and can be preferred over other methods. 


GARCH Volatility Value-at-Risk MCMC

Article Details

Author Biography

Farhat Iqbal, University of Balochistan, Quetta-Pakistan

Assistant Professor,

Department of Statistics

University of Balochistan, 


How to Cite
Iqbal, F. (2016). Risk Forecasting of Karachi Stock Exchange: A Comparison of Classical and Bayesian GARCH Models. Pakistan Journal of Statistics and Operation Research, 12(3), 453-465.