Risk Estimation of Karachi Stock Exchange via Conditional Autoregressive Value-at-Risk by Regression Quantiles
Abstract
This paper examines the market risk of Karachi Stock Exchange (KSE) by employing the Conditional Autoregressive Value-at-Risk by Regression Quantiles (CAViaR) model. The CAViaR model interprets the Value-at-Risk (VaR) as the quantile of future portfolio values conditional on current information and directly compute this quantile instead of inverting the distribution of returns. An asymmetric conditional heteroscedastic specification for CAViaR is proposed and applied along with four commonly used CAViaR specifications for the one-day-ahead VaR estimation of KSE for the period 1998 – 2010. The in-sample and out-of-sample predictive performance of alternative CAViaR specifications are compared and evaluated. The proposed model that accounts for asymmetry of risk is found to produce better and reliable estimates for VaR of KSE.
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