Abstract
A class of robust M-estimators for generalised autoregressive conditional heteroscedastic (GARCH) type models are used for the prediction of value-at-risk (VaR) of Karachi Stock Exchange (KSE). To better understand the impact of global financial crisis on KSE, the daily stock return data is divided into three sub-periods: the pre-crisis period (3 January 2005 to 31 December 2007), the crisis period (2 January 2008 to 30 June 2009), and the post-crisis period (1 July 2009 to 31 December 2013). Symmetric and asymmetric GARCH models that capture the most common stylised facts about index returns such as volatility clustering and leverage effect are fitted to these time periods and in-sample and out-of-sample estimates of VaR are obtained. Our results show that M-estimators provide accurate and reliable estimates of VaR in low and high volatile time. Our findings also show that the asymmetric model provides better fit than the symmetric model for the KSE.
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