130 Afro-Asian J. Finance and Accounting, Vol. 7, No. 2, 2017 Copyright © 2017 Inderscience Enterprises Ltd. Robust value-at-risk forecasting of Karachi Stock Exchange Farhat Iqbal Department of Statistics, University of Balochistan, Quetta, Pakistan Email: farhatiqb@gmail.com 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. Keywords: generalised autoregressive conditional heteroscedastic; GARCH; M-estimator; volatility; value-at-risk; VaR. Reference to this paper should be made as follows: Iqbal, F. (2017) ‘Robust value-at-risk forecasting of Karachi Stock Exchange’, Afro-Asian J. Finance and Accounting, Vol. 7, No. 2, pp.130–146. Biographical notes: Farhat Iqbal is an Associate Professor of Statistics at University of Balochistan, Quetta-Pakistan. He holds a Doctorate degree in Statistics from University of Lancaster, UK and an MSc in Computer Science. He has published articles in statistics, economics and finance journals. His areas of interest include volatility modelling, robust estimation and bootstrapping. 1 Introduction A commonly used statistic for measuring potential risk in financial market is value-at-risk (VaR). Financial institutions and banks including portfolio managers use VaR for financial risk management. It is the quantile of the loss that can occur within a given portfolio during a specified time period. The stability of the institutions and sustainability of economic growth largely depend on the comprehensive risk management practices by