Quantitative risk analysis: an approach for Vietnam stock market

Abstract

Value at Risk (VaR) is widely used in risk measurement. It is dened as the worst expected loss of a portfolio under a given time horizon at a given condence level. The aim of the thesis is to evaluate performance of 16 VaR models in forecasting one - day ahead VaR for daily return of VNINDEX and a group 8 banking stock indexes including ACB, BVH, CTG, EIB, MBB, SHB, STB, VCB to nd out the most appropriate model for each stock index. Three unconditional volatility models including historical, normal and Students - t as well as EWMA and two volatility models including GARCH, GJR - GARCH with three return distributions normal, Students - t and skewed Students - t and associated Extreme Value Theory (EVT) models are performed at 5%, 2.5% and 1% of signicance level. Violation ration, Kupiecs unconditional coverage test, independence test and Christo⁄ersen conditional coverage test are used to backtested performance of all models. Besides statistical analysis, graphical analysis is also incorporated. Backtesting indicates that there is no best model for all cases because of characteristic di⁄erence from particular stock index. Implication of this thesis is that a suitable VaR forecasting model is only chosen after backtesting frequently performance of various models in order to ensure that most relevant and most accurate models are suited for current nancial market situation.