Applying three VaR (value at risk) approaches in measuring market risk of stock portfolio: the case study of VN-30 stocks basket in HOSE

Abstract

This study examines and applies the three statistical value at risk models including variance-covariance, historical simulation, and Monte Carlo simulation in measuring market risk of VN-30 portfolio of Ho Chi Minh stock exchange (HOSE) in Vietnam stock market and some back-testing techniques in assessing the validity of the VaR performance in the timeframe of January 30, 2012–February 26, 2016. The models are constructed from two volatility methods of stock price: SMA and EWMA throughout the five chosen confi-dence level: 90%, 93%, 95%, 97.5%, and 99%. The findings of the study show that the differences among the results of three models are not significant. Additionally, three VaR (Value at Risk) models have generally the similar accepted range assessed in both types of back-tests at all confidence levels considered and at the 97.5% con-fidence level. They can work best to achieve the highest validity level of results in satisfying both conditional and unconditional back-tests. The Monte Carlo Simulation (MCS) has been considered the most appropriate method to apply in the context of VN-30 port-folio due to its flexibility in distribution simulation. Recommenda-tions for further research and investigations are provided according-ly.