Forecasting Value at Risk: Evidence from Emerging Economies in Asia

Abstract

In this paper, various Value-at-Risk techniques are applied to stock indices of 9 Asian emerging financial markets. The results from our selected models are then backtested by Unconditional Coverage, Independence, Joint Tests of Unconditional Coverage and Independence and Basel tests to ensure the quality of Value-at-Risk (VaR) estimates. The main conclusions are: (1) Timevarying volatility is the most important characteristic of stock returns when modelling VaR; (2) Financial data is not normally distributed, indicating that the normality assumption of VaR is not relevant; (3) Among VAR forecasting approaches, the backtesting based on in- and out-of-sample evaluations confirms its superiority in the class of GARCH models; Historical Simulation (HS), Filtered Historical Simulation (FHS), RiskMetrics and Monte Carlo were rejected because of its underestimation (for HS and RiskMetrics) or overestimation (for the FHS and Monte Carlo); (4) Models under student’s t and skew student’s t distribution are better in taking into account financial data’s characters; and (5) Forecasting VaR for futures index is harder than for stock index. Moreover, results show that there is no evidence to recommend the use of GARCH (1,1) to estimate VaR for all markets. In practice, the HS and RiskMetrics are popularly used by banks for large portfolios, despite of its serious underestimations of actual losses. These findings would be helpful for financial managers, investors and regulators dealing with stock markets in Asian emerging economies.

Value-at-Risk Analysis in Risk Measurement and Formation of Optimal Portfolio in Banking Share

“…These results also support Machfiroh's (2016) research, showing that stock returns do not follow the normal distribution based on the skewness test. The results of this study agree with Thanh et al (2018), proving that VaR based on the assumption of normality is irrelevant. The amount of loss multiplies the VaR value by the investment amount.…”

“…VaR can be estimated through one of the primary methods, called historical (nonparametric) simulation. The non-parametric approach based on historical data does not require the assumption of normality of the data (Thanh et al, 2018). Historical simulations are based on the belief that the distribution of possible changes in market factors over the next period is identical to the distribution observed in the previous period (Christoffersen, 2012).…”

“…Value at Risk (VaR) measures the maximum loss that may occur in the following 1-day, 1-week ahead, and so on according to the desired period. Thanh et al (2018) explained that VaR could be through three primary methods, the variance-covariance method (parametric), the Monte Carlo simulation method (semiparametric), and the historical simulation method (non-parametric). The parametric approach is based on the assumption that returns are normally distributed.…”

“…Hull (2015) explained that the standard distribution assumption does not describe the distribution of losses because it has a fat tail (abnormality) nature. Thanh et al (2018) showed that the return on the stock market is not normally distributed. The historical simulation approach helps to solve the data normality problem.…”