No 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.

Asian frontier markets present compelling investment opportunities for investors seeking higher returns and low correlation with traditional assets. As such, it is important for financial market participants to understand the volatility transmission mechanism across these markets in order to make better portfolio allocation decisions. This study investigates the magnitude of return and volatility spillovers from the international crude oil markets on the Asian frontier oil and gas stock markets. In particular, we construct mean return and volatility spillover models to discuss whether regional (DSE, CSE, HNX, HOSE) and global (ICE) market impacts are crucial for the determination of oil & gas stock returns in Bangladesh, Sri Lanka, and Vietnam by employing ARMA(1,1)-GARCH(1,1) model. Using daily returns from January 4, 2010 to December 31, 2019, the findings of this paper show that the Brent oil and WTI crude oil markets influence the Sri Lanka and Vietnamese oil and gas stock markets. WTI price changes, however, have a relatively minor impact on Sri Lanka companies. For Bangladesh, it is noticeable that none of the spillover effects is statically significant. The results are explained by different levels of the reform process in the energy sector as well as by the importance of oil in these markets. In general, these frontier markets, especially the Bangladesh and Sri Lanka may offer promising diversification benefits due to low correlations with developed equity markets. These results are important for economic policymakers and investors in understanding the magnitude of volatility spillover effects of the international crude oil on these markets. Investors can use this information to make better portfolio allocation decisions to reduce risks and enhance returns.