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.