**5. Conclusions**

We compare the forecasting performance of different GARCH-type and Stochastic Volatility models as well as non- and semi-parametric approaches in terms of the widely-used Value-at-Risk measure. We obtain results that are not consistent across markets as well as trading positions. The results imply that, for the long and short trading positions, different forecasting methods should be implemented. Adding to this inconsistency, we find that, for different ASEAN stock indices, the model performances vary, indicating that the markets volatility might be driven by different factors. The simple GARCH and the RiskMetrics framework provide insufficient forecasts in terms of coverage and clustering. With only a few exceptions, the two models fail for all forecasting horizons and for all markets. This is a clear indication that the index volatilities should not be modeled by short memory and symmetric processes. Long memory models with or without asymmetric news impact, such as the FIGARCH, APARCH, or FIAPARCH, are potent alternatives.

Given the significant skewness in the empirical returns, skewed distributions driving the volatility processes are suggested. The Historical Simulation appears to be superior over its filtered extension and provides reasonably good results for the multilevel unconditional coverage test. With Stochastic Volatility models, we improve the quality of some forecasts. In general, we obtain better results for shorter horizons. In addition, there is no clear pattern in the failure rate of the unconditional and conditional coverage tests. Interestingly, for the stochastic volatility framework, we achieve a good overall VaR coverage, which is, however, clustered for most markets across the forecasting horizons. The clustering might be caused by periods of extreme market movements paired with only a minor reaction of the volatility models.

In summary, the results show that simple volatility models do not provide VaR forecasts of practical value and that more sophisticated models, which cover different stylized facts, are needed to properly quantify financial risk on the long and short side for ASEAN stock market indices. Moreover, we conclude that, despite their regional proximity and homogeneity of the markets, the stock index volatilities of the biggest ASEAN markets are driven by different factors. This needs to be addressed in further research.

**Acknowledgments:** We are thankful to three anonymous reviewers, whose valuable suggestions helped to improve an earlier version of this paper. We thank Hermann Locarek-Junge and Duc Khuong Nguyen for support, and Stanislas Augier for his excellent student assistance during his stay at John von Neumann Institute. Moreover, we are thankful to the participants of the 2nd Vietnam Symposium in Banking and Finance in Ho Chi Minh City (2017), especially to Maximilian Adelmann for his discussion. Parts of the paper were written while Thomas Walther was a visiting researcher at the John von Neumann Institute, Ho Chi Minh City, Vietnam. Thomas Walther is grateful for the funding received from Innosuisse (Swiss Innovation Agency, Bern, Switzerland) for conducting this research.

**Author Contributions:** Nam H. Nguyen and Paul Bui Quang implemented the Stochastic Volatility models and the corresponding Value-at-Risk estimation. Tony Klein and Thomas Walther jointly implemented and analyzed the GARCH models, carried out the backtests, and wrote the article.

**Conflicts of Interest:** The authors declare no conflict of interest.
