Value‐at‐Risk in Emerging Equity Markets: Comparative Evidence for Symmetric, Asymmetric, and Long‐Memory GARCH Models

This paper extends research concerned with the evaluation of alternative volatility forecasting methods under value at risk (VaR) modeling in the context of the Basle Committee adequacy criteria by broadening the class of generalized autoregressive conditional heteroscedasticity models, to include both asymmetric models and long memory models, in addition to the statistical methods commonly used in financial institutions. In the analysis of daily index data for eight emerging stock markets in the Asia – Pacific region, in addition to US and UK benchmark comparators, we find both asymmetric and long memory features to be important considerations in providing improved VaR estimates that minimize occasions when the minimum capital requirement identified by the VaR methodology would have fallen short of actual trading losses. More generally, our results illustrate the importance of adopting the stringent probability level stipulated in the regulatory framework, and of using fully out‐of‐sample forecast evaluation methods for the identification of forecasting models that mitigate the likelihood of inappropriately small VaRs and consequent regulatory intervention.