Evaluation of GARCH-based models in value-at-risk estimation: Evidence from emerging equity markets

This paper evaluates the forecasting performance of a range of volatility models in Value-at-Risk estimation in the context of the Basle regulatory framework using stock index return data from a selection of emerging markets. It extends the current research in these economies by including a range of GARCH models and their long memory extension, in addition to some standard statistical methods often used by financial institutions. The results suggest that models with long memory or asymmetric effects or both are important considerations in providing improved VaR estimates that minimise occasions when the minimum capital requirement identified by the VaR process would have fallen short of actual trading losses. In addition, the results highlight the relevance Basel regulatory framework, and of using out-of-sample forecast evaluation methods for the identification of forecasting models that provide accurate VaR estimates.