A comprehensive evaluation of value-at-risk models and a comparison of their performance in emerging markets

This paper aims to evaluate the performance of different value-at-risk (VaR) calculation methods, allowing us to identify models that are valid for use in emerging markets. We apply several widely used methods for calculating VaR, including both parametric and nonparametric methods. We consider different confidence levels for the VaR as well as different sample sizes. To test our models’ validity, we use both unconditional and conditional coverage backtests. In addition, we use a ranking method (which entails a backtesting approach based on the regulatory loss function) to appropriately compare the VaR calculation methods. Obtained from data for three different indexes (namely, Iranian, Turkish and Russian), our backtesting results indicate that parametric models from the generalized autoregressive conditional heteroscedasticity family, with asymmetric effects and fat tails (associated with their use of a t distribution), display the best performance. That is, the best-performing models under emerging market conditions are those that satisfy three important criteria simultaneously. First, they account for the time-varying variance. Second, they capture the asymmetric nature of shocks. Third, they are able to deal with fat tails in the distribution. These can also be regarded as the main features of emerging markets.