Value at risk in emerging markets: Empirical evidence from twelve countries

This study focuses on the relative performance of three Value-at-Risk (VaR) estimation methodologies. The daily stock market index returns of twelve different emerging markets are used for the empirical analysis. In addition to the well-known methodologies, such as the historical simulation and GARCH-based ones, the extreme value theory (EVT) is also used to estimate the daily VaR. In this paper, we focus on EVT because it studies the non-linear estimation of the tails and we expect to find many extreme events when analysing the return distributions in these twelve emer ging markets. We focus on the negative extreme events rather than on the positive ones. The daily VaR is forecasted at three different quantile levels: 90%, 97.5%, 99.9%; and competing methodologies are back-tested accordingly. The results indicate that the historical simulation and GARCH-based methodologies work better at lower quantile levels than they do at higher quantile levels, while VaR estimated using EVT is more accurate at higher quantiles. EVT provides better information about extreme events, especially when financial distress occurs in these economies.