Forecasting Swedish Stock Market Volatility and Value-at-Risk: A Comparison of EWMA and GARCH Models

Abstract: In this study we compare different volatility models on their ability to forecast one day ahead volatility and value-at-risk (VaR). We compare five different GARCH specifications: GARCH, IGARCH, GJR-GARCH, EGARCH and APARCH, as well as EWMA, each paired with six different conditional distributions. These models are used to forecast volatility and VaR one day ahead using daily return data from the Swedish stock market index OMXS30. The forecasts are then compared using the model confidence set procedure of Peter Reinhard Hansen, Asger Lunde, and James M Nason (2011). “The model confidence set.” In: Econometrica 79.2, pp. 453–497. We find the APARCH models best for forecasting volatility, while for forecasting VaR the best models are either APARCH, GJR-GARCH or EGARCH—depending on which level of VaR we use—paired with conditional distributions that take skewness and excess kurtosis into account. EWMA, GARCH and IGARCH specifications cannot be recommended either for forecasting volatility or for forecasting VaR.