Volatility Forecasting Performance : An evaluation of GARCH-class models

Volatility is considered among the most vital concepts of the financial market and is frequently used as a rough measure of the total risk of financial assets. Volatility is however not directly observable in practice; it must be estimated. The procedure in estimating and modeling volatility can be performed in numerous ways. However, the GARCH-class models have historically proven to be successful in these estimations while also being rather simple and practically applicable. The purpose of this study is to evaluate which of the included six GARCH models that produce the most accurate forecasts of future volatility during a “normal” trading year. Our data sample consists of 9 stock market indices and the forecast is performed on the trading year of 2019. The out-of-sample forecasts have been analyzed against the proxy of realized volatility which is based on high-frequency data and proven to be successful in previous studies. To evaluate the correlation between the forecasts and the realized volatility proxy, four different evaluation measures (MAE, MAPE, MSE and QLIKE) are executed. Findings suggest that the asymmetric GARCH-class and particularly the EGARCH volatility model(s) tend to be superior