Forecasting Volatility on Swedish Stock Returns : A study comparing the performance of different volatility forecasting models

Sammanfattning: This study aims to find the model which generates the best volatility forecasts of single stock returns on the Swedish Market. The models are estimated using an in-sample dataset of daily observations from 2010.01.01 to 2018.12.31, they produce out-of-sample forecasts during the period 2019.01.01 to 2019.03.31 which are evaluated against a proxy for daily realized volatility using 4 loss functions. The forecasts are also evaluated against daily implied volatilities. The models considered in this study are ARCH(1), GARCH(1,1), EGARCH(1,1) and Implied Volatility measures. The study finds that, in the evaluation against daily realized volatility, the EGARCH(1,1) generates the best forecasts, which is consistent with literature. However, results indicate that the naïve ARCH(1) outperforms the GARCH(1,1) which is not consistent with previous research. In the evaluation against implied volatilities, the ARCH(1) specification performed the best. Although, the differences in the losses of the different ARCH-family models were often very small.