Improving financial volatility nowcasts

Recently, a simple nowcasting model for volatility has been proposed by Breitung and Hafner [2016. A Simple Model for Now-Casting Volatility Series. International Journal of Forecasting 32 (4): 1247–1255. doi:10.1016/j.ijforecast.2016.04.007]. They suggest a model in which today's volatility is not only driven by past returns, but also by the current information from the same day. Empirical results demonstrate the relevance of the current squared return for volatility nowcasting. Their model obeys an ARMA representation estimable by maximum likelihood. However, their estimation approach builds on a number of simplifications and we suggest improvements. Rather than assuming normality of the innovations in the ARMA representation for highly skewed and leptokurtic log-squared returns, we take non-normality explicitly into account. Contrary to most situations regarding volatility estimation and forecasting, the distribution actually plays a crucial role in the construction of volatility nowcasts. We devise an exact maximum likelihood estimator which offers significant improvements in estimation efficiency and volatility nowcast accuracy in finite samples. In our empirical application, we investigate five major international stock markets from 2000 to 2019 (including sub-samples relating to the Great Financial Crisis). The results suggest that our estimation approach significantly outperforms the one by Breitung and Hafner [2016. A Simple Model for Now-Casting Volatility Series. International Journal of Forecasting 32 (4): 1247–1255. doi:10.1016/j.ijforecast.2016.04.007] in all cases. Financial volatility can be nowcasted more accurately by applying our suggested estimation approach.