Forecast Evaluation in Large Cross-Sections of Realized Volatility

Forecasting volatility has a fundamental scope for financial economics with applications in asset pricing, risk management as well as systemic risk monitoring due to the fact that forecasts of asset return volatilities are essential inputs for pricing models (Bollerslev et al. (2020)). A vast body of literature has been devoted to model design capable of accurately capturing volatility dynamics and producing reliable volatility forecasts. Furthermore, the increasing availability of high frequency data pushed the development of methods such as latent variable models such as the GARCH specifications as well as models for Stochastic Volatility (as in Bollerslev (1986) and Hansen and Lunde (2005)). Moreover, the inclusion of high frequency filters via the use of estimators for the true latent integrated volatilities has been examined in various studies such as in Andersen and Bollerslev (1998), Barndorff-Nielsen and Shephard (2002), Andersen et al. (2001), Andersen et al. (2003), Andersen et al. (2007) and Aït-Sahalia and Jacod (2014). In practise, time series observations for realized volatility measures at a given frequency (such as daily) are typically obtained by summing higher frequency squared returns (e.g.