Footnote:
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments December 30, 2022 erstellt
Description:
Standard realized volatility (RV) measures estimate the latent volatility of an asset price using high frequency data with no reference to how or where the estimate will subsequently be used. This paper presents methods for “tailoring” the estimate of volatility to the application in which it will be used. For example, if the volatility measure will be used in a specific parametric forecasting model, it may be possible to exploit that information and construct a better measure of volatility. We use methods from machine learning to estimate optimal “bespoke” RVs for heterogeneous autoregressive (HAR) and GARCH-X forecasting applications. We apply the methods to 886 U.S. stock returns and find that bespoke RVs significantly improve out-of-sample forecast performance. We find that the bespoke RV places more weight on data from the end of the trade day, and that the resulting volatility forecasts are more responsive to news than benchmark forecasts