The volatility of realized volatility - Fulvio Corsi; Uta Kretschmer; Stefan Mittnik; Christian Pigorsch (Goethe University Frankfurt, Center for Financial Studies (CFS), 2005)
Using unobservable conditional variance as measure, latent-variable approaches, such as GARCH and stochastic-volatility models, have traditionally been dominating the empirical finance literature. In recent years, with the availability of high-frequency financial market data modeling realized volatility has become a new and innovative research direction. By constructing 'observable' or realized volatility series from intraday transaction data, the use of standard time series models, such as ARFIMA models, have become a promising strategy for modeling and predicting (daily) volatility. In this paper, we show that the residuals of the commonly used time-series models for realized volatility exhibit non-Gaussianity and volatility clustering. We propose extensions to explicitly account for these properties and assess their relevance when modeling and forecasting realized volatility. In an empirical application for S&P500 index futures we show that allowing for time-varying volatility of realized volatility leads to a substantial improvement of the model's fit as well as predictive performance. Furthermore, the distributional assumption for residuals plays a crucial role in density forecasting.
urn:nbn:de:hebis:30-25891
Keywords: C22; C51; C52; C53; 330; Finance; Realized Volatility; Realized Quarticity; GARCH; Normal Inverse Gaussian Distribution; Density Forecasting