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The volatility of realized volatility

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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.

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Fulvio Corsi, Uta Kretschmer, Stefan Mittnik, Christian Pigorsch

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Adapt according to the presented license agreement and reference the original author.