The volatility of realized volatility
Fulvio Corsi (),
Stefan Mittnik and
No 2005/33, CFS Working Paper Series from Center for Financial Studies (CFS)
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.
Keywords: Finance; Realized Volatility; Realized Quarticity; GARCH; Normal Inverse Gaussian Distribution; Density Forecasting (search for similar items in EconPapers)
JEL-codes: C22 C51 C52 C53 (search for similar items in EconPapers)
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Journal Article: The Volatility of Realized Volatility (2008)
Working Paper: The Volatility of Realized Volatility (2005)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:cfswop:200533
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