Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/25467
Authors: 
Corsi, Fulvio
Kretschmer, Uta
Mittnik, Stefan
Pigorsch, Christian
Year of Publication: 
2005
Series/Report no.: 
CFS Working Paper 2005/33
Abstract: 
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.
Subjects: 
Finance
Realized Volatility
Realized Quarticity
GARCH
Normal Inverse Gaussian Distribution
Density Forecasting
JEL: 
C22
C51
C52
C53
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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