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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