The volatility of realized volatility
AbstractUsing unobservable conditional variance as measure, latentvariable approaches, such as GARCH and stochasticvolatility models, have traditionally been dominating the empirical finance literature. In recent years, with the availability of highfrequency 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 timeseries models for realized volatility exhibit nonGaussianity 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 timevarying volatility of realized volatility leads to a substantial improvement of the models fit as well as predictive performance. Furthermore, the distributional assumption for residuals plays a crucial role in density forecasting. --
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Bibliographic InfoPaper provided by Center for Financial Studies (CFS) in its series CFS Working Paper Series with number 2005/33.
Date of creation: 2005
Date of revision:
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Finance; Realized Volatility; Realized Quarticity; GARCH; Normal Inverse Gaussian Distribution; Density Forecasting;
Other versions of this item:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
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