Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/284182 
Year of Publication: 
2023
Series/Report no.: 
Cardiff Economics Working Papers No. E2023/20
Publisher: 
Cardiff University, Cardiff Business School, Cardiff
Abstract: 
Realized covariance models specify the conditional expectation of a realized covariance matrix as a function of past realized covariance matrices through a GARCH-type structure. We compare the forecasting performance of several such models in terms of economic value, measured through economic loss functions, on two datasets. Our empirical results indicate that the (HEAVY-type) models that use realized volatilities yield economic value and significantly surpass the (GARCH) models that use only daily returns for daily and weekly horizons. Among the HEAVY-type models, for a dataset of twenty-nine stocks, those that are specified to capture the heterogeneity of the dynamics of the individual conditional variance processes and to allow these to differ from the correlation processes (namely, DCC-type models) are more beneficial than the models that impose the same dynamics to the variance and covariance processes (namely, BEKK-type models), whereas for the dataset of three assets, the different models perform similarly. Finally, using a directly rescaled intra-day covariance to estimate the full-day covariance provides more economic value than using the overnight returns, as the latter tend to yield noisy estimators of the overnight covariance, impairing their predictive capacity.
Subjects: 
volatility timing
realized volatility
high-frequency data
forecasting
JEL: 
G11
G17
C32
C58
Document Type: 
Working Paper

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