Predicting volatility: getting the most out of return data sampled at different frequencies

We consider various mixed data sampling (MIDAS) regressions to predict volatility. The regressions differ in the specification of regressors (squared returns, absolute returns, realized volatility, realized power, and return ranges), in the use of daily or intra-daily (5-min) data, and in the length...

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Bibliographic Details
Published in:Journal of econometrics 2006-03, Vol.131 (1), p.59-95
Main Authors: Ghysels, Eric, Santa-Clara, Pedro, Valkanov, Rossen
Format: Article
Language:English
Subjects:
Capital market
Data analysis
Econometric models
Econometrics
Financial analysis
Financial models
Forecasting techniques
High-frequency data
MIDAS
Model selection
Modelling
Regression analysis
Sampling
Statistical methods
Stock returns
Studies
Volatility
Volatility forecasting
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Summary:We consider various mixed data sampling (MIDAS) regressions to predict volatility. The regressions differ in the specification of regressors (squared returns, absolute returns, realized volatility, realized power, and return ranges), in the use of daily or intra-daily (5-min) data, and in the length of the past history included in the forecasts. The MIDAS framework allows us to compare regressions across all these dimensions in a very tightly parameterized fashion. Using equity return data, we find that daily realized power (involving 5-min absolute returns) is the best predictor of future volatility (measured by increments in quadratic variation) and outperforms models based on realized volatility (i.e. past increments in quadratic variation). Surprisingly, the direct use of high-frequency (5 min) data does not improve volatility predictions. Finally, daily lags of 1–2 months are sufficient to capture the persistence in volatility. These findings hold both in- and out-of-sample.
ISSN:0304-4076
1872-6895
DOI:10.1016/j.jeconom.2005.01.004