Predicitng volatility
Subtitle: getting the most out of return data sampled at different frequencies
By: Eric Ghysels, Pedro Santa-Clara, Rossen Valkanov.
Contributors: Santa-Clara, Pedro.; Valkanov, Rossen I. 1973-; National Bureau of Economic Research.
Series: NBER working paper series -- working paper 10914; Working paper series (National Bureau of Economic Research : Online) -- working paper no. 10914.
Publisher: National Bureau of Economic Research
Published: 2004
Place of publication: Cambridge, MA
Language: eng
Format: Electronic resource
Subjects: Econometric models
Description: "We consider various MIDAS (Mixed Data Sampling) regression models to predict volatility.  The models 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-minute) data, and in the length of the past history included in the forecasts.  The MIDAS framework allows us to compare models across all these dimensions in a very tightly parameterized fashion.  Using equity return data, we find that daily realized power (involving 5-minute absolute returns) is the best predictor of future volatility (measured by increments in quadratic variation) and outperforms model based on realized volatility (i.e.  past increments in quadratic variation).  Surprisingly, the direct use of high-frequency (5-minute) data does not improve volatility predictions.  Finally, daily lags of one to two months are sucient to capture the persistence in volatility.  These findings hold both in- and out-of-sample"--National Bureau of Economic Research web site.
Notes: Title from PDF file as viewed on 1/13/2005.

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