Optimal sampling frequency for volatility forecast models for the Indian stock markets

Bhattacharyya, Malay ; Dileep Kumar, M. ; Kumar, Ramesh (2009) Optimal sampling frequency for volatility forecast models for the Indian stock markets Journal of Forecasting, 28 (1). pp. 38-54. ISSN 0277-6693

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Official URL: http://www3.interscience.wiley.com/journal/1213840...

Related URL: http://dx.doi.org/10.1002/for.1080


This paper evaluates the performance of conditional variance models using high-frequency data of the National Stock Index (S&P CNX NIFTY) and attempts to determine the optimal sampling frequency for the best daily volatility forecast. A linear combination of the realized volatilities calculated at two different frequencies is used as benchmark to evaluate the volatility forecasting ability of the conditional variance models (GARCH (1, 1)) at different sampling frequencies. From the analysis, it is found that sampling at 30 minutes gives the best forecast for daily volatility. The forecasting ability of these models is deteriorated, however, by the non-normal property of mean adjusted returns, which is an assumption in conditional variance models. Nevertheless, the optimum frequency remained the same even in the case of different models (EGARCH and PARCH) and different error distribution (generalized error distribution, GED) where the error is reduced to a certain extent by incorporating the asymmetric effect on volatility. Our analysis also suggests that GARCH models with GED innovations or EGRACH and PARCH models would give better estimates of volatility with lower forecast error estimates.

Item Type:Article
Source:Copyright of this article belongs to John Wiley and Sons, Inc.
Keywords:Integrated Volatility; Realized Volatility; Conditional Variance; Daily Volatility Forecasting; Optimal Sampling Frequency; High Frequency Data
ID Code:9820
Deposited On:02 Nov 2010 04:44
Last Modified:30 May 2011 11:52

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