Optimal sampling frequency for volatility forecast models for the Indian stock markets
Article first published online: 5 SEP 2008
Copyright © 2008 John Wiley & Sons, Ltd.
Journal of Forecasting
Volume 28, Issue 1, pages 38–54, January 2009
How to Cite
Bhattacharyya, M., Kumar M, D. and Kumar, R. (2009), Optimal sampling frequency for volatility forecast models for the Indian stock markets. J. Forecast., 28: 38–54. doi: 10.1002/for.1080
- Issue published online: 10 DEC 2008
- Article first published online: 5 SEP 2008
- integrated volatility;
- realized volatility;
- conditional variance;
- daily volatility forecasting;
- optimal sampling frequency;
- high frequency data
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. Copyright © 2008 John Wiley & Sons, Ltd.