Forecasting intraday volatility in the US equity market. Multiplicative component GARCH

This paper proposes a new intraday volatility forecasting model, particularly suitable for modeling a large number of assets. We decompose volatility of high-frequency returns into components that may be easily interpreted and estimated. The conditional variance is a product of daily, diurnal, and stochastic intraday components. This model is applied to a comprehensive sample consisting of 10-minute returns on more than 2500 US equities. Apart from building a new model, we obtain several interesting forecasting results. We apply a number of different specifications. We estimate models for separate companies, pool data into industries, and consider other criteria for grouping returns. In general, forecasts from pooled cross-section of companies outperform the corresponding forecasts from company-by-company estimation. For less liquid stocks, however, we obtain better forecasts when we group less frequently traded companies together. Copyright The Author 2011. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com., Oxford University Press.