Title: Combining multiple frequencies in multivariate volatility forecasting
Authors: Alessandra Amendola - University of Salerno (Italy) [presenting]
Vincenzo Candila - Sapienza University of Rome (Italy)
Giuseppe Storti - University of Salerno (Italy)
Abstract: In a multivariate volatility framework, several options are available to estimate the conditional covariance matrix of returns. Some models, like the multivariate GARCH (MGARCH) ones, rely on daily returns while others exploit the additional information provided by the analysis of intra-daily prices, like the realized covariance (RC) specifications. An additional source of uncertainty is related to the choice of the frequency at which the intradaily returns, used to construct the RC matrices, are observed. Our interest is in analyzing the impact of these two sources of uncertainty on volatility prediction. In particular, we investigate the profitability of a prediction strategy based on combining forecasts coming from different model structures that are estimated using information at various frequencies. In order to illustrate the benefits of our approach we carry out an extensive application to portfolio allocation for a panel of U.S. stocks.