Forecasting Multivariate Volatility using the VARFIMA Model on Realized Covariance Cholesky Factors
-
Roxana Halbleib
and
Valeri Voev
Summary
This paper analyzes the forecast accuracy of the multivariate realized volatility model introduced by Chiriac and Voev (2010), subject to different degrees of model parametrization and economic evaluation criteria. Bymodelling the Cholesky factors of the covariance matrices, the model generates positive definite, but biased covariance forecasts. In this paper, we provide empirical evidence that parsimonious versions of the model generate the best covariance forecasts in the absence of bias correction. Moreover, we show by means of stochastic dominance tests that any risk averse investor, regardless of the type of utility function or return distribution, would be better-off from using this model than from using some standard approaches.
© 2011 by Lucius & Lucius, Stuttgart
Articles in the same Issue
- Titelei
- Inhalt / Contents
- Special Issue on Economic Forecasts: Guest Editorial
- Abhandlungen / Original Papers
- Information or Institution?
- Forecasting with Factor Models Estimated on Large Datasets: A Review of the Recent Literature and Evidence for German GDP
- A Factor Model for Euro-area Short-term Inflation Analysis
- Combining Survey Forecasts and Time Series Models: The Case of the Euribor
- Predictive Ability of Business Cycle Indicators under Test
- Forecasting Nonlinear Aggregates and Aggregates with Time-varying Weights
- Forecasting Multivariate Volatility using the VARFIMA Model on Realized Covariance Cholesky Factors
- Practice and Prospects of Medium-term Economic Forecasting
- Buchbesprechungen / Book Reviews
Articles in the same Issue
- Titelei
- Inhalt / Contents
- Special Issue on Economic Forecasts: Guest Editorial
- Abhandlungen / Original Papers
- Information or Institution?
- Forecasting with Factor Models Estimated on Large Datasets: A Review of the Recent Literature and Evidence for German GDP
- A Factor Model for Euro-area Short-term Inflation Analysis
- Combining Survey Forecasts and Time Series Models: The Case of the Euribor
- Predictive Ability of Business Cycle Indicators under Test
- Forecasting Nonlinear Aggregates and Aggregates with Time-varying Weights
- Forecasting Multivariate Volatility using the VARFIMA Model on Realized Covariance Cholesky Factors
- Practice and Prospects of Medium-term Economic Forecasting
- Buchbesprechungen / Book Reviews
