Quantile forecast combinations in realised volatility prediction

Abstract

This paper tests whether it is possible to improve point, quantile, and density forecasts of realised volatility by conditioning on a set of predictive variables. We employ quantile autoregressive models augmented with macroeconomic and financial variables. Complete subset combinations of both linear and quantile forecasts enable us to efficiently summarise the information content in the candidate predictors. Our findings suggest that no single variable is able to provide more information for the evolution of the volatility distribution beyond that contained in its own past. The best performing variable is the return on the stock market followed by the inflation rate. Our complete subset approach achieves superior point, quantile, and density predictive performance relative to the univariate models and the autoregressive benchmark.

Keywords:

• Forecasting
• realised volatility
• forecast combination
• predictive quantile regression
• subset quantile regressions

Acknowledgments

We would like to thank the editors and two anonymous referees for constructive comments and suggestions on an earlier version of the paper.

Additional information

Funding

Ioannis Vrontos greatly acknowledge financial support from the Research Center of the Athens University of Economics and Business under the grant no EP-2778-01.