Quantile Forecast Combinations in Realised Volatility Prediction

43 Pages Posted: 14 Oct 2015

See all articles by Loukia Meligkotsidou

Loukia Meligkotsidou

National and Kapodistrian University of Athens

Ekaterini Panopoulou

Essex Business School

Ioannis D. Vrontos

Athens University of Economics and Business

Spyridon D. Vrontos

University of Piraeus - Department of Statistics and Insurance Science

Date Written: October 13, 2015

Abstract

This paper tests whether it is possible to improve point, quantile and density forecasts of realized volatility by conditioning on macroeconomic and financial variables. We employ quantile autoregressive models augmented with a plethora of 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 quantile, density and point predictive performance relative to the univariate models and the autoregressive benchmark.

Keywords: Forecasting, Realised volatility, Forecast combination, Predictive quantile regression, Subset quantile regressions

JEL Classification: G12, G22, C22, C53

Suggested Citation

Meligkotsidou, Loukia and Panopoulou, Ekaterini and Vrontos, Ioannis D. and Vrontos, Spyridon D., Quantile Forecast Combinations in Realised Volatility Prediction (October 13, 2015). Available at SSRN: https://ssrn.com/abstract=2673529 or http://dx.doi.org/10.2139/ssrn.2673529

Loukia Meligkotsidou

National and Kapodistrian University of Athens ( email )

5 Stadiou Strt
Athens, 12131
Greece

Ekaterini Panopoulou (Contact Author)

Essex Business School ( email )

Wivenhoe Park
Colchester, CO4 3SQ
United Kingdom

Ioannis D. Vrontos

Athens University of Economics and Business ( email )

76 Patission Street
Athens, 104 34
Greece

Spyridon D. Vrontos

University of Piraeus - Department of Statistics and Insurance Science ( email )

80 Karaoli & Dimitriou str.
Piraeus, 18534
Greece

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