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Quantile Forecast Combinations in Realised Volatility Prediction

Meligkotsidou, Loukia, Panopoulou, Ekaterini, Vrontos, Ioannis D., Vrontos, Spyridon D. (2019) Quantile Forecast Combinations in Realised Volatility Prediction. Journal of the Operational Research Society, . ISSN 0160-5682. (doi:10.1080/01605682.2018.1489354) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided)

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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.

Item Type: Article
DOI/Identification number: 10.1080/01605682.2018.1489354
Uncontrolled keywords: Forecasting; Realised volatility; Forecast combination; Predictive quantile regression; Subset quantile regressions
Subjects: H Social Sciences
Divisions: Faculties > Social Sciences > Kent Business School > Accounting and Finance
Depositing User: Ekaterini Panopoulou
Date Deposited: 06 Jun 2018 11:41 UTC
Last Modified: 12 Jul 2019 15:07 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/67216 (The current URI for this page, for reference purposes)
Panopoulou, Ekaterini: https://orcid.org/0000-0001-5080-9965
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