Meligkotsidou, Loukia and Panopoulou, Ekaterini and Vrontos, Ioannis and Vrontos, Spyridon D (2019) Quantile Forecast Combinations in Realised Volatility Prediction. Journal of the Operational Research Society, 70 (10). pp. 1720-1733. DOI https://doi.org/10.1080/01605682.2018.1489354
Meligkotsidou, Loukia and Panopoulou, Ekaterini and Vrontos, Ioannis and Vrontos, Spyridon D (2019) Quantile Forecast Combinations in Realised Volatility Prediction. Journal of the Operational Research Society, 70 (10). pp. 1720-1733. DOI https://doi.org/10.1080/01605682.2018.1489354
Meligkotsidou, Loukia and Panopoulou, Ekaterini and Vrontos, Ioannis and Vrontos, Spyridon D (2019) Quantile Forecast Combinations in Realised Volatility Prediction. Journal of the Operational Research Society, 70 (10). pp. 1720-1733. DOI https://doi.org/10.1080/01605682.2018.1489354
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 |
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Uncontrolled Keywords: | Forecasting; Realised volatility; Forecast combination; Predictive quantile regression; Subset quantile regressions. |
Subjects: | Q Science > QA Mathematics |
Divisions: | Faculty of Science and Health Faculty of Social Sciences Faculty of Science and Health > Mathematics, Statistics and Actuarial Science, School of Faculty of Social Sciences > Essex Business School |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 14 Sep 2018 09:19 |
Last Modified: | 16 May 2024 19:27 |
URI: | http://repository.essex.ac.uk/id/eprint/22208 |
Available files
Filename: subset_volatility.pdf