Quantile forecast combinations in realised volatility prediction

Meligkotsidou, Loukia, Panopoulou, Ekaterini, Vrontos, Ioannis D., Vrontos, Spyridon D. (2016) Quantile forecast combinations in realised volatility prediction. In: 9th International Conference on Computational and Financial Econometrics, 12-14 December 2015, Senate House, London. (Unpublished) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:64356)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL: http://cfenetwork.org/CFE2015/

Abstract

Whether it is possible to improve realised volatility forecasts by conditioning on macroeconomic and financial variables is tested. We employ complete subset combinations of both linear and quantile forecasts in order to construct robust and accurate stock market volatility predictions. Our findings suggest that the complete subset approach delivers statistically significant out-of-sample forecasts relative to the autoregressive benchmark and traditional combination schemes. A recursive algorithm that selects, in real time, the best complete subset for each predictive regression quantile succeeds in identifying the best subset in a time- and quantile-varying manner.

Item Type:	Conference or workshop item (Paper)
Subjects:	H Social Sciences > HG Finance
Divisions:	Divisions > Kent Business School - Division > Kent Business School (do not use)
Depositing User:	Ekaterini Panopoulou
Date Deposited:	13 Nov 2017 10:06 UTC
Last Modified:	05 Nov 2024 11:01 UTC
Resource URI:	https://kar.kent.ac.uk/id/eprint/64356 (The current URI for this page, for reference purposes)

University of Kent Author Information

Panopoulou, Ekaterini.

Creator's ORCID:	https://orcid.org/0000-0001-5080-9965
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