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Quantile forecast combinations

Meligkotsidou, Loukia, Panopoulou, Ekaterini, Vrontos, Ioannis D., Vrontos, Spyridon D. (2016) Quantile forecast combinations. In: 10th International Conference on Computaional and Financial Econometrics, 9-11 December 2016, Sevilla, Spain. (Unpublished) (The full text of this publication is not currently available from this repository.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)

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. (Contact us about this Publication)

Abstract

Whether it is possible to improve point, quantile and density forecasts via quantile forecast combinations is tested. The models we employ are quantile autoregressive and mean regression models augmented with a plethora of macroeconomic and financial variables. Complete subset combinations of both linear and quantile forecasts enable us to efficiently summarize the information content in the candidate predictors. We also develop a recursive algorithm that selects, in real time, the best complete subset for each predictive regression quantile. We provide two forecasting applications; one related to stock market return forecasting and the second on realised volatility forecasting. We show that our approach delivers statistically and economically significant out-of-sample forecasts relative to both the historical average/autoregressive benchmark and the complete subset regression approach.

Item Type: Conference or workshop item (Paper)
Subjects: H Social Sciences > HG Finance
Divisions: Faculties > Social Sciences > Kent Business School
Faculties > Social Sciences > Kent Business School > Accounting and Finance
Depositing User: Ekaterini Panopoulou
Date Deposited: 13 Nov 2017 09:51 UTC
Last Modified: 29 May 2019 19:48 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/64355 (The current URI for this page, for reference purposes)
Panopoulou, Ekaterini: https://orcid.org/0000-0001-5080-9965
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