Bayesian estimation of electricity price risk with a multi-factor mixture of densities

Kang, L, Walker, S, Damien, P and Bunn, D W (2022) Bayesian estimation of electricity price risk with a multi-factor mixture of densities. Quantitative Finance. ISSN 1469-7688 (In Press)

Abstract

The risks in daily electricity prices are becoming substantial and it is clear that improvements in price density forecasting can translate into improved risk management. However, the specification of the most appropriate price density function is challenging as the best functional forms differ by time of day evolve over time, dynamically respond to fluctuating exogenous factors such as wind speed and solar irradiance. This research develops and tests a new flexible, functional form based upon the Gamma Mixture of Uniform (GMU) densities which effectively avoids the choice of a particular density function and has conditional moments specified as a function of the dynamic exogenous drivers. Empirical testing shows that it outperforms the multi-factor skewed student-t family of densities, previously advocated in this context. Additionally, using Bayesian estimation the new methodology provides a complete description of the uncertainty in the estimation of the coefficients for those exogenous factors. Empirical testing on day-ahead hourly electricity prices in the German market from 2012 to 2016, where renewable energy sources, such as wind and solar, play a critical role in the formation of electricity price risk, validates the extra accuracy of this formulation.

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Item Type: Article
Subject Areas: Management Science and Operations
Additional Information:

This is an Accepted Manuscript version of the following article, accepted for publication in Quantative Finance: Li Kang, Stephen Walker, Paul Damien & Derek Bunn (2022) Bayesian estimation of electricity price risk with a multi-factor mixture of densities, Quantitative Finance, DOI: 10.1080/14697688.2022.2052165

It is deposited under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License https://creativecommons.org/licenses/by-nc-nd/4.0/, which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.

Date Deposited: 04 Apr 2022 09:57
Date of first compliant deposit: 28 Feb 2022
Subjects: Electricity supply industry
Last Modified: 10 Apr 2022 00:23
URI: https://lbsresearch.london.edu/id/eprint/2456
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