Risk modelling of energy futures: A comparison of riskmetrics, historical simulation, filtered historical simulation, and quantile regression

Kai Erik Dahlen*, Ronald Huisman, Sjur Westgaard

*Corresponding author for this work

Research output: Chapter/Conference proceedingConference proceedingAcademicpeer-review

2 Citations (Scopus)

Abstract

Prices of energy commodity futures often display high volatility and changes in return distribution over time, making accurate risk modelling both important and challenging. Non-complex risk measuring methods that work quite well for financial assets perform worse when applied to energy commodities. More advanced approaches have been developed to deal with these issues, but either are too complex for practitioners or do not perform consistently as they work for one commodity but not for another. The goal of this paper is to examine, from the viewpoint of a European energy practitioner, whether some non-estimation complex methods for calculating Value-at-Risk can be found to provide consistent results for different energy commodity futures. We compare Risk Metrics™, historical simulation, filtered historical simulation and quantile regression applied to crude oil, gas oil, natural gas, coal, carbon and electricity futures. We find that historical simulation filtered with an exponential weighted moving average (EWMA) for recent trends and volatility performs best and most consistent among the commodities in this paper.

Original languageEnglish
Title of host publicationStochastic Models, Statistics and Their Applications, 2015
EditorsEwaryst Rafajłowicz, Krzysztof Szajowski, Ansgar Steland
PublisherSpringer New York
Pages283-291
Number of pages9
ISBN (Electronic)9783319138800
DOIs
Publication statusPublished - 2015
Event12th Workshop on Stochastic Models, Statistics and their Applications, 2015 - Wrocław, Poland
Duration: 16 Feb 201520 Feb 2015

Publication series

SeriesSpringer Proceedings in Mathematics and Statistics
Volume122
ISSN2194-1009

Conference

Conference12th Workshop on Stochastic Models, Statistics and their Applications, 2015
Country/TerritoryPoland
CityWrocław
Period16/02/1520/02/15

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2015.

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