Commodity value-at-risk modeling: comparing RiskMetrics, historic simulation and quantile regression

Marie Steen and Sjur Westgaard and Ole Gjølberg

Journal of Risk Model Validation

Abstract: ABSTRACT Commodities constitute a nonhomogeneous asset class. Return distributions differ widely across different commodities, both in terms of tail fatness and skewness. These are features that we need to take into account when modeling risk. In this paper, we outline the return characteristics of nineteen different commodity futures during the period 1992-2013. We then evaluate the performance of two standard risk modeling approaches, ie, RiskMetrics and historical simulation, against a quantile regression (QR) approach. Our findings strongly support the conclusion that QR outperforms these standard approaches in predicting value-at-risk for most commodities.

References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.risk.net/journal-of-risk-model-validat ... -quantile-regression (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:rsk:journ5:2414407

Access Statistics for this article

More articles in Journal of Risk Model Validation from Journal of Risk Model Validation
Bibliographic data for series maintained by Thomas Paine ().