Time-varying risk aversion: An application to energy hedging
John Cotter () and
Jim Hanly ()
Energy Economics, 2010, vol. 32, issue 2, 432-441
Risk aversion is a key element of utility maximizing hedge strategies; however, it has typically been assigned an arbitrary value in the literature. This paper instead applies a GARCH-in-Mean (GARCH-M) model to estimate a time-varying measure of risk aversion that is based on the observed risk preferences of energy hedging market participants. The resulting estimates are applied to derive explicit risk aversion based optimal hedge strategies for both short and long hedgers. Out-of-sample results are also presented based on a unique approach that allows us to forecast risk aversion, thereby estimating hedge strategies that address the potential future needs of energy hedgers. We find that the risk aversion based hedges differ significantly from simpler OLS hedges. When implemented in-sample, risk aversion hedges for short hedgers outperform the OLS hedge ratio in a utility based comparison.
Keywords: Energy; Hedging; Risk; management; Risk; aversion; Forecasting (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12) Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
Working Paper: Time Varying Risk Aversion: An Application to Energy Hedging (2011)
Working Paper: Time Varying Risk Aversion: An Application to Energy Hedging (2010)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:32:y:2010:i:2:p:432-441
Access Statistics for this article
Energy Economics is currently edited by R. S. J. Tol, Beng Ang, Lance Bachmeier, Perry Sadorsky, Ugur Soytas and J. P. Weyant
More articles in Energy Economics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().