Time Varying Risk Aversion: An Application to Energy Hedging

TL;DR

This paper introduces a dynamic method to estimate time-varying risk aversion in energy markets using a GARCH-M model, leading to more effective hedge strategies that outperform traditional methods.

Contribution

It develops a novel approach to estimate and forecast risk aversion dynamically, improving hedge strategy performance in energy markets.

Findings

Risk aversion based hedges outperform OLS hedges in-sample.

Dynamic risk aversion estimates improve hedge effectiveness.

Forecasted risk aversion enhances future hedge strategies.

Abstract

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.