Jim Hanly

Time-varying risk aversion: An application to energy hedging

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.

Time varying risk aversion : an application to energy hedging

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.

Managing Energy Price Risk Using Futures Contracts: A Comparative Analysis

This paper carries out a comparative analysis of managing energy risk through futures hedging, for energy market participants across a broad dataset that encompasses the largest and most actively traded energy products. Uniquely, we carry out a hedge comparison using a variety of risk measures including Variance, Value at risk (VaR), and Expected Shortfall as well as a utility based performance metric for two different investor horizons; weekly and monthly. We find that hedging is effective across the spectrum of risk measures we employ. We also find significant differences in both the hedging strategies and the hedging effectiveness of different energy assets. Better performance is found for West Texas Intermediate Oil and Heating Oil while the poorest performer in hedging terms is Natural Gas.

Volatility and Risk Management in European Electricity Futures Markets

This paper estimates and applies a risk management strategy for electricity spot exposures using futures hedging. We apply our approach to three of the most actively traded European electricity markets, Nordpool, APXUK and Phelix. We compare both optimal hedging strategies and the hedging effectiveness of these markets for two hedging horizons, weekly and monthly using both Variance and Value at Risk (VaR). We find significant differences in both the Optimal Hedge Ratios (OHR’s) and the hedging effectiveness of the different electricity markets. Better performance is found for the Nordpool market while the poorest performer in hedging terms is Phelix. However we also find that electricity futures hedging are relatively ineffective as a risk management tool when compared with other energy assets. This is especially true at the weekly frequency.