Downside risk and the energy hedger's horizon (original) (raw)
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Energy Commodities: A Review of Optimal Hedging Strategies
Energies, 2019
Energy is considered as a commodity nowadays and continuous access along with price stability is of vital importance for every economic agent worldwide. The aim of the current review paper is to present in detail the two dominant hedging strategies relative to energy portfolios, the Minimum-Variance hedge ratio and the expected utility maximization methodology. The Minimum-Variance hedge ratio approach is by far the most popular in literature as it is less time consuming and computationally demanding; nevertheless by applying the appropriate multivariate model Garch family volatility model, it can provide a very reliable estimation of the optimal hedge ratio. However, this becomes possible at the cost of a rather restrictive assumption for infinite hedger's risk aversion. Within an uncertain worldwide economic climate and a highly volatile energy market, energy producers, retailers and consumers had to become more adaptive and develop the necessary energy risk management and optimal hedging strategies. The estimation gap of an optimal hedge ratio that would be subject to the investor's risk preferences through time is filled by the relatively more complex and sophisticated expected utility maximization methodology. Nevertheless, if hedgers share infinite risk aversion or if alternatively the expected futures price is approximately zero the two methodologies become equivalent. The current review shows that when evidence from the energy market during periods of extremely volatile economic climate is considered, both hypotheses can be violated, hence it becomes reasonable that especially for extended hedging horizons it would be wise for potential hedgers to take into consideration both methodologies in order to build a successful and profitable hedging strategy.
Quantile hedge ratio for energy markets
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In this study, we estimate the minimum variance (MV) and quantile hedge ratios for three energy-related commodities: crude oil, heating oil and natural gas. For crude oil and heating oil, we find the quantile hedge ratios to have inverted U shape using daily data. However, for natural gas, the quantile hedge ratios are mostly below the MV hedge ratio which is significantly lower compared to naive hedge ratio. Such behavior of hedge ratios for daily data is consistent with our empirical results which suggest that price discovery mostly takes place in the futures market for natural gas. We also estimate the hedge ratios for weekly and four-weekly hedging horizons using non-overlapping data. For the longer horizon, we use wavelet analysis to decompose the return time series into different components with respect to different times-scales. We find that, eventually for longer hedging horizons, the quantile hedge ratios converges to MV hedge ratio. The crude oil takes the shortest timescale to achieve the convergence and the natural gas takes the longest timescale. Finally, consistent with other studies, we find the hedging effectiveness to increase with hedging horizon.
Energy Market Risk Management Under Uncertainty: A Var Based on Wavelet Approach
International Journal of Energy Economics and Policy, 2021
This study contributes to the literature on energy market risk management and portfolio management by examining co-movements between several energy commodities in a portfolio context in light of the impact of several types of uncertainty over time and under high, medium, and low frequencies. Using of wavelet decomposition analysis, we first investigate the lead-lag relationship together with the power of the correlation over time between major renewable and non-renewable energy indexes and uncertainty indexes. Second, we explore the contribution of uncertainty to the energy portfolio. Our procedure reveals that a dependent relationship generally exists between energy returns and changes in uncertainty. The risks of clean energy and crude oil returns are more sensitive to financial uncertainties, whereas investing in GAS markets offers market diversification opportunities during periods of energy uncertainty.
The risk premia of energy futures
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This paper studies the energy futures risk premia that can be extracted through long-short portfolios that exploit heterogeneities across contracts as regards various characteristics or signals and integrations thereof. Investors can earn a sizeable premium of about 8% and 12% per annum by exploiting the energy futures contract risk associated with the hedgers' net positions and roll-yield characteristics, respectively, in line with predictions from the hedging pressure hypothesis and theory of storage. Simultaneously exploiting various signals towards style-integration with alternative weighting schemes further enhances the premium. In particular, the style-integrated portfolio that equally weights all signals stands out as the most effective. The findings are robust to transaction costs, data mining and sub-period analyses.
Commodity Futures Hedging, Risk Aversion and the Hedging Horizon
SSRN Electronic Journal, 2000
This paper examines the impact of investor preferences on the optimal futures hedging strategy and associated hedging performance. Explicit risk aversion levels are often overlooked in hedging analysis. Applying a mean-variance hedging objective, the optimal futures hedging ratio is determined for a range of investor preferences on risk aversion, hedging horizon and expected returns. Wavelet analysis is applied to illustrate how investor time horizon shapes hedging strategy. Empirical results reveal substantial variation of the optimal hedge ratio for distinct investor preferences and are supportive of the hedging policies of real firms. Hedging performance is then shown to be strongly dependent on underlying preferences. In particular, investors with high levels of risk aversion and a short horizon reduce the risk of the hedge portfolio but achieve inferior utility in comparison to those with low risk aversion.
Time-varying risk aversion: An application to energy hedging
Energy Economics, 2010
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.
Research Square (Research Square), 2021
In this paper, long memory and time varying hedging opportunities between clean energy, West Texas Intermediate (WTI) crude oil and technology share prices were analyized between 3 May 2005-16 October 2019. The relationships were investigated by DECO-FIGARCH model with daily frequencies. According to findings, it is understood that volatility clusters were determined in crude oil, alternate source energy and technology returns. Due to this useful information shocks reach to all three investment tools and being eliminated at hyperbolic speed, also the volatility spillover lasted for a long time. The most important finding of the research is that long position risks arising in both clean energy and technology sectors can be effectively and efficiently hedged with WTI futures contracts. On the other hand, it was determined that WTI can be added to the portfolio in order to reduce the risks of portfolio to be established with clean energy and technology sector.
Modeling, Risk Assessment and Portfolio Optimization of Energy Futures
This paper examines the portfolio optimization of energy futures by using the STARR ratio that can evaluate the risk and return relationship for skewed distributed returns. We model the price returns for energy futures by using the ARMA(1,1)-GARCH(1,1)-PCA model with stable distributed innovations that reflects the characteristics of energy: mean reversion, heteroskedasticity, seasonality, and spikes. Then, we propose the method for selecting the portfolio of energy futures by maximizing the STARR ratio, what we call "Winner portfolio". The empirical studies by using energy futures of WTI crude oil, heating oil, and natural gas traded on the NYMEX compare the price return models with stable distributed innovations to those with normal ones.