Minimum Variance Hedge Ratios Research Articles

Estimation of Optimal Hedge Ratio: A Wild Bootstrap Approach

This paper proposes a new approach to estimating the minimum variance hedge ratio (MVHR) based on the wild bootstrap and evaluates the approach using a spectrum of conservative to aggressive alternative hedging strategies associated with the percentiles of the MVHR’s bootstrap distribution. This approach is suggested to be more informative and effective relative to the conventional method of hedging solely based on a single-point estimate. Furthermore, the percentile-based MVHRs are robust to influential outliers, non-normality, and unknown forms of heteroskedasticity. The bootstrap percentile-based hedging strategies’ effectiveness is compared with those from the naïve method and the asymmetric DCC-GARCH model for a range of financial assets and commodities. The bootstrap percentile-based hedging technique is identified to outperform its alternatives in terms of hedging effectiveness, downside risk, and return variability, suggesting its superiority to other methods in both the literature and in practice.

Hedging with futures during nonconvergence in commodity markets

Hedging in grain futures markets offers market participants the opportunity to mitigate the price risk in spot markets by taking offsetting positions in futures. The performance of a traditional minimum variance hedge ratio (MVHR) relies on the correlation between the spot and futures price changes. During 2005–2010, delivery-location cash prices for several crops decoupled from the prices for their related expiring futures contracts—raising concerns over the hedging effectiveness of these contracts. We investigate how short hedgers, like farmers, performed during periods with and without convergence in corn, soybean, and wheat markets. We show that, ex post, MVHR, often does not minimize the variance of wheat producers’ profits during nonconvergence when compared to a range of other hedging choices. We also find that the performance of MVHR weakens during years with low carryover. We further assess hedging performance of MVHR and other hedge ratios in achieving higher net selling prices, and find that nonconvergence particularly impairs their performance in the wheat market where the nonconvergence anomaly was the most prominent. Taken together, our results raise questions on the role of futures markets as risk management tools during nonconvergence episodes regardless of how the hedge ratio is chosen.

A Comparative Analysis of Hedging Determination for Three Alternative International Equity Index Futures

This study provides a comparative analysis of hedging determination for three alternative international equity index futures, namely FTSE 100, NIKKEI 225 and S&P 500 futures contracts. Both the conventional regression and the error correction modeling approaches are used to estimate the minimum-variance hedge ratios and to evaluate the hedging effectiveness. Comparisons of out-of-sample hedging performance reveal that the error correction model outperforms the conventional model, suggesting that the error correction model serves as a better hedging model than conventional model in a hedge using stock index. In addition, the effects of temporal aggregation on hedge ratio estimates and hedging effectiveness are evaluated. It is found that temporal aggregation plays an important role on sizing hedging positions and measuring hedge effectiveness.

Contribution of Exchange Traded Funds in Hedging Crude Oil Price Risk

In this study, we empirically analyze the contributions of three crude oil-based exchange traded funds (ETFs) and the futures contract in hedging crude oil price risk. In order to measure hedging contributions of ETFs, we estimate the usual minimum variance hedge ratios as well as the quantile based minimum variance hedge ratios based on three different methods. We also compute the hedging effectiveness of the futures contract and three ETFs. We find that ETFs can be used as hedging instruments especially for the longer hedging horizons and extreme quantiles. However, overall, we find the futures contract to be the most effective instrument for hedging.

Minimum Variance Hedge Ratios using Short-Lived Arbitrage Model and Artificial Neural Network

Minimum Variance Hedge Ratios using Short-Lived Arbitrage Model and Artificial Neural Network

Using the short-lived arbitrage model to compute minimum variance hedge ratios: application to indices, stocks and commodities

The short-lived arbitrage model has been shown to significantly improve in-sample option pricing fit relative to the Black–Scholes model. Motivated by this model, we imply both volatility and virtual interest rates to adjust minimum variance hedge ratios. Using several error metrics, we find that the hedging model significantly outperforms the traditional delta hedge and a current benchmark hedge based on the practitioner Black–Scholes model. Our applications include hedges of index options, individual stock options and commodity futures options. Hedges on gold and silver are especially sensitive to virtual interest rates.

Baltic Exchange index changes and FFA hedging efficiency

We investigate how changes in the composition of the underlying Baltic index affect the hedging efficiency of Forward Freight Agreements (FFAs) on composite regional routes. We evaluate the hedging efficiency using minimum variance hedge ratios and naïve hedge ratios, both within and across sub-periods between 2006 and 2018, and assess whether index changes have a statistically significant impact using bootstrapping techniques and bias-corrected confidence intervals. Our findings suggest that the trend of adding more routes, reducing the weights of constituent routes, does not reduce hedging efficiency. We suggest that the co-integration between individual routes, and between spot rates and FFA prices, make Baltic Exchange index changes largely irrelevant with regard to hedging efficiency.

Generalised cepstral models for the spectrum of vector time series

The paper treats the modeling of stationary multivariate stochastic processes via a frequency domain model expressed in terms of cepstrum theory. The proposed model nests the vector exponential model of [20] as a special case, and extends the generalised cepstral model of [36] to the multivariate setting, answering a question raised by the last authors in their paper. Contemporarily, we extend the notion of generalised autocovariance function of [35] to vector time series. Then we derive explicit matrix formulas connecting generalised cepstral and autocovariance matrices of the process, and prove the consistency and asymptotic properties of the Whittle likelihood estimators of model parameters. Asymptotic theory for the special case of the vector exponential model is a significant addition to the paper of [20]. We also provide a mathematical machinery, based on matrix differentiation, and computational methods to derive our results, which differ significantly from those employed in the univariate case. The utility of the proposed model is illustrated through Monte Carlo simulation from a bivariate process characterized by a high dynamic range, and an empirical application on time varying minimum variance hedge ratios through the second moments of future and spot prices in the corn commodity market.

Energy Commodities: A Review of Optimal Hedging Strategies

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.

Hedging performance and the heterogeneity among market participants

PurposeThis paper aims to apply the heterogeneous autoregressive model of realized volatility (HAR-RV) model to minimum-variance hedge ratio estimation and compares the hedging performance of presenting a model with that of a conventional rolling ordinary-least-square (OLS) hedging model. Moreover, this paper empirically analyzes the relationship between hedging performance and the heterogeneity of investors with different trading frequency in forming the expectation for the spot volatility, futures volatility and the covariance in the market.Design/methodology/approachUse HAR-RV to form expectations of participants of spots and futures market for the next period volatility based on two parts. One is the current observable realized volatility at the same time scale. The other is the expectation for the next longer time scale horizon volatility. Compare hedging performance with rolling OLS model and HAR-RV model. Present a three-times-scale-length (daily, weekly and monthly) HAR-RV model for the spot and futures returns and volatility to analyze the relationship between the hedging performance and the heterogeneity among participants in each market.FindingsThe empirical results show that HAR-RV model outperforms the rolling OLS in terms of variance reduction and expected utility in the out-of-sample period. The results also indicate that the greater variance reduction occurs when investors with different trading frequency have a less heterogeneous expectation for spot volatility and more heterogeneous expectation for futures volatility and the covariance. In addition, the expected utility increases along with lower heterogeneity in spot volatility and higher in futures volatility and the covariance. Hedging performance improves along with decreasing heterogeneity of investors in spot volatility and increasing heterogeneity in futures volatility and the covariance.Originality/valueThis paper considers the heterogeneity of participants in spot and futures market, the authors apply HAR-RV model to MVHR estimation and compare the hedging performance of presenting a model with that of conventional rolling OLS hedging model, providing more evidence in hedging literature. This paper analyzes in depth the relationship between hedging performance and the heterogeneity in the market.

Futures minimum variance hedge ratio determination: An ex-ante analysis

Traditionally, futures Minimum Variance Hedge Ratios (MVHRs) are determined ex post. In this paper, we derive 3 increasingly realistic ex ante MVHRs, based on the carry cost and the Vasicek model. The hedging performance of the most realistic ex ante MVHR determination method is compared to, and found to be superior to, that of the traditional MVHR for the S&P 500 index, gold, and the EUR/USD exchange rate.

Dynamic hedging using the realized minimum-variance hedge ratio approach – Examination of the CSI 300 index futures

This paper investigates the dynamic hedging performance of the high frequency data based realized minimum-variance hedge ratio (RMVHR) approach. We comprehensively examine a number of popular time-series models to forecast the RMVHR for the CSI 300 index futures, and evaluate the out-of-sample dynamic hedging performance in comparison to the conventional hedging models using daily prices, as well as the vector heterogeneous autoregressive model using intraday prices. Our results show that the dynamic hedging performance of the RMVHR-based methods significantly dominates that of the conventional methods in terms of both hedging effectiveness and tracking error volatility in the out-of-sample forecast period. Furthermore, the superiority of the RMVHR-based methods is robust in different market structures and different volatility regimes, including China's abnormal market fluctuations in 2015 and the US financial crisis in 2008.

Dynamic Hedging Performance of the CSI 300 Index Futures - The Realized Minimum-Variance Hedge Ratio Approach

This paper comprehensively investigates the dynamic hedging performance of China’s CSI 300 index futures by using the realized minimum-variance hedge ratio (RMVHR) as an efficient way to utilize the high-frequency intraday information. We thoroughly examine a number of RMVHR-based time-series models for CSI 300 index futures, and evaluate the out-of-sample dynamic hedging performance in comparison to the conventional hedging models using daily prices, as well as the vector heterogeneous autoregressive model using five-minute prices. Our results show that the dynamic hedging performance of the RMVHR-based methods robustly dominates that of the conventional methods in terms of major performance measures including the hedge ratio, the hedging effectiveness, the portfolio return and the Sharp ratio in the out-of-sample forecast period. Furthermore, the superiority of the RMVHR-based methods is consistent during different volatility regimes of China’s financial markets, including China’s abnormal market fluctuations in 2015.

Long Term Hedging of the Australian All Ordinaries Index Using a Bivariate Error Correction FIGARCH Model

This article compares the performance of bivariate error correction GARCH and FIGARCH models when estimating long term dynamic minimum variance hedge ratios (MVHRs) on the Australian All Ordinaries Index. The paper therefore introduces the bivariate error correction FIGARCH model into the hedging literature, which to date has only employed the GARCH class of processes. This is important for those interested in managing long term equity exposures, given that FIGARCH processes exhibit long memory, whilst the GARCH class of processes exhibit short memory. The naive hedge ratio, the constant MVHR estimated via ordinary least squares (the OLS MVHR), the single period dynamic MVHR and the multi-period dynamic MVHR of Lee (1999) are considered. The results strongly support the estimation of dynamic MVHRs that allow for time varying correlations. Whilst long memory dependencies appear important, a multi-period dynamic MVHR that responds more rapidly to persistent changes in volatility dynamics requires development.

Hedging and speculative pressures and the transition of the spot-futures relationship in energy and metal markets

This paper examines the impact of hedging and speculative pressures on the transition of the spot-futures relationship in metal and energy markets. We build a Markov regime switching (MRS) model where hedging and speculative pressures affect the transition probabilities between a stronger and weaker spot-futures relationship. It is found that hedging pressure increases the likelihood of transition, i.e. destabilises the existing spot-futures relationship, while speculative pressure reduces it, i.e. stabilises the relationship, in the copper, crude oil and natural gas markets, but this effect is relatively weak in the silver and heating oil markets. We also examine whether these findings generate practical benefits by testing the hedging effectiveness of the minimum variance hedge ratios (MVH) derived from the MRS models with hedging and speculative pressures. A relatively strong reduction of the portfolio variance, hedger's utility and value at risk (VaR) is observed in the energy markets.

Realized hedge ratio: Predictability and hedging performance

This study explores the dynamic properties and predictability of the Realized Minimum Variance Hedge Ratio (RMVHR), constructed from five-minute spot and future returns of two stock indices and two exchange rates. A number of econometric models are employed to forecast directly the RMVHR and the out-of-sample performance is evaluated. Results from statistical measures suggest that the evolution of the realized hedge ratio series is predictable. In terms of risk reduction, we conclude that realized hedge ratio forecasts dominate conventional methods that use daily data while the benefit is pronounced when economic gains are considered. The superior performance of RMVHR methods holds across different asset classes but is more conspicuous in the case of stock indices. Finally, this study assesses the effect of sampling frequency and transaction costs.

Evaluating the Hedging Performance of Oil and Oilseeds Futures in India

The risk of commodity price volatility impacts almost everyone from individuals to companies and the economy as a whole. Hedging with derivative instruments is a way to insure against such risks. In the present study, the risk reduction function of commodity futures has been evaluated for commodities in the oil and oilseeds segment, namely, refined soya oil, mustard seed, mentha oil and crude palm oil (CPO). The choice of these commodities is made because in India, oilseeds’ trading has grown phenomenally due to massive increase in its domestic consumption which is being met by imports. To attain self-sufficiency and to enhance domestic production, besides other measures, commodity futures help to improve price realization and thereby encourage farmers to increase the area under cultivation. The minimum variance hedge ratio (MVHR) is calculated using the ordinary least square (OLS) method, OLS with additional variables and error correction model (ECM). Hedging effectiveness is quantified using MVHR. The estimates of hedging effectiveness are satisfactory for more liquid futures contracts of refined soya oil and CPO which gave variance reduction to the extent of 55 and 71 per cent, respectively. On the other hand, risk reduction with mustard seed and refined mentha oil futures is only around 28 per cent. According to market reports, the latter are narrow commodities with a small market size and greater susceptibility to price manipulation and cartelization. Measures, such as, better regulatory framework, wider participation of actual users in the commodity value chain and suitable contract specifications, are most essential for lucrative commodity markets.

Evaluation of realized multi-power variations in minimum variance hedging

This study investigated the hedging performance of realized multi-power variations under minimum variance strategy. The minimum variance hedge ratios are estimated by the realized DCC-GARCH model, and the risk and utility metrics are used to evaluate the performances of long and short hedge. The empirical results derived from the S&P 500 index demonstrated that the realized DCC-GARCH model with realized tri-power variation outperforms others in reducing risks, and generates largest economic benefits. While considering transaction costs, the superiority of the realized DCC-GARCH model with realized multi-power variations persists and produced less rebalancing costs than the realized DCC-GARCH model with realized variance.

European natural gas seasonal effects on futures hedging

This paper is the first to discuss the design of futures hedging strategies in European natural gas markets (NBP, TTF and Zeebrugge). A common feature of energy prices is that conditional mean and volatility are driven by seasonal trends due to weather, demand, and storage level seasonalities. This paper follows and extends the Ederington and Salas (2008) framework and considers seasonalities in mean and volatility when minimum variance hedge ratios are computed. Our results show that hedging effectiveness is much higher when the seasonal pattern in spot price changes is approximated with lagged values of the basis (futures price minus spot price). This fact remains true for short (a week) and long (one, three and six months) hedging periods. Furthermore, volatility of weekly price changes also has a seasonal pattern and is higher in winter than in summer. A simple volatility seasonal model that is based on sinusoidal functions on the basis improves the risk reduction obtained by strategies in which hedging ratios are estimated with linear regressions. Seasonal hedging strategies, linear regression based strategies, or even a naïve position, perform better than more sophisticated statistical methods.

European Natural Gas Seasonal Effects on Futures Hedging

This paper is the first to discuss the design of futures hedging strategies in European natural gas markets (NBP, TTF and Zeebrugge). A common feature of energy prices is that conditional mean and volatility are driven by seasonal trends due to weather, demand, and storage level seasonalities. This paper follows and extends the Ederington and Salas (2008) framework and considers seasonalities in mean and volatility when minimum variance hedge ratios are computed. Our results show that hedging effectiveness is much higher when the seasonal pattern in spot price changes is approximated with lagged values of the basis (futures price minus spot price). This fact remain true for short (a week) and long (one, three and six months) hedging periods. Furthermore, volatility of weekly price changes also has a seasonal pattern and is higher in winter than in summer. A simple volatility seasonal model that is based on sinusoidal functions on the basis improves the risk reduction obtained by strategies in which hedging ratios are estimated with linear regressions. Seasonal hedging strategies, linear regression based strategies, or even a naive position, perform better than more sophisticated statistical methods.