Hedging Model Research Articles

Futures hedging in crude oil markets: A trade-off between risk and return

Risk and return are two fundamentals that have an impact on an investor’s or hedger’s investing choices. Based on the proposed synchronous movement intensity index, this paper aims to improve the hedging performance by adjusting the model-driven hedge ratio and realize the trade-off between return and risk in futures hedging. First, without loss of generality, we forecast crude oil spot and futures volatility using 10 GARCH-type models, including three linear models and seven nonlinear models, to obtain the ex-ante hedging ratio under the minimum variance framework. Then, we develop a novel and tractable method to identify the market state based on the index of consistency intensity, in which the index portrays the synchronous degree of stock price movements in the energy sector. Last but not least, we propose the hedge ratio adjustment criteria based on the identified state, and adjust the ratio driven by GARCH-type models of futures in accordance with the market state. Empirical results of crude oil futures markets indicate that the proposed state-dependent hedging model is superior to the commonly used models in terms of three criteria including mean of returns, variance, and ratio of mean to variance of returns for measuring hedging effect. We apply the DM test to make a statistical inference and discover that while the mean and the ratio of mean to variance of returns are increasing, the variance and hedging effectiveness of the hedged portfolio based on the modified methods are not significantly affected. Furthermore, the superiority of the proposed method is robust to different market conditions, including significant rising or falling trends, large basis, and COVID-19 pandemic. We also test the robustness of the proposed method with respect to the baseline model, quantile, and evaluation window. Overall, this paper provides a more realistic approach for crude oil risk managers to hedge crude oil price risk, some corresponding implications are also concluded.

A Joint Dispatch Operation Method of Hydropower and Photovoltaic: Based on the Two-Stage Hedging Model

The randomness and volatility of large-scale clean energy output represented by wind power and photovoltaic lead to difficulties in grid connection. The problems of abandoned wind, light, and water become increasingly prominent. The adjustment capacity of traditional thermal power is limited and it is difficult to ensure the consumption of high proportion clean energy. On this basis, the marginal benefit hedging rule in economics is introduced into the hydropower and photovoltaic joint operation system in this paper. A two-stage spatio-temporal hedging strategy is designed to solve the spatio-temporal conflict problem in the hydropower and photovoltaic joint system. The multi-objective joint dispatching model of hydropower and photovoltaic system considering system benefits, risks, and stability is established, which can be solved by a MOEA/D-GABS algorithm with selection strategy. The joint system scheduling schemes under different schemes are analyzed by case. The results demonstrate that, compared with the traditional multi-objective decision-making scheme, the flood control risk in each period of the reservoir in the proposed method is controlled to be no more than 1.63 × 10−3 (the flood control standard corresponding to the 50-year flood control risk is 0.006); the flood limit water level of the reservoir is increased from 583.00 m to 583.70 m, which improves the benefit of the reservoir; and the water utilization rate is effectively improved. On the other hand, compared with the traditional scheme, the proposed method reduces the peak valley difference of the combined system by 50.67% and 59.68% in typical sunny and cloudy scenarios, respectively, which greatly reduces the uncertainty of photovoltaic output, and the stability of the combined system is improved. It is shown that the proposed method can be used to guide the economic dispatch of a complementary system with hydropower as the regulating energy.

Establishing effective warning storage to derive optimal reservoir operation policy based on the drought condition

The conventional discrete hedging model is connected to a drought early warning system (DEWS) and a new concept called effective warning storage (ES). To prepare the desired model, an optimization model (genetic algorithm method) is connected to a simulation model to simultaneously search for threshold values (rule curves) and rationing coefficients as well as ES-related coefficients in the reservoir system of the Jarreh Dam located on the Zard River in southwestern Iran. The advantage of this method over conventional hedging models is that it responds earlier to drought periods and starts rationing earlier. With the application of the proposed rationing model, the objective function i.e., modified shortage index (MSI) in the historical period is better optimized about 6 % than the conventional rationing method, which shows the superiority of the new model in adjustment of large deficiencies and its distribution in other normal periods. The impact of the proposed method on the objective function in the future period is found to be small (about 1 %). It is mainly due to the drastic reduction of inflow to the reservoir in the future period (both in scenarios RCP4.5 and RCP8.5), so that the needs won't be fully met in most of the months. The results show that with the application of the proposed hedging policy, the objective function (MSI) in the historical period and also in the scenario of RCP4.5 and RCP8.5 is improved by 45 %, 21.3 % and 25.7 %, respectively, compared to the standard operation policy (SOP) method. Also, with the application of the hedging policy, the vulnerability of the system has been greatly improved in both the historical and future periods compared to the SOP method. System vulnerability decreases from 0.64 to 0.24 in the historical period while it changes from 0.62 to 0.44 and from 0.6 to 0.41 for the RCP4.5 and RCP8.5 scenarios.

Symmetry Analysis of a Model of Option Pricing and Hedging

The Guéant and Pu model of option pricing and hedging, which takes into account transaction costs, and the impact of operations on the market is studied by group analysis methods. The infinite-dimensional continuous group of equivalence transforms of the model is found. It is applied to get the group classification of the model under consideration. In addition to the general case, the classification contains three specifications of a free element in the equation, which correspond to models with groups of symmetries of a special kind. Optimal systems of subalgebras for some concrete models from the obtained classification are derived and used for the calculation of according invariant submodels.

State-dependent hedge strategy for crude oil spot and futures markets

Relying on the hidden Markov model improved by the particle swarm optimization algorithm (PSO-HMM), we develop a dual-decision method to address the issue of state-dependent futures hedging. Our approach is attractive in two ways. First, it uses the PSO algorithm to overcome the shortcomings of the traditional algorithm, which can easily fall into the local optima to estimate parameters in a hidden Markov mode. Second, this paper proposes a new hedge position adjustment method based on the identified market states, instead of sticking to the hedge position calculated by the commonly used GARCH-type models to achieve a better trade-off between risk hedging and return acquisition. Specifically, we first improve the accuracy of parameter measurement and employ the PSO-HMM to identify two market states, bear and bull, and fully illustrate the rationality and effectiveness of the proposed model. Based on the market states identified, we then adjust the hedge ratio estimated by GARCH-type models and compare the hedging effects of no hedge, model-driven, and state-dependent strategies. Our empirical results show that the PSO-HMM method can improve the accuracy of state identification over the classical HMM. The market state-dependent hedging strategy has better performance than other strategies when it comes to the trade-off between the return and the risk of a hedged portfolio. Furthermore, robustness checks under different conditions confirm that the state-dependent hedging strategy outperforms the model-driven hedging and no hedge strategies. Thus, our research sheds new light on conventional hedging models.

A novel hybrid strategy for crude oil future hedging based on the combination of three minimum-CVaR models

Effective and robust crude oil hedging strategies are becoming increasingly important for investors. However, due to the differences between data characteristics, the choice of model usually has a significant impact on hedging performance, and an incorrect model can make the hedging performance less efficient and robust. In this paper, a novel hybrid hedging model is proposed to reduce the model uncertainty. In detail, different hedging models are combined to construct a hybrid. Then, the maximum percentage reduction of the hybrid is selected as the optimization target to derive optimal hedge ratios and combination weights. The essence of the hybrid hedging model is to choose a better one among different strategies. In the empirical analysis, we construct the hybrid model by combining three single models: parametric, nonparametric, and semiparametric minimum-CVaR hedging models. The empirical results show that the novel hybrid model significantly outperforms other four competitive models in return, Sharpe ratio, maximum drawdown and downside volatility, while slightly outperforms other competitors in CVaR reduction. The robustness tests by changing the window width, confidence level, data frequency, empirical data, and objective function to be optimized validate the above conclusions.

What are reference rates for?

Reference rates like LIBOR were designed to reflect banks’ full cost of funds, including credit premia. With publication of LIBOR expected to cease soon, regulators have recommended a shift to risk-free reference rates. To study the implications of this shift, this paper develops a tractable model of bank lending and hedging in which the design of reference rates shapes the allocation of risk in equilibrium. Reference rates that capture credit risk, but are sufficiently free from manipulation, could improve welfare relative to risk-free reference rates. It remains an open question whether such reference rates can be constructed.

Leveraged Exchange-Traded Funds with Market Closure and Frictions

Although leveraged exchange-traded funds (ETFs) are popular products for retail investors, how to hedge them poses a great challenge to financial institutions. We develop an optimal rebalancing (hedging) model for leveraged ETFs in a comprehensive setting, including overnight market closure and market frictions. The model allows for an analytical optimal rebalancing strategy. The result extends the principle of “aiming in front of target” introduced by Gârleanu and Pedersen (2013) from a constant weight between current and future positions to a time-varying weight because the rebalancing performance is monitored only at discrete time points, but the rebalancing takes place continuously. Empirical findings and implications for the weekend effect and the intraday trading volume are also presented. This paper was accepted by Agostino Capponi, finance. Funding: M. Dai acknowledges support from the National Natural Science Foundation of China [Grant 12071333], the Hong Kong Polytechnic University [Grant P0039114], and the Singapore Ministry of Education [Grants R-146-000-243/306/311-114 and R-703-000-032-112]. H. M. Soner acknowledges partial support from the National Science Foundation [Grant DMS 2106462]. C. Yang acknowledges support from the Chinese University of Hong Kong [Grant 4055132 and a University Startup Grant]. Supplemental Material: Data and the online supplement are available at https://doi.org/10.1287/mnsc.2022.4407 .

A Minimum Variance Hedging Ratio Model Based on Nonlinear Grey Classification Model

The risk transfer function of futures market is mainly realized by hedging strategy. Futures price yield and spot price yield tend to show different fluctuations before and during hedging, which leads to the distortion of hedging ratio, that is, the calculated hedging effect is weaker than the traditional hedging effect. On the basis of MV (minimum variance) hedging model, this paper introduces NGCM (nonlinear grey classification model) to solve the nonlinear correlation between futures and spot returns, which can improve the hedging effect. The results show that, due to the existence of basis, the price change model violates the linear assumption of OLS (ordinary least squares) parameters, and there is a problem of model missetting. It is estimated that HR (hedging ratio) should choose the price model, which can better depict the linkage between futures price and spot price. The effectiveness of HR in this study is higher than that of existing models. Applying this model to hedge can effectively avoid the spot price risk. Investors can reasonably choose the hedging model according to their own needs.

Estimating the Optimal Hedge Ratio and the Hedge Effectiveness of LNG Contracts in Asia

Purpose This study examines the effectiveness of hedging Japan/Korea Marker (JKM) markets by estimating the optimal minimum variance hedge ratio with various models. Design/Methodology/Approach In this study, the LNG Japan/Korea Marker (JKM), which is published by Platts, was used over the period of January 02, 2014 to July 12, 2019. Both constant and dynamic hedge ratios were found using various methods including OLS, VECM, BEKK-GARCH, and the MRS model. Estimated hedge ratios through an out-of-sample analysis were evaluated through hedge effectiveness (HE). Findings Basically, the empirical results indicate that hedging effectiveness seems low in both the in-sample and out-of-sample periods. The OLS model slightly outperforms time-variant models, but generally there is no significant gap between models. It is worth noting that although OLS, VECM and BEKK-GARCH models hold relatively stable outcomes in both horizons, the MRS model experiences a large gap between 14.85% and 5.90%. Research Implications The results do not show a high hedging effectiveness for several reasons. However, considering most of studies on this issue focus on popular gas indices such as NBP, TTF and HH, this study makes a valuable contribution to the research of Asian LNG markets by comparing various hedging models for the first time.

Cobertura de Volatilidad del Precio de la Electricidad Aplicando la Descomposición Estacional y de Tendencia

The Wholesale Electricity Market (MEM) has allowed participants to trade electricity at Local Marginal Price (LMP); therefore, developing hedging models to face high volatility electricity prices and avoid financial losses has become essential. This work proposes a methodology based on the Seasonal and Trend Decomposition Model (STL) to the LMP returns series, which is fitted into NIG distribution by obtaining empirical NIG parameters from LMP returns using Maximum Likelihood Estimation (MLE) to generate a simulated NIG distributed series. Finally, the goodness-of-fit test is estimated to demonstrate that empirical data can be fitted into NIG Distribution. This work should be considered the first Electricity Hedging Valuation Methodology for the MEM. Results obtained show that electricity price returns can be fitted and simulated by NIG distribution even through economic crisis periods. The analysis period is from 29/01/2016 to 09/07/2021.JEL Classification: C15; G10; O13; P18; Q47.

A Leland Model for Delta Hedging in Central Risk Books

A Leland Model for Delta Hedging in Central Risk Books

Learning sequential option hedging models from market data

Following a direct data-driven approach, we propose a robust encoder-decoder Gated Recurrent Unit (GRU), GRU δ , for optimal discrete option hedging. The proposed GRU δ utilizes the Black-Scholes model as a pre-trained model and incorporates sequential information and feature selection. Using the S&P 500 index European option market data, we demonstrate that the weekly and monthly hedging performance of the proposed GRU δ significantly surpasses that of the data-driven minimum variance (MV) method, the regularized kernel data-driven model, and the SABR-Bartlett method. In addition, the daily hedging performance of the proposed GRU δ also surpasses that of MV methods based on parametric models, the kernel method, and SABR-Bartlett method.

Hedging Bitcoin with conventional assets

The recent 50% drop in the price of the flagship cryptocurrency Bitcoin reinforces the persistent anxiety among cryptocurrency investors. Can alternative assets hedge Bitcoin risk? This study investigates the ability of equities, commodities, bonds, currencies, and VIX futures to hedge Bitcoin. Our in-sample analysis shows that the USDX, Gilt, Australian dollars, wheat, cocoa, cotton, sugar, copper, and lean hog can hedge Bitcoin, and the out-of-sample analysis reveals that the DAX, Dow-Jones, Nikkei, S&P 500, Brent, and WTI futures can be effective hedging instruments. We use a wavelet-based dynamic hedging model to account for heterogeneous investors in the Bitcoin market. For a short-term horizon, soybean futures reduce the variance in the in-sample hedged portfolio, and cotton futures offer the highest out-of-sample utility. Copper futures are the best for in-sample hedging in a long-term horizon, whereas live cattle futures have the best out-of-sample performance. These results show that conventional assets can hedge wild swings in Bitcoin.

Risk hedging strategies for electricity retailers using insurance and strangle weather derivatives

With the increase of extreme weather events and the penetration of distributed energy resources, electricity retailers will encounter more risks at both transmission and distribution levels during the business operation process. For risks at the transmission level, huge damages to the transmission lines and towers caused by extreme events, like bushfires, ice storms, and flooding, will lead to power shortage. For risks at the distribution level, demand variations in accordance with temperature change will result in energy procurement difficulty for the retailers. In this paper, besides the normal bilateral contract, the insurance, the strangle weather derivatives, and the energy storage system are implemented to hedge the risks at both the transmission and distribution levels. Simulation results show that the proposed model ensures higher profits for the retailers in summer and winter compared to the conventional model when there are no extreme events occurring. When there are extreme events in both summer and winter, the proposed model incurs lower reduction of profits than that of the conventional model. In brief, the overall profits of the retailer using the proposed hedging model are higher than the convention model, and the overall profit variation of the conventional model is about 26% higher than the proposed model. Furthermore, when the budget of the retailer is sufficient, all three hedging tools can be invested. Whereas when the budget of the retailer is limited, the investment order should be insurance the first, strangle weather derivatives the second, and energy storage system the third.

Customized yet Standardized Temperature Derivatives: A Non-Parametric Approach with Suitable Basis Selection for Ensuring Robustness

Previous studies have demonstrated that non-parametric hedging models using temperature derivatives are highly effective in hedging profit/loss fluctuation risks for electric utilities. Aiming for the practical applications of these methods, this study performs extensive empirical analyses and makes methodological customizations. First, we consider three types of electric utilities being exposed to risks of “demand”, “price”, and their “product (multiplication)”, and examine the design of an appropriate derivative for each utility. Our empirical results show that non-parametrically priced derivatives can maximize the hedge effect when a hedger bears a “price risk” with high nonlinearity to temperature. In contrast, standard derivatives are more useful for utilities with only “demand risk” in having a comparable hedge effect and in being liquidly traded. In addition, the squared prediction error derivative on temperature has a significant hedge effect on both price and product risks as well as a certain effect on demand risk, which illustrates its potential as a new standard derivative. Furthermore, spline basis selection, which may be overlooked by modeling practitioners, improves hedge effects significantly, especially when the model has strong nonlinearities. Surprisingly, the hedge effect of temperature derivatives in previous studies is improved by 13–53% by using an appropriate new basis.

Corporate Hedging Models: A Review

Corporate Hedging Models: A Review

Dynamic Global Currency Hedging

Abstract This article proposes a model for discrete-time currency hedging based on continuous-time movements in portfolio and foreign exchange rate returns. The vector of optimal currency exposures is given by the negative realized regression coefficients from a one-period conditional expectation of the intraperiod quadratic covariation matrix for portfolio and exchange rate returns. Empirical results from an extensive hedging exercise for equity investments illustrate that currency exposures exhibit important time variation, leading to substantial volatility reductions when hedging, without sacrificing returns. A risk-averse investor is willing to pay several hundred annual basis points to switch from existing hedging methods to the proposed dynamic strategies.

A verification model to capture option risk and hedging based on a modified underlying beta

The mining and hedging of option volatility information are the core issues of stock option markets. This paper analyzes the relationship between option risk and expected return from the perspective of the underlying beta, and estimates the degree of correlation. As the assumptions of the capital asset pricing model and Black–Scholes model are not consistent with the actual situation in the financial market, we use applied statistical models to introduce kurtosis and skewness, and to introduce curvature and high-order-moment error terms to optimize the underlying beta model. We then develop a verification model for mining option risk and hedging by employing the modified underlying beta. We verify the hedging performance of the above model by choosing different market samples, such as the China, Hong Kong and US financial markets. The results show that the hedging performance of the optimized underlying beta model in the US market is most satisfactory, followed by the Hong Kong market and then the Chinese mainland market. Meanwhile, the hedging effect of the underlying beta model improved by curvature and high-order-moment error terms is superior to that of the model of the underlying beta adjusted by the kurtosis and skewness.

An Equilibrium Model of Imperfect Hedging with Transaction Costs

An Equilibrium Model of Imperfect Hedging with Transaction Costs