Hedging Model Research Articles

Comparative analysis of futures contract cross-hedging effectiveness for soybean: models and insights

ABSTRACT The purpose of this research is to investigate the use of cross-hedging in the South African soybean market, by using the JSE BEAN contract as a cross-hedge instrument for the JSE SOYA contract. The research study involves a detailed assessment of the data, which includes the use of stationarity tests such as the Phillips-Perron and Augmented Dickey-Fuller, as well as the Johansen co-integration approach to analyse long-term relationships between the variables. To evaluate the efficiency of cross-hedging strategies, three distinct hedging models are used: the ordinary least squares (OLS) model, error correction model (ECM) and EC-GARCH. The results demonstrate that these models produce significant hedge ratios. Through back-testing and the application of hedging strategies, it is determined that the JSE BEAN contract holds the potential to serve as a cross-hedge for the JSE SOYA contract in the South African soybean market. This study provides useful information for market participants and model selection in the context of cross-hedging.

SARIMA Model-Based Monte Carlo Simulation of Option Contract Design for Maize Seasonal Heavy Precipitation in Shenyang

Agricultural production is highly dependent on weather conditions such as temperature, light, water and heat. The huge loss caused by extreme disaster weather makes the global insurance and reinsurance market overwhelmed, and seriously affects the enthusiasm of agricultural investment and the development of agricultural economy. With unique topography and vast territory, China is vulnerable to complex and diverse climate disasters. In recent years, the annual average economic loss caused by extreme weather disasters has reached about 200-300 billion yuan, among which floods caused by extreme heavy rainfall are the main agricultural disasters in China. In order to enhance the "farmer - insurance company - government" interest association to resist the risk of flood disaster caused by extreme heavy rainfall, financial hedging derivatives of weather disaster risk have gradually become a new hedging tool besides traditional insurance and reinsurance. This paper takes Shenyang, the main grain producing area in northeast China, as the sample of the study area, and uses the SARIMA model to obtain the distribution characteristics of seasonal rainfall time series. Different seasonal rainfall index option products (call option and put option contracts) are designed respectively, and the final option pricing is obtained by Monte Carlo stochastic simulation. The seasonal rainfall call option and put option contracts have opened up a new hedging model of agricultural extreme weather catastrophe outside the insurance and reinsurance market, which has enriched the varieties of weather financial derivatives market in China, reduced the severe impact of extreme weather disasters on agriculture, and enhanced the ability of agricultural stakeholders to resist the risk of extreme weather disasters.

Research on Hedging Ratio of Stock Index Futures to ETF Fund

Exchange Traded Funds (ETF) can largely avoid non-systematic risk, but investors often cannot avoid systemic risk, and the hedging function of stock index futures can do this. Therefore, hedging ETF with stock index futures has become a good investment strategy. Based on the trading data of the China Securities Index (CSI) 300 stock index futures and CSI 300 Exchange-Traded Fund (ETF), this paper analyzes the optimal hedging ratio of CSI 300 stock index futures by Ordinary Least Squares (OLS) and dynamic Error Correction Model - Generalized Autoregressive Conditional Heteroskedasticity (ECM-GARCH) model and compares the hedging performance predicted by the model to avoid the systematic risk of ETF. The results show that the hedging effect of the dynamic hedging model is better than that of the static model, and the ability to avoid systemic risk is also better. The predictions of both models show that stock index futures hedge ETFs very well. The dynamic model is more able to reduce the heteroscedasticity of the transaction data.

Optimal hedging efficiency in global freight markets: Comparing FFAs and time charter strategies

The paper aims to mitigate financial risk in highly volatile shipping freight markets by employing a dynamic hedging model. The primary criterion for evaluating the effectiveness of various methods for estimating optimal hedge ratios through Forward Freight Agreements (FFAs) is the minimum variance hedging rule. Four different methods are utilized to estimate two types of hedge ratios. The first type, a static hedge ratio, is calculated using the OLS and ECM methods. The second type, a time-varying hedge ratio, is determined through a bivariate GARCH model and a Rolling Window OLS method. Additionally, the hedging effectiveness of the traditional method of hedging, time chartering, is compared to the more modern and sophisticated shipping derivatives methods using the coefficient of variation. Highlights Estimating optimal hedge ratios through Forward Freight Agreements (FFAs) with the minimum variance hedging rule Four different methods are utilized to estimate two types of hedge ratios for static and dynamic hedging Assess the hedging effectiveness of the different methods of hedge ration estimation Compare the hedging effectiveness of the traditional method of hedging, time chartering, to the more modern and sophisticated shipping derivatives methods using the coefficient of variation

Hedging policy using neural networks and its combination with heuristic algorithms case study: Dez Reservoir

In recent years, the use of various Artificial Intelligence (AI) methods, such as evolutionary computation, heuristic algorithms, artificial neural networks, and fuzzy theory calculations, has gained popularity in addressing water resources issues. These algorithms have shown great success in solving problems that traditional deterministic methods struggle with. This study focuses on optimizing Dez reservoir operation over a long-term period using a nonlinear loss function through an evolutionary artificial neural network algorithm. The outcomes of this approach are then contrasted with genetic exploration and harmony search algorithms, highlighting the strengths and weaknesses of each method. Ultimately, a combination of the evolutionary artificial neural network method and hedging models is employed for optimal reservoir management, with results compared to the previous approach. Results show the appropriate performance of combining hedging policy with artificial neural network and harmony search algorithm. This combination significantly reduces the vulnerability value with a slight decrease in reliability.

Are Chinese live hog futures useful hedging tools?

ABSTRACT China’s newly listed live hog futures provide a standardized risk management tool for the hog market. Our paper provides the first systematic and quantitative evaluation of live hog futures’ hedging ability based on various traditional static and dynamic hedging models and an innovative quantile hedging model. Consistent results confirm that live hog futures are useful hedging tools. Specifically, the in-sample results of multiple traditional models show that the hedging ratios are around 0.4 and hedging effectiveness is 0.13. Out-of-sample hedging effectiveness increases significantly to 0.22. Quantile hedging results show more significant risk-management ability in bearish and average futures markets. We further compare live hog futures’ hedging effectiveness and market performance to other commodities. The comparison shows that the live hog futures market has modest hedging effectiveness and underperforms in liquidity, volatility, and basis. Following the empirical results, it is recommended to encourage hedging transactions, simplify trading processes, and enable night trading to enhance liquidity, lower volatility, and eventually improve hedging ability.

Time–frequency domain based optimization of hedging strategy: Evidence from CSI 500 spot and futures

Considering the significance of the frequency domain information in price series, we propose a decomposition-integration hedging model from the perspective of time–frequency domain to improve the hedging effectiveness and robustness. First, the returns series of spot and futures are decomposed and reconstructed into high frequency, medium frequency, and low frequency sequences to incorporate the frequency information into the hedging model. Then, we construct a model pool to fit the various characteristics of the data in each frequency domain and further extract the corresponding information. Finally, by taking four moments of investors risk appetite into consideration, the multi-objective optimization is employed to integrate the final hedging ratio. The empirical results indicate that the decomposition-integration hedging model is superior to the single time domain models in comprehensive evaluation indices. In addition, within a certain range of market fluctuations, the structure of our model has some characteristics that are compatible with reality. To recapitulate briefly, the hedging ratio of our model is mainly determined by the high frequency and medium frequency information under drastic market fluctuations. While when the market volatility is small, the hedging ratio largely depends on low frequency information.

Evaluating the Efficiency of Financial Assets as Hedges against Bitcoin Risk during the COVID-19 Pandemic

In the turbulent landscape of financial markets, Bitcoin has emerged as a significant focus for investors due to its highly volatile returns. However, the risks and uncertainties associated with it necessitate effective hedging strategies. This paper explores the potential of various financial assets, including interest rates, stock markets, commodities, and exchange rates, as dynamic hedges against Bitcoin’s risk. Utilizing a DCC-GARCH model, we construct a dynamic hedging model to analyze the viability of these financial assets as hedges. The data is categorized into pre-pandemic and pandemic periods to assess any change in hedging performance due to the outbreak of COVID-19. Our empirical findings suggest that the dynamic DCC-GARCH model outperforms the static OLS model in this context. During the pandemic period, a diverse set of financial assets demonstrated enhanced efficiency in hedging Bitcoin risk compared to the pre-pandemic phase. Among the hedging commodities, stock market indices, the US dollar index, and commodity futures displayed superior performance.

Data-driven hedging of stock index options via deep learning

We develop deep learning models to learn the hedge ratio for S&P500 index options from options data. We compare different combinations of features and show that with sufficient training data, a feedforward neural network model with time to maturity, the Black-Scholes delta and market sentiment as inputs performs the best in the out-of-sample test under daily hedging. This model significantly outperforms delta hedging and a data-driven hedging model. Our results also demonstrate the importance of market sentiment for hedging.

Causality-based drought propagation analyses among meteorological drought, hydrologic drought, and water shortage

Droughts propagate through the hydrologic cycle, leading to water deficiencies in various hydro-climate variables, such as rainfall, streamflow, soil moisture, and/or groundwater. Understanding drought propagation characteristics is an essential issue in water resources planning and management. This study aims to detect the causal relationships from meteorological drought to hydrologic drought and how these natural phenomena cause water shortage using CCM (convergent cross mapping). The causal influences among the SPI (standardized precipitation index), SSI (standardized streamflow index), and SWHI (standardized water shortage index) of the Nanhua Reservoir-Jiaxian Weir system located in southern Taiwan are identified based on 1960–2019 records. Since water shortages are influenced by reservoir operation models, three different models, the SOP (standard operating policy), RC (rule-curve-based model), and OPT (optimal hedging model), are considered in this study. The results reveal that clear and strong causality is observed between SPI and SSI for both watersheds. The causality of SSI-SWHI is stronger than that of SPI-SWHI, but both causalities are weaker than that of SPI-SSI. Among the three operation models, the no hedging SOP leads to the weakest causal links of SPI/SSI-SWHI, and the strongest causality is noted for OPT since the optimally derived hedging policy uses future hydrologic information. The CCM-based causal network of drought propagation reveals that the Nanhua Reservoir and Jiaxian Weir are equally important for water supplies since nearly identical causal strengths are observed in both watersheds.

A Leland model for delta hedging in central risk books

AbstractUsing a tractable extension of the model of Leland (1985), we study how a delta‐hedging strategy can realistically be implemented using market and limit orders in a centralized, automated market‐making desk that integrates trading and liquidity provision for both options and their underlyings. In the continuous‐time limit, the optimal limit‐order exposure can be computed explicitly by a pointwise maximization. It is determined by the relative magnitudes of adverse selection, bid–ask spreads, and volatilities. The corresponding option price—from which the option can be replicated using market and limit orders—is characterized via a nonlinear PDE. Our results highlight the benefit of tactical liquidity provision for contrarian trading strategies, even for a trading desk that is not a competitive market maker. More generally, the paper also showcases how reduced‐form models are competitive with “brute force” numerical approaches to market microstructure. Both the estimation of microstructure parameters and the simulation of the optimal trading strategy are made concrete and reconciled with real‐life high frequency data.

Comparative Study of Hedging Models in US

This study focuses on the use of three dynamic hedging models in the American stock market, and it computes the dynamic correlation coefficient, dynamic hedging ratio, and hedging effectiveness of three common models (DCC, BEKK, CCC). This paper fills in the gaps in the application and comparison of three hedging models commonly used in the US stock market during the pandemic. The results showed that NASDAQ index and futures were highly correlated and had significant volatility spillover effect before and during the epidemic. The hedging efficiency of the three models before the epidemic was higher than 0.931, and the hedging efficiency of the three models during the epidemic was higher than 0.936. It is proved that futures hedging is a good way to avoid risk no matter before or during the epidemic, and the highest hedging efficiency is dcc-garch.

The Philippines’ hedging strategy against China in the South China Sea dispute: Based on the human-ocean regional system

We constructed an analytical framework and geo-influence model for the South China Sea (SCS) disputes in the context of the human–ocean regional system. Specifically, we used the Goldstein score and the number of mentions of the events in the Global Database of Events, Language, and Tone to build a hedging model to portray the evolution of Philippine hedging against China and provided ideas for the response. The results show that, first, the complexity of natural, social, and relational attributes in the geographical system of human–ocean relations profoundly affect the Philippines’ threat perception of China, making it become the root cause of the hedging strategy. The geopolitical influence of the Chinese and American powers in the SCS region and the systemic pressure formed by their competitive game were the direct causes of the Philippines’ hedging strategy. Second, since the 21st century, the evolutionary features of the Philippine hedging pattern against China have been remarkable and can be divided into three phases: cooperative behavior over confrontational behavior (2001–2010), confrontational over cooperative behavior (2010–2016), and moderation of oppositional behavior and decline in cooperative behavior (2016–2022). Finally, China should continue to strengthen dialogue and cooperation with the Philippines in low-political areas such as marine scientific research, marine education, and marine environmental protection. Furthermore, China should shoulder its responsibilities as a major country, actively promote the “Code of Conduct Consultation in the South China Sea”, and continue to strengthen its own maritime strategic determination and planning capacity.

Research on the Applications of Neural Network Algorithms in Deep Hedging

Under market completeness assumptions, hedging a portfolio of derivatives is straightforward. In view of friction, transaction costs, liquidity and other factors, a framework is presented to extend the pricing and hedging with the hedging strategy treated as a neural network. We study the deep hedging model under incomplete market constraints such as frictions, traction cost, permanent impacts on the market and illiquidity. We discuss the limitations of certain models concerning the applications in deep hedging with constraints. After which, we analyse the advantages of different models and their joint models and find that the hedging strategy is close to the Black-Scholes delta hedging strategy. An example is also given when training after designing two hedging models. The Black-Scholes delta hedging is indeed approximated by unsupervised learning.

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.

Testing a profit margin hedging model as a price risk-management solution for crop farmers in South Africa

Risks inherent in the agricultural environment are often complex and include a combination of risks such as production risk (weather), market risk (uncertain prices), technological risks (new production techniques), price risk (changes in input and output prices) and institutional risk (changes in policies). Also, farmers experience personal and financial risks, which relate to cash flow and the ability of the business to generate sufficient profit. Literature shows that crop farmers manage price risk to fulfil the end-values of security, feeling good and operating a viable and competitive business. A profit margin hedging approach could effectively be used as a price risk management strategy. Although certain other agricultural industries are using profit margin hedging as a risk management strategy, not much is known of the usage and success of this strategy in crop farming. A gap in research exists where an effective plan combines the various components into an effective crop risk management strategy. It is also evident that a gap in practice exists where all the components identified in the literature are not combined into a working model to do better price risk management on a daily basis. Consequently, this study aimed to test the performance of such a new profit margin hedging (PMH) model, with and without using put options as a pre-harvest strategy, against the performance of a control group. The study was conducted in the Free State Province in South Africa. Data for eight (8) seasons of a group of ten farmers were obtained from a bureau service. The data was analysed using the software suite Jamovi 1.0.7.0. Independent t-tests were used to compare the profitability of the new PMH model to the control group. Statistically, significant differences were reported between the groups. The study found both a significant improvement in profitability and a stabilising effect on profitability when using a profit margin hedging approach. This study proposes that crop farmers can operate more viable and secure businesses by utilising a profit margin hedging model. Key words: agriculture marketing, crop farmers, price risk management, profit margin hedging

Investigating adaptive hedging policies for reservoir operation under climate change impacts

Concerns about whether the reservoirs against climate change will be able to fulfill their missions in the future have revealed the necessity of adapting their operations to changing circumstances. On the other hand, the fact that ensembles of hydrological climate-impact projections contain uncertainties originating from the general circulation models (GCMs) and the representative concentration pathway (RCP) scenarios is also an unavoidable challenge. This study, carried out to address these two issues together, corroborates a two-dimensional hedging model as an adaptive measure for climate change impacts and undertakes an uncertainty investigation over the Gordes reservoir in western Turkey. First, the statistically downscaled and bias-corrected meteorological data from twelve GCMs under two RCPs were transformed into inflow projections employing a hydrological modeling procedure for the period 2021–2050. Then, a parameterization-simulation–optimization framework imposed on adapting to all scenario-GCM variations was developed by integrating the hedging rule with the differential evolution algorithm. The results demonstrated that adaptive hedging policies mitigated potentially derived vulnerabilities from standard operating policies, ranging from 68% to 97%, and kept the sustainability index above 0.75 against climate change-induced water deficits. Finally, the decomposition of uncertainty contributions in reservoir operation optimization along the GCM-RCP-rule curve chain was made by the analysis of variances. It is obvious from the findings that the GCM was the foremost source of uncertainty in both predicted releases and key performance indices, while the uncertainty from the RCP-GCM interaction is of secondary importance. But a noteworthy detection is that the contributions of GCMs to the total variance for vulnerability and sustainability indices could be reduced by about 20% with a refined ensemble consisting of the outputs from four GCMs that better represent the local predictors. The interpretation of the uncertainty analysis approach, which was previously performed in the literature for runoff quantiles, at the reservoir scale as well as the reservoir operation optimization model, which has been verified to be viable, might be advantageous for water resources management.

Economic Representative Scenarios for Variable Annuity Dynamic Hedging of GMMB and GMDB

Variable annuities introduce significant risk for the insurers. To control this risk, insurers generally use dynamic hedging models. In practice, calculations for dynamic hedging models for variable annuities are computationally intensive since they require many nested stochastic scenario projections with outer and inner loops. In this article, we study the relationship between the hedging errors and the descriptive statistics of economic scenarios. Then, using the relationships between the hedging errors and the descriptive statistics of the economics scenarios, we present two novel scenario selection algorithms to determine economic representative scenarios (ERS) to run for dynamic risk hedging models. We do this for guaranteed minimum maturity benefit (GMMB) and guaranteed minimum death benefit (GMDB). We also benchmark these new algorithms against a control variate algorithm and Monte Carlo simulations. One of the two proposed novel scenario selection algorithms, the adapted Clara algorithm gives a lower mean absolute deviation of the conditional tail expectation (CTE) than the control variate method and Monte Carlo simulations for the same number of scenarios. Also, 500 representative scenarios selected by the adapted Clara algorithm generate a mean absolute deviation of the CTE95 of the hedging errors approximately equivalent to 1000 (or more in some cases) Monte Carlo simulations for 5 years GMMB and GMDB.

Production Planning with Risk Hedging Under a Conditional Value at Risk Objective

Effective management of demand risk is a central problem of production planning. In many applications, it is observed that demand often depends on certain asset prices in the financial market. This calls for a model that jointly optimizes the production decision along with a risk hedging policy, using the latter to compensate for loss of profit in scenarios of weak demand. In “Production Planning with Risk Hedging under a Conditional Value at Risk Objective,” Wang and Yao develop an integrated production and risk hedging model, where the risk hedging strategy is derived in explicit form and the production quantity is solved as a concave maximization problem. Furthermore, a complete characterization of the efficient frontier is provided, and the improvement over the production-only decision is quantified.

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.