Hedging Price Risk Research Articles

Hedging price risk through commodity derivatives: a case of Sethi Jewellers

Learning outcomes After completion of the case study, the students will be able to understand the different risks associated with a business, focusing on price risk and the importance of price risk management in business; understand and evaluate the products available for hedging price risk through exchange-traded derivatives in the Indian scenario; and understand and evaluate the different strategies for price risk management through exchange-traded derivatives in the Indian scenario. Case overview/synopsis The case study pertains to a small business, M/s Sethi Jewellers. The enterprise is being run by Shri Charan Jeet Sethi and his son Tejinder Sethi. The business is located in Jain Bazar, Jammu, UT, in Northern India. The business was started in 1972 by Charan Jeet’s father. They deal in a wide range of jewelry products and are well-established jewelers known for selling quality ornaments. Tejinder (MBA in marketing) was instrumental in revamping his business recently. Under his leadership, the business has experienced rapid transformation. The business has grown from a one-room shop fully managed by Tejinder’s grandfather to a multistory showroom with several artisans, sales staff and security persons. Through his e-store, Tejinder has a bulk order from a client where the client requires him to accept the order with a small token at the current price and deliver the final product three months from now. Tejinder is in a dilemma about accepting or rejecting the large order. Second, if he accepts, should he buy the entire gold now or wait to buy it later at a lower price? He is also considering hedging the price risk through exchange-traded derivatives. However, he is not entirely sure, as he has a few apprehensions regarding the same, and he is also not fully aware of the process and the instruments he has to use for hedging the price risk on the exchange. Complexity academic level The case study is aimed to cater to undergraduate, postgraduate and MBA students in the field of finance. This case study can be used for students interested in commodity derivatives, risk management and market microstructure. Supplementary materials Teaching notes are available for educators only. Subject code CSS 1: Accounting and finance.

Cross-hedging wild salmon prices

A number of studies have documented that there is a global market for salmon, in which wild and farmed salmon have a common price determination process. Recent studies report that Norwegian farmed salmon spot prices are also highly correlated with the Fish Pool salmon future contract price, indicating that the futures market can be an important risk management tool, as producers and buyers can hedge price risk. Here, we investigate whether the wild salmon prices can be cross-hedged against the Fish Pool salmon future contract, by testing whether the two prices are correlated.. If so, the Fish Pool future contracts can be used as a risk management tool for fishers and buyers of wild salmon. We find that this is the case, providing an additional link between wild and farmed salmon specifically, and between fisheries and aquaculture generally.

Hedging Price Risk Using Contracts for Difference with Volume Uncertainty

Hedging Price Risk Using Contracts for Difference with Volume Uncertainty

The Climate Effect on Colombian Coffee Prices and Quantities Based on Risk Analysis and the Hedging Strategy in Discrete Setting Approach

This paper provides a risk-hedging strategy for coffee markets including climate impact in the context of Colombian Coffee producers, companies, regulators, and policymakers. From the intermediaries' perspective, we present a hedging price and quantity risks using financial instruments based on price and weather variables (El Niño and La Niña phenomena). The coffee price and quantities produced are mitigated by the inclusion of climatic variables in two ways: first, through analysing the changes observed in the forward curve against spot price measuring the deterministic effect, known in this market as the forward risk premium. Second, including the weather index in the hedge structure on price and quantity in the coffee market improves the agent's result; this latter aims to improve the hedging claim's performance due to the link between demanded volume and weather-linked index. An experiment shows the strategy profit over the best-performing claim price derived only and without hedging.

Hedging performance analysis of energy markets: Evidence from copula quantile regression

AbstractThis study investigates hedging performance with respect to different market structures for energy‐related commodities, including West Texas Intermediate crude oil, Brent crude oil, Chinese crude oil, and Heating oil. Copula quantile regression functions and the generalized autoregressive conditionally heteroscedasticity model are combined to analyze the nonlinear impact of dependence and the heterogeneous impact of market structure changes on hedging performance. Results show that hedging performance presents nonlinearity and market structure changes have surprisingly strong heterogeneous effects on the quantile hedge ratio, where bearish and bullish have lower hedge ratios than normal markets, which is captured better by Clayton copula quantile regression. Additionally, the trend of hedging effectiveness over different market structures also shows an inverted U shape. After changing data frequency or the types of futures contracts, the conclusions remain the same. Our empirical findings imply that hedgers are supposed to adjust the hedging number of futures according to market structure changes to hedge price risk effectively.

The Way Forward for Hedging Price Risk in Maize: A Price Discovery Mechanism

The Way Forward for Hedging Price Risk in Maize: A Price Discovery Mechanism

Economic imperatives of financialization of agricultural commodity markets

Purpose. The purpose of the article is to substantiate the theoretical and methodological foundations and economic feasibility of intensifying the processes of financialization of the business model of Ukrainian grain producers, in particular through the formation of the grain futures market and the creation of commodity exchange in Ukraine. 
 Methodology / approach. In the article, it is clarified the concept of financialization as one of the global trends of economic development that provides deep integration of financial and real economy on macro- and micro levels. Hedge effectiveness of price risks for Ukrainian grain (wheat and corn) using futures contracts traded on CME Group and Euronext is evaluated using regression analysis. Ukrainian wheat and corn FOB price volatility for 2016–2021 marketing years is estimated. The dynamics and volatility of the basis for Ukrainian wheat and corn and respective futures contracts traded on CME Group and Euronext are analyzed. 
 Results. The analysis of securitization index of global agricultural markets has shown an increase of its financialization rate. The main reasons for this process were the deepening of financial and commodity markets integration, deregulation of financial markets and introduction of new financial instruments. The structure of the grain market in Ukraine is analyzed, particularly the role of agricultural holdings and international companies. The tendencies and determinants of wheat and corn market prices in Ukraine during marketing year are revealed. Regression analysis of hedge effectiveness has shown that all considered futures contracts are suitable for wheat and corn CPT and FOB price hedging in Ukraine. The highest hedge effectiveness was revealed for Black Sea Corn Financially Settled (Platts) Futures) (CME Group) and Milling Wheat / Ble de Meunerie (Euronext Matif). Short hedge efficiency of CME Group futures contracts was supported by analysis of basis dynamics for Ukrainian wheat and corn and respective futures contracts. 
 Originality / scientific novelty. Based on the analysis of the theoretical foundations of financialization, the positive and negative consequences of the strengthening of financialization of global commodity markets and the peculiarities of the functioning of the modern grain market in Ukraine, the economic imperatives of the development of the Ukrainian market of grain derivatives, as an objectively determined trend of agricultural commodity market, are substantiated. The evaluation of the effectiveness of hedging price risks on the Ukrainian grain markets using derivatives traded on global exchanges has gained further development.
 Practical value / implications. It is proposed to intensify market tools usage for risk hedging in Ukraine in order to cover interests of all market participants. Risk management instruments available for Ukrainian agricultural producers are discussed.

The impact of price hedging on subsidized insurance: Evidence from Canada

AbstractThis article examines the effect of price hedging on publicly subsidized insurance. Theoretically, the effect of hedging is found to have an ambiguous effect on the incentive to purchase revenue insurance. A simulation is used to analyze the relationship between hedging and the Canadian AgriStability program in three regions in Saskatchewan. AgriStability is a margin insurance product, though in the simulation I hold input use constant, allowing the program to operate as if it were revenue insurance. Hedging is found to substantially reduce enrolment in AgriStability in each region. Furthermore, in some simulations the benefits that producers receive from AgriStability are smaller than the value of government subsidies. The results offer an explanation for low participation in AgriStability and show that the risk‐reducing effect of AgriStability is moderated by the ability of crop producers to hedge price risk through other means.

Real-time electricity price forecasting of wind farms with deep neural network transfer learning and hybrid datasets

The ability to forecast real-time electricity price for wind power is key to the operation of energy markets and hedging price risks. Recent research suggests new deep neural network (DNN) architectures can capture temporal dependencies in historical price data, along with the ability to automatically extract important features of the dataset. However, most existing price prediction DNN representations still utilize basic architecture designs and either no pre-training, or simple training approaches. This work studies both the effect of transfer learning on three network representations and different source domains, as well as the mechanism of transfer learning. It is shown that transfer learning improves accuracy across all network representations. The best performance is obtained with a GRU-based architecture, termed GRU-TL, that has been pre-trained from a hybrid dataset of all wind farms in the same subzone. This model outperforms all statistical and deep learning benchmarks by an average of 6.7% in the mean absolute percent error (MAPE) metric. The underlying mechanism of transfer learning enables the pre-trained DNN representation to learn the features of the target dataset more accurately.

How do firms attract the attention of individual investors? Shareholder perks and financial visibility

Shareholder perks are packages of benefits offered to shareholders who own a threshold number of firm shares. We test and confirm the hypothesis that shareholder perks programs in Japan provide mechanisms to increase firm visibility for individual investors through stock-price run-ups and increased trading volume just before the ex-perks date. First, firms having perks programs experience a significant price run-up toward the ex-perks date. Second, the observed price run-ups are significantly higher for stocks having tighter short-selling restrictions. Third, trading volumes and balances of short interests rise significantly just before the ex-perks date. The latter suggests active cross trades, wherein investors hedge price risk. Fourth, the results from the multivariate regression are also consistent with the hypothesis.

Hedging with commodity futures: evidence from the coffee market in Vietnam

In July 2018, the Vietnam Commodity Exchange (VNX) was transferred into the Mercantile Exchange of Vietnam (MXV) to hedge price risks through futures on international commodity exchanges. This research aimed to verify the efficiency of futures on ICE EU and ICE US under the perspective of hedging for Vietnamese coffee, determine optimal hedging ratios and the optimal number of each futures contract, and investigate the feasibility of introducing domestic commodity exchanges in Vietnam. Using the Vector Error Correction Model (VECM), the results show that (1) Robusta futures with expiration dates of January, March, May, and July on ICE EU are efficient hedging tools, but the adverse result is justified for Arabica futures on ICE US; (2) Robusta futures with the expiration date of January are the best in terms of risk management for Vietnamese coffee market; (3) optimal hedge ratio of Robusta futures of around 34% is much lower than ratios showed by previous researches; (4) in the short term, introducing coffee futures into the domestic commodity exchanges is still not feasible in the short term, but should be considered in the long term in Vietnam. This is the first study providing empirical evidence about the hedging role of futures contracts on ICE EU and ICE US, contributing to enrich the existing empirical evidence on the hedging role of futures for the agricultural sector.

Price Index Insurances in the Agriculture Markets

In this paper, we introduce price index insurances on agricultural goods. Seemingly similar to derivatives, there are significant differences between price index insurances and derivatives. First, unlike derivatives, there are no entrance barriers for purchasing insurances, making them the risk management tools that are accessible to almost all farmers. Second, since insurances are issued at a certain number for any individual farm, unlike futures, for example, they cannot be used for speculation and are used solely for hedging price risk. Third, unlike forwards, they are heavily regulated and do not default and cause counterparty risk. Besides all differences (or benefits), such products have just recently been introduced in the agricultural insurance market. In this paper, we investigate if there could have been a financially viable market where these products are traded. More precisely, we investigate if an insurance company can design a portfolio of optimal contracts that gives a higher Sharpe ratio than the financial market index prices (in our paper FTSE 100 and other three major indexes). To reach the paper's objective we take three steps, by considering theoretical, practical and corporation standpoints. In the first step, we will see how an optimal contract would look like from the demand side in a theoretical setup and we obtain the optimal contract from the farmers' standpoint. In the second step, by adopting a more practical approach, by meeting the Key Performance Indicators (KPI) requirements set by the market participants (both demand and supply side), we find the optimal policy specifications from the first step, in the market equilibrium. This step also helps to find some unobservable market parameters like volatility. Finally, by adopting a corporation standpoint we encounter our model to the UK farm index prices and find an optimal portfolio of the products on products from 10 commodities. We find out that investing in such a business is financially viable, as the optimal insurance portfolio produces a Sharpe ratio that outperforms FTSE 100 and other major market indexes.

Price Index Insurances in the Agriculture Markets

In this article, we introduce price index insurances on agricultural goods. Although these seem similar to derivatives, there are significant differences between price index insurances and derivatives. First, unlike derivatives, there are no entrance barriers for purchasing insurances, making them the risk management tools that are accessible to almost all farmers. Second, since insurances are issued in a certain number for any individual farm, unlike futures, for example, they cannot be used for speculation and are used solely for hedging price risk. Third, unlike forwards, they are heavily regulated and do not default and cause counterparty risk. In addition to all differences (or benefits), such products have just recently been introduced in the agricultural insurance market. In this article, we investigate if there could have been a financially viable market where these products are traded. More precisely, we investigate whether an insurance company can design a portfolio of optimal contracts that gives a higher Sharpe ratio than the financial market index prices (here, FTSE 100 and other three major indexes). To reach the article’s objective, we take three steps, in considering theoretical, practical, and corporation standpoints. In the first step, we show what an optimal contract would look like from the demand side in a theoretical setup and we obtain the optimal contract from the farmers' standpoint. In the second step, by adopting a more practical approach in meeting the Key Performance Indicators requirements set by the market participants (both demand and supply side), we find the optimal policies specifications from the first step, in the market equilibrium. This step also helps to determine some unobservable market parameters like volatility. Finally, by adopting a corporation standpoint we bring our model to the U.K. farm index prices and find an optimal portfolio of the products on products from 10 commodities. We demonstrate that investing in such a business is financially viable, as the optimal insurance portfolio produces a Sharpe ratio that outperforms the FTSE 100 and other major market indexes.

Electoral uncertainty and financial volatility: Evidence from two‐round presidential races in emerging markets

AbstractWe study how the predictability and the decisiveness of electoral outcomes affect financial volatility. We argue that traders’ optimal investment strategies depend on their ability to make accurate electoral forecasts and the prospective losses associated with placing a bet on the wrong candidate. Using a triple difference‐in‐difference approach and data from two‐round presidential elections in five Latin American countries between 1999 and 2018, we find that financial volatility is greatest in the days immediately following unpredictable, decisive, elections. Postelectoral volatility also occurs following predictable, indecisive elections. The effect of learning the identity of the winning candidate on financial volatility is null when the election is unpredictable and indecisive, as well as when the election is decisive, but the outcome is predictable. These findings offer insights into investors seeking to hedge price risk around elections. They also have important implications regarding the relationship between public opinion polls and postelectoral financial volatility.

The specialty coffee trading company (TSCT): future and option contracts

Learning outcomes The main pedagogical objectives of the case are: illustrate how Latin American companies dedicated to the production and harvesting of commodities can be vertically integrated to gain a larger share of the value created throughout the production chain. Understand how futures and options contracts in commodities can be used to hedge price risk on long and short positions in the underlying products. Understand how option contracts add value by hedging risk in those contexts where the counterparty has optionality. Discuss the implications of Fair Trade for commodity traders and producers. Case overview/synopsis In the case, Hernan Arosamena, CFO of The Specialty Coffee Trading Co. (TSCT), faces the challenge of designing an effective strategy to hedge the price risk caused by the increasing demand of the so-called Fair Trade coffee. Hernan Arosamena decides to review how the company has typically managed the price risk in its business transactions using future contracts to then incorporate the additional elements that trading Fair Trade coffee may entail. The typical price risk hedging strategy involves the use of coffee future contracts in long and short positions to ensure that the company obtains the desired margin in its coffee trading negotiations. To hedge the exposure to the risk of fluctuations in the price of coffee when the company sells Fair Trade coffee requires the additional use of put options. Complexity academic level The case is appropriate for students enrolled in courses or specialization programs at both the undergraduate and graduate levels. Supplementary materials Teaching notes are available for educators only. Subject code CSS 5: International Business.

Financial hedging in energy market by cross-learning machines

The recent price volatility in the energy market highlights the importance of financial hedging and the need of its incorporation into an investor’s set of portfolio strategies. Realizing the rapid advancement of artificial intelligence, our study empirically examines the use of machine learning models for hedging price risk associated with the holding of energy-based financial product. From a technical perspective, kernel regression and support vector machine are trained to estimate the time-varying optimal hedge ratio given the observed price movement and other factors. The estimated hedge ratio is then employed to guide the price hedging strategy of the crude oil contracts traded in the commodity exchanges. The two machine approaches’ hedging effectiveness against price risk is also compared with those of the un-hedged portfolio as well as a well-studied econometric time series approach. Our results indicate that the two forms of learning machines substantially outperform time series model and no-hedging over the out-of-sample period but neither machine dominates another across all testing scenarios. Given these mixed results between kernel regression and support vector machine, we propose and develop a kernel-supervised support vector machine to take advantage of the complementary nature of the two machine learning approaches and enhance the supervised learning process through hierarchical/sequential infusion of information. Cross-learning empirical testing shows that the proposed cross-learning machine is more effective in hedging than individual kernel regression and support vector machine. Furthermore, our study evaluates the impact of incorporating the coefficient of absolute risk aversion and the transaction costs to machine learning models. In general, cross-learning machine outperforms others in all tested scenarios.

How to hedge if the payment date is uncertain?

AbstractThis paper investigates how firms should hedge price risk when payment dates are uncertain. We derive variance‐minimizing strategies and show that the instrument choice is essential for this problem, similar to the choice between a strip and a stack hedge. The first setting concentrates on futures hedges, whereas the second allows for nonlinear derivatives. In both settings, firms should take positions in derivatives with different maturities simultaneously. We present an empirical analysis for commodities and exchange rates, showing that in both settings the optimal strategy clearly outperforms the commonly used heuristic strategies which consider only one hedging instrument at a time.

Hedging Efficiency of Gold Option Contracts in India

The main purpose of introducing gold option futures is because option futures on gold will be fundamentally used for hedging the risks associated with gold price changes. But there is a common question whether that gold option contracts are effective in hedging price. This paper will be the answer to that question. The paper aims to find the hedging efficiency of gold option contracts in Multi Commodity Exchange (MCX). The paper uses various tests like Augmented dickey fuller test (ADF) and Simple linear regressions (OLS model). The result of this paper shows the attractiveness of the gold derivative markets as a platform for hedging price risks. The paper also see that there should be adequate awareness needed for the major stakeholders in realising the major benefits of option contracts, there by using the exchange traded platforms and derivative instruments to its full potential.

Risk Sharing in Highly Incomplete Markets: The Role of Insurance Companies

This paper analyzes how insurance companies allow the issue of market incompleteness to be overcome. A general equilibrium economy is considered in which heterogeneous agents face endowment risks. Markets are incomplete: there are only markets for trading commodities ex-post and hedging price risks ex-ante. I introduce insurance companies supplying individual insurance contracts against endowment risks and sharing their uncertain profit through stock markets. I show that this restores Pareto optimality and generates the allocation obtained in the complete Arrow-Debreu setting despite drastically fewer markets. Substituting a tremendous amount of illiquid financial markets by insurance companies seems to be an effective way to implement risk management in the real world.

The Effects of Financial Crisis on Hedging Efficiency of Indian Rubber Future Markets

This research, under Engle-Granger Co-integration framework, examines the hedging efficiency of Indian rubber future markets during the period 2004-2017. The essence of this study is to seek evidence for the effects of global financial crisis of 2008 on the efficiency of rubber futures in hedging price risks of spot rubber in India. The study proved the hedging efficiency of rubber futures during both pre and post recession periods. However, increased price volatility of Indian rubber after recession heightened risk exposure to market participants that eventually lead to unexpected changes in the hedging efficiency of rubber futures. The research concludes with a suggestion that writing of rubber futures in India allows traders to hedge risk exposures in spot market along with the potentials of arbitrage gains.