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为促进食用植物油全国统一大市场建设，依据“一价定律”理论，基于期货价格数据，运用协整检验、向量误差修正模型、脉冲响应分析、方差分解分析等计量方法，分析食用植物油市场多种油品的均衡比价关系，并提出相关建议。结果表明：在期货价格引导下，通过套利机制的作用，多种食用植物油之间价格信息相互联通，存在长期均衡关系，多种油品的供给、需求、价格动态协调，共同构成一个统一大市场；从加强油脂油料期货市场建设、积极推动期货市场功能宣讲和利用、提升大豆和油料的国内生产能力、广泛挖掘多种食用植物油的供给潜力和有效维护进口环节经济利益等角度提出促进食用植物油全国统一大市场建设的建议。To promote the construction of a unified national market for edible vegetable oils, according to the theory of "one-price law", based on the data of the futures prices of edible vegetable oils, using cointegration test, vector error correction model, impulse response analysis, variance decomposition analysis were used to analyze the equilibrium price relationship between a variety of edible vegetable oils, and relevant suggestions were proposed. The results showed that it was interconnected that the price information between a variety of edible vegetable oils with the guidance of futures prices and the arbitrage mechanism, and they had a long-term equilibrium relationship. There was a dynamic coordination relationship between the supply, the demand and the price of a variety of vegetable oils, and they together formed a unified market. The suggestions such as strengthening the construction of the oils and the oilseeds futures markets, actively promoting the promotion and utilization of futures market functions, improving the domestic production capacity of soybean and oilseeds, widely tapping into the supply potential of various edible vegetable oils and effectively safeguarding the economic interests of the import link were put forward to promote the construction of a unified national market for edible vegetable oils.

This research paper delves into the substantial impact of commodity exchanges in India on the economy, specifically focusing on the areas of improved price discovery and the facilitation of systematic derivatives trading. Market participants, including hedgers and traders, effectively utilize futures contracts as strategic tools to control prices and capitalize on market volatility. Both agricultural and non-agricultural products are actively traded in the spot and futures markets, with metals emerging as the dominant category on the Multi Commodity Exchange (MCX). The process of price discovery within financial markets is a pivotal factor that ensures sustainable trading practices. Through an extensive study analyzing the relationship between spot and futures prices of seven metal derivatives traded on the MCX over a decade-long period, significant correlations have been identified across all cases. [ABSTRACT FROM AUTHOR]

We present a new term-structure model for commodity futures prices based on Trolle and Schwartz (2009), which we extend by incorporating seasonal stochastic volatility represented with two different sinusoidal expressions. We obtain a quasi-analytical representation of the characteristic function of the futures log-prices and closed-form expressions for standard European options' prices using the fast Fourier transform algorithm. We price plain vanilla options on the Henry Hub natural gas futures contracts, using our model and extant models. We obtain higher accuracy levels with our model than with the extant models. [ABSTRACT FROM AUTHOR]

A number of papers have dealt with commodity financialization finding strong evidence for its existence and its effect on commodity prices and volatility. We chose convenience yield (CY) to study the effect of commodity financialization based on the theory of storage and on the argument that CY resembles a call option. Using quarterly data in the period 1995–2018, on soybeans stocks, cash and futures prices, a dynamic Autoregressive Distributed Lag with Exogenous model is estimated to measure the effects of independent variables from both the financial and commodity markets on CY. The evidence reveals that financial markets volatility along macroeconomic global variables affect soybeans CY giving support to the existence of commodity financialization. Besides, we find a statistically significant and negative relation between volatility index and CY. Support for this evidence rests on the theory of storage, Real Option Analysis, and behavioral finance. [ABSTRACT FROM AUTHOR]

This article aims to examine the potential relationship between Brent crude oil futures prices and the index of the European renewable energy companies. After the overview of the European legislation and the most recent literature review on the topic, the article deploys a method of the Vector Autoregressive Model (VAR). The analysis includes weekly data over eight years (2015-2022). Our results indicate a positive correlation between Brent crude oil futures prices and the value of the European Renewable Energy Total Return (ERIX) index. The estimated bivariate VAR model indicates a statistically significant relationship, meaning that past values of the ERIX Index may be used to predict future Brent crude oil prices in the long run. Considering the most recent systemic disturbance in the world’s commodity market, future research should consider longer time series and possible relationships of other macroeconomic factors.

As an excellent category for futures trading, soybean meal futures have shown an orderly and positive development trend since its listing, which has been favored by many investors in futures market. Exploring linkage between soybean meal futures prices and spot prices is beneficial for reducing the impact of price fluctuations on soybean meal industry and providing support for stabilizing agricultural product futures market. Taking monthly data from August 2009 to August 2021 as samples, the paper uses VAR model, impulse response function and variance decomposition to test impact of soymeal spot price on futures price. The results showed that the response period of soybean meal futures price to spot price shock is 2 years, and the contribution rate of soybean meal spot price to futures price is 6.97% in the second year. The research shows that the spot price of soybean meal has a significant promoting effect on the futures price. [ABSTRACT FROM AUTHOR]

The objective of this paper was to determine whether the futures markets have a stabilising or destabilising impact on soybean's spot prices in North America. Directed acyclic graphs (DAGs) are used to test for causality between futures prices, spot prices and ending stocks, followed by time series econometric analysis. The DAGs point to the two‐way causal link between futures and spot prices and a lack of a causal link between inventory/stocks and spot price volatility. Time series results, including cointegration, vector error correction, impulse response and variance decomposition analysis, indicate a large impact from futures markets on the level and volatility of soybean spot prices in both the short and long run. These results have potentially important implications, as the impact of commodity price volatility is typically asymmetric across different actors. Farmers, for example, unlike speculators, utilise price risk management (PRM) instruments such as futures markets to mitigate price risks and appear to suffer from intensified volatility precisely because of their use of these instruments. Therefore, additional policies to cope with commodity price volatility, such as direct price controls or mitigation of consequences, can have critical stabilising functions supporting farmers' welfare and regional (rural) development. [ABSTRACT FROM AUTHOR]

The futures market's forecasts are significant to investors and policymakers, where the application of deep learning approaches to finance has received a great deal of attention. In this study, we propose a multivariate financial time-series forecasting method. Our model addresses the long- and short-term features, multimodal and non-stationarity nature of multivariate time-series by incorporating the improved deep neural networks and certified noise injection. Specifically, multimodal variational autoencoder is used to extract deep high-level features of multivariate time-series, Long- and Short- Term recurrent neural network is applied for multivariate time-series forecasting, and certified noise injection mechanism, inspired by differential privacy, is proposed to improve the robustness and accuracy of prediction. Extensive empirical results on real-world agricultural commodity futures price time series and relevant external data demonstrate that our model achieves better performance over that of several state-of-the-art baseline methods.

Commodity financialization has been a subject of discussion since the 2008 financial crisis. It is estimated that between 2003 and 2008, index investorsʼ positions increased from $13 billion to $317 billion. Surprisingly, most studies, predominantly based on Granger‐causality testing, find no relationship between financialization and commodity prices. We examine the effects of shocks to the common stochastic trends in the index positions, the spot and futures prices of Chicago corn and soybeans, WTI crude oil and Henry Hub natural gas. The results show that financialization has contributed to the price movements of these commodities. [ABSTRACT FROM AUTHOR]

The impact of speculative activities in financial markets on food-grain prices is a topical but debated issue. We consider four major food-grain commodities and several speculation measures–which represent different underlying definitions of speculation–in structural vector autoregression models to examine this issue empirically. Results show that the impact of speculation depends on the commodity in question (rice and wheat are generally less sensitive than corn and soybean) and the measure of speculation used. Further analysis shows that traders' behaviour in the futures market is different for wheat and rice, around 80% of which are directly used for human consumption, compared to corn and soybean, only around 15% of which are directly used as human food. We also show that the heterogeneous effect of different speculative measures is mostly due to their construction. Results are robust to various modifications of the models. From a policy perspective, we think a blanket regulation for all the commodities may not be optimal. [ABSTRACT FROM AUTHOR]

This paper proposes a sparse trading model of futures prices. The model considers that nearby futures contract with liquidity plays an important role in the price recovery, and allows that far futures contract with sparse trading uses the price of nearby futures contract as a source of information. Also, it is shown whether and how liquidity may well be an influential factor for futures prices in Chinese commodity futures markets. Empirical results show strong evidence for lead-lag information transmission for fuel oil, nature rubber, and soybean, which implies sparse trading effect on futures prices.

The objective of the paper is to determine if the futures prices of hard red spring wheat (HRSW) have stabilizing or destabilizing impact on spot HRSW price in North America. Several important results emerge from thorough empirical analysis. First, both Granger causality tests and directed acyclic graph algorithms (DAGs) point to two‐way causality between futures and spot HRSW prices and thus endogeneity in both prices formation. To the contrary, both procedures suggest that ending stocks are exogenous to spot and futures HRSW prices. Both vector error correction model and impulse response functions point to a large and long‐lasting impact of a shock to futures price on spot price level. Finally, variance decomposition analysis indicates that futures prices are responsible for the bulk of spot price volatility in both short and long run. Our result is consistent with those of theoretical models suggesting that when production (supply side) is the dominant disturbance, spot price is destabilized in both the short and the long run by futures prices. An important implication of this research is the need for alternative market mechanisms or alternative farm policy measures that would mitigate price risk and ensure sustainable farming of American HRSW farmers. [ABSTRACT FROM AUTHOR]

Global debates about determining the direction of relationship between commodity futures and spot prices reflect the importance of this issue. Resolving the debate will guide different economic agents to make correct decisions. The aim of this paper is to empirically investigate the direction of relationship between food spot and futures prices using different methodologies so as to resolve the debate. In addition, it is important to know which market could cause price volatilities to the other market, and thus, addressing price volatilities in a correct way. The paper conducts linear and non-linear Granger causality tests along with cointegration and error correction model and concludes with mixed findings. Specifically, linear and nonlinear Granger causality tests found evidence that food futures prices cause food spot prices. This suggests that food futures markets lead the price discovery process, and hence, the direction of information flows goes from food futures markets to food spot markets, and accordingly, any price volatilities in futures markets lead to price volatilities in spot markets. In contrast, the cointegration and error correction model found evidence that food spot prices cause food futures prices. This suggests that food spot markets lead the price discovery process, and hence, the direction of information flows goes from food spot markets to food futures markets, and accordingly any price volatilities in food spot markets lead to price volatilities in futures markets. Based on these differences in the obtained results, the current paper suggests the cointegration and error correction model is preferable since it provides a more formal framework for examining the short-run dynamics and testing for the equilibrium relationship among economic variables. Special attention to alternative instruments, such as the implementation of a global virtual reserve, should be highlighted so as to minimize speculative attacks and avoid excessive spikes of prices in spot and futures markets. This implies the importance of adopting the possible protectionist measures by developing countries in order to hedge against the negative reflections of global food price volatility. [ABSTRACT FROM AUTHOR]

We examine how consumers and financial markets in the United States react to two health warnings about mad cow disease: the first discovery of an infected cow in December 2003 and an Oprah Winfrey show on the potentially harmful effects that aired seven years earlier. Using a unique product-level scanner data set of a national grocery chain, we find a pronounced and significant reduction in beef sales following the first discovered infection, which dissipates slowly over the next three months. Cattle futures show a comparable pattern of abnormal price drops to the scanner data. Contracts with longer maturity show smaller drops, suggesting that the market anticipated the impact to be transitory. Cattle futures show abnormal price drops after the Oprah Winfrey show that are more than 50% of the drop following the 2003 discovery of an infected cow.

Even though commodity-pricing models have been successful in fitting the term structure of futures prices and its dynamics, they do not generate accurate true distributions of spot prices. This paper develops a new approach to calibrate these models using not only observations of oil futures prices, but also analysts' forecasts of oil spot prices. We conclude that to obtain reasonable expected spot curves, analysts' forecasts should be used, either alone or jointly with futures data. The use of both futures and forecasts, instead of using only forecasts, generates expected spot curves that do not differ considerably in the short/medium term, but long term estimations are significantly different. The inclusion of analysts' forecasts in addition to futures, instead of only futures prices, does not alter significantly the short/medium part of the futures curve but does have a significant effect on long-term futures estimations. [ABSTRACT FROM AUTHOR]

This article aims at exploring the performance of the price discovery function of cornstarch futures market in China. In order to test the stationarity of the cash and futures prices of cornstarch, the augmented Dickey–Fuller test is applied. Both prices are integrated of order one. Then, the Johansen cointegration test is conducted to test the cointegrating relationship between those two prices. Finally, the Granger causality test is performed to observe the direction of causality. The evidence shows that there is a long-run relationship between cash and futures prices and the futures price Granger causes cash price. As a whole, price discovery of cornstarch market in China is present although it is a newly emerged market. [ABSTRACT FROM AUTHOR]

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In the literature on modeling commodity futures prices, we find that the stochastic behavior of the spot price is a response to between one and four factors, including both short- and long-term components. The more factors considered in modeling a spot price process, the better the fit to observed futures prices—but the more complex the procedure can be. With a view to contributing to the knowledge of how many factors should be considered, this study presents a new way of computing the best number of factors to be accounted for when modeling risk-management of energy derivatives. The new method identifies the number of factors one should consider in the model and the type of stochastic process to be followed. This study aims to add value to previous studies which consider principal components by assuming that the spot price can be modeled as a sum of several factors. When applied to four different commodities (weekly observations corresponding to futures prices traded at the NYMEX for WTI light sweet crude oil, heating oil, unleaded gasoline and Henry Hub natural gas) we find that, while crude oil and heating oil are satisfactorily well-modeled with two factors, unleaded gasoline and natural gas need a third factor to capture seasonality.

Abstract: Contemporaneous observations on expected supply and on prices of post‐harvest futures contracts for corn are used to estimate expected demand relationships. These equations are used to estimate the prices of the post‐harvest contracts based on new supply estimates. Each estimate can be compared with a corresponding futures price, i.e. the market forecast. The differences help discern the market expectations about the expected demand for the new crop relative to historical experience, which can help support outlook analyses. We find that in recent years, a 100 million bushel change in the expected supply of corn results in about a 6 cent per bushel negative change in the price of December corn. The discussion also deepens understanding of the term ‘anticipatory prices’ as defined by Holbrook Working in his 1958 work. [ABSTRACT FROM AUTHOR]

The recent and rapidly growing interest in biofuel as a green energy source has raised concerns about its impact on the prices, returns and volatility of related agricultural commodities. Analyzing the spillover effects on agricultural commodities and biofuel helps commodity suppliers hedge their portfolios, and manage the risk and co-risk of their biofuel and agricultural commodities. There have been many papers concerned with analyzing crude oil and agricultural commodities separately. The purpose of this paper is to examine the volatility spillovers for spot and futures returns on bio-ethanol and related agricultural commodities, specifically corn and sugarcane. The diagonal BEKK model is used as it is the only multivariate conditional volatility model with well-established regularity conditions and known asymptotic properties. The daily data used are from 31 October 2005 to 14 January 2015. The empirical results show that, in 2 of 6 cases for the spot market, there were significant negative co-volatility spillover effects: specifically, corn on subsequent sugarcane co-volatility with corn, and sugarcane on subsequent corn co-volatility with sugarcane. In the other 4 cases, there are no significant co-volatility spillover effects. There are significant positive co-volatility spillover effects in all 6 cases, namely between corn and sugarcane, corn and ethanol, and sugarcane and ethanol, and vice-versa, for each of the three pairs of commodities. It is clear that the futures prices of bio-ethanol and the two agricultural commodities, corn and sugarcane, have stronger co-volatility spillovers than their spot price counterparts. These empirical results suggest that the bio-ethanol and agricultural commodities should be considered as viable futures products in financial portfolios for risk management. [ABSTRACT FROM AUTHOR]

The increasing volatility in electricity markets has reinforced the need for better trading strategies by both sellers and buyers to limit the exposure to losses. Accordingly, this paper proposes an electricity trading strategy based on a mid-term forecast of the average spot price and a risk premium analysis based on this forecast. This strategy can help traders (buyers and sellers) decide whether to trade in the futures market (of varying monthly maturity) or to wait and trade in the spot market. The forecast model consists of an Artificial Neural Network trained with the Long Short Term Memory architecture to predict the average monthly spot prices, using only market price-related data as input variables. Statistical analysis verified the correlation and dependency between variables. The forecast model was trained, validated and tested with price data from the Iberian Electricity Market (MIBEL), in particular the Spanish zone, between January 2015 and August 2019. The last year of this period was reserved for testing the performance of the proposed forecast model and trading strategy. For comparison purposes, the results of a forecasting model trained with the Extreme Learning Machine over the same period are also presented. In addition, the forecasted value of the average monthly spot price was used to perform a risk premium analysis. The results were promising, as they indicated benefits for traders adopting the proposed trading strategy, proving the potential of the forecast model and the risk premium analysis based on this forecast. [ABSTRACT FROM AUTHOR]

Analysis of prices and volatility plays an important role in coffee market, especially for developing countries, whose small producers and economies rely heavily on income generated by coffee trade. This study explores the impact of coffee crop reports on price volatility for coffee futures contracts during 2004-2014. Overall, results indicate that crop reports generally affect price volatility. The impact is particularly stronger when they provide information following the flowering periods in Colombia, Brazil, and Vietnam, world's major producers. [ABSTRACT FROM AUTHOR]

Oil producers are going through a hard period. They have a number of real options at their disposal. This paper addresses the valuation of two of them: the option to delay investment and the option to abandon a producing field. A prerequisite for this is to determine the value of a producing well. For this purpose we draw on a stochastic model of oil price with three risk factors: spot price, long-term price, and spot price volatility. This model is estimated with spot and futures West Texas Intermediate (WTI) oil prices. The numerical estimates of the underlying parameters allow calculate the value of a producing well over a fixed time horizon. We delineate the optimal boundary that separates the investment region from the wait region in the spot price/unit cost space. We similarly draw the boundary governing the optimal exercise of the option to abandon and the one governing the active/inactive production decision when there is no such option. [ABSTRACT FROM AUTHOR]

While in speculative markets forward prices could be regarded as natural predictors for future spot rates, empirically, forward prices often fail to indicate ex ante the direction of price movements. In terms of forecasting, the random walk approximation of speculative prices has been established to provide 'naive' predictors that are most difficult to outperform by both purely backward-looking time series models and more structural approaches processing information from forward markets. We empirically assess the implicit predictive content of forward prices by means of waveletbased prediction of two foreign exchange (FX) rates and the price of Brent oil quoted either in US dollars or euros. Essentially, wavelet-based predictors are smoothed auxiliary (padded) time series quotes that are added to the sample information beyond the forecast origin. We compare wavelet predictors obtained from padding with constant prices (i.e. random walk predictors) and forward prices. For the case of FX markets, padding with forward prices is more effective than padding with constant prices, and, moreover, respective wavelet-based predictors outperform purely backward-looking time series approaches (ARIMA). For the case of Brent oil quoted in US dollars, wavelet-based predictors do not signal predictive content of forward prices for future spot prices. [ABSTRACT FROM AUTHOR]

This article presents a method for measuring the functional efficiency of agricultural futures markets in terms of social welfare using a standard futures market structural model. Employing the concept of social surplus, it can be shown that, when futures prices are used to estimate future spot prices, the errors in prediction produce to some degree resource misallocation, which in turn results in welfare losses. Therefore, the social welfare associated with the presence of futures markets can be measured using a Social Loss index. The indicator was calculated for the period 1975-2015 and for several subperiods, which allow us to analyse functional efficiency before and after the 2007-2008 spikes in the prices of agricultural commodities. Futures contracts for 12 products are evaluated. The products are grouped in three different categories: 'soft products', 'livestock' and 'grains and oilseeds'. The results indicate that livestock contracts tended to be more efficient than the rest of the contracts during the whole period, but in 2008-2015 their efficiency decreased vis-á-vis the rest of the products. Nevertheless, 2008-2015 proved to be the most efficient subperiod, confirming the remarkable development of agricultural futures markets over time. [ABSTRACT FROM AUTHOR]

The equity derivatives market in India has undergone remarkable changes in terms of instruments introduced. Introduction of single stock futures, amidst great misgivings, was solely responsible for placing Indian exchanges in the topmost position in the global scenario. Till 2006-07, single stock futures were the most traded instruments in the Indian equity derivative segment. But, post-Global Financial Crisis, there has been a continuous drift in favour of index options from single stock futures. There has been a continuous decline in the share of single stock futures, the gain being that of index options. This is considered as a clear indication towards mature stock market. Even though the inception of derivative trading has significantly influenced the trading volatility in the capital market segment, it is yet to be seen whether the introduction of derivatives has achieved its purpose or not. The present study is an attempt to study the impact of volatility on the stock market after the introduction of derivatives in Indian segment. The study takes into account a period of thirteen years, from 9th November 2001 to 31st March 2014. A bunch of Nifty companies which satisfy the set criteria are selected for the study. The study reveals that there exists causality between the futures and spot prices of these companies. These companies are found to be co-integrated in the long run as well as in the short run. [ABSTRACT FROM AUTHOR]

American U.S. crude oil prices have dropped significantly of late down to a low of less than $30 a barrel in early 2016. At the same time price volatility has increased and crude in storage has reached record amounts in the U.S. America. Low oil prices in particular pose quite a challenge for the survival of U.S. America's tight-oil industry. In this paper we assess the current profitability and future prospects of this industry. The question could be broadly stated as: should producers stop operation immediately or continue in the hope that prices will rise in the medium term? Our assessment is based on a stochastic volatility model with three risk factors, namely the oil spot price, the long-term oil price, and the spot price volatility; we allow for these sources of risk to be correlated and display mean reversion. We then use information from spot and futures West Texas Intermediate (WTI) oil prices to estimate this model. Our aim is to show how the development of the oil price in the future may affect the prospective revenues of firms and hence their operation decisions at present. With the numerical estimates of the model's parameters we can compute the value of an operating tight-oil field over a certain time horizon. Thus, the present value (PV) of the prospective revenues up to ten years from now is $37.07/bbl in the base case. Consequently, provided that the cost of producing a barrel of oil is less than $37.07 production from an operating field would make economic sense. Obviously this is just a point estimate. We further perform a Monte Carlo (MC) simulation to derive the risk profile of this activity and calculate two standard measures of risk, namely the value at risk (VaR) and the expected shortfall (ES) (for a given confidence level). In this sense, the PV of the prospective revenues will fall below $22.22/bbl in the worst 5% of the cases; and the average value across these worst scenarios is $19.77/bbl. Last we undertake two sensitivity analyses with respect to the spot price and the long-term price. The former is shown to have a stronger impact on the field's value than the latter. This bodes well with the usual time profile of tight oil production: intense depletion initially, followed by steep decline thereafter. [ABSTRACT FROM AUTHOR]

Measuring the risk of investment projects involving commodities and modelling its price dynamics behaviour is usually implemented with Kalman filtering techniques. However, because the use of these techniques has high implementation requirements, recent literature has employed approximate models. This paper proposes a new and simpler spreadsheet implementation procedure which presents lower implementation requirements than the widely used Kalman filtering estimation procedure. The proposal needs to estimate fewer parameters than usual and does not directly estimate sequences but considers the relationship between the states implicitly when defining the regression matrices. This translates into a significant reduction in processing time. We apply the proposal to estimate the parameters of a 4-factor model for four commercial commodities: crude oil, heating oil, unleaded gasoline and natural gas; we then compare the accuracy with results using the Kalman filter method. Results indicate that error measurements are approximately equal for the actual model and the approximation proposed in this paper, for both the in- and out-of-sample data-sets. [ABSTRACT FROM PUBLISHER]

This work deals with issues relating to multiperiod hedging ratios (MHRs). First, we derive an analytical formula for the MHR starting from the triangular representation of a cointegrated system. Second, using both overlapping and non-overlapping price changes, we investigate the properties of OLS MHR. Third, we resort to simulated data to investigate the performance of MHR estimators. Unlike previous studies, we do not use real data whose data generating process is unknown; instead we run a Monte Carlo exercise to investigate estimators and compare them with theoretical measures. Finally, we apply our approach to real data for a hedging related to soft wheat. [ABSTRACT FROM AUTHOR]

The optimal management of a non-renewable resource extraction project is studied when input and output prices follow correlated stochastic processes. The decision problem is specified by two Bellman equations describing the project when it is currently operating or mothballed. Solutions are determined numerically using the Least Squares Monte Carlo methodology. The analysis is applied to an oil sands project which uses natural gas during extracting and upgrading. The paper takes into account the co-movement between crude oil and natural gas prices and proposes two price models: one incorporates a long-run link between the two while the other has no such link. Incorporating a long-run relationship between oil and natural gas prices has a significant effect on the value of the project and its optimal operation and reduces the sensitivity of the project to the natural gas price process. [ABSTRACT FROM AUTHOR]

Oil producers are going through a hard period. They have a number of real options at their disposal. This paper addresses the valuation of two of them: the option to delay investment and the option to abandon a producing field. A prerequisite for this is to determine the value of a producing well. For this purpose we draw on a stochastic model of oil price with three risk factors: spot price, long-term price, and spot price volatility. This model is estimated with spot and futures West Texas Intermediate (WTI) oil prices. The numerical estimates of the underlying parameters allow calculate the value of a producing well over a fixed time horizon. We delineate the optimal boundary that separates the investment region from the wait region in the spot price/unit cost space. We similarly draw the boundary governing the optimal exercise of the option to abandon and the one governing the active/inactive production decision when there is no such option.

It is commonly believed that the trading of futures on a commodity enables the market to overcome short selling constraints on the spot commodity itself. This belief is embedded in the notion that trading strategies involving futures contracts enable traders to replicate the payoffs as if they were short the spot commodity. The purpose of this paper is to investigate this common belief in a general arbitrage-free semimartingale financial model with trading in futures and a short selling prohibition on the spot commodity. We show via various examples that, in general, this common belief is incorrect. Furthermore, we provide a set of sufficient conditions, albeit very restrictive, under which the common belief is true. [ABSTRACT FROM AUTHOR]

We consider an arbitrage-free futures price model of Heath-Jarrow-Morton type which is driven by a multidimensional Wiener process and a marked point process. We find necessary and sufficient conditions for this model to produce a log futures curve that changes only through parallel shifts. The same analysis is carried out for the case when the log futures curve changes only through proportional shifts. We prove that there exist nontrivial parallel and proportional shifting log futures curves and we show how to specify the futures price model in order to obtain them. Additionally the shift functions are characterized. Finally, we consider the case of all other single-factor affine models which are neither parallel nor proportional shifting curves. We find necessary and sufficient conditions for the purely Wiener-driven log futures model to admit such other affine shifting curve and we characterize the shift functions. [ABSTRACT FROM AUTHOR]

Crop yield shocks are partially predictable--high planting-time futures prices have tended to indicate that yield would be below trend. As a result, regressions of total caloric production on futures prices produce estimates of the supply elasticity that are biased downwards by up to 75%. Regressions of the world's growing area on futures prices have a much smaller bias of about 20% because although yield shocks are partially predictable, this predictability has a relatively small effect on land allocation. We argue that the preferred method for estimating the crop supply elasticity is to use regressions of growing area on futures prices and to include the realized yield shock as a control variable. An alternative method for bias reduction is to use instrumental variables (IVs). We show that the marginal contribution of an IV to bias reduction is small--IVs are not necessary for futures prices in supply analysis. [ABSTRACT FROM AUTHOR]

The paper presents a framework to forecast futures prices of stocks listed on the National Stock Exchange (NSE) in India during normal (unaffected by the COVID-19 pandemic) and new normal times (affected by COVID-19 and a macroeconomic slowdown). The model leverages a structural model that determines the relevance of the explanatory features used in the study; namely, spot prices, market sentiment, sectoral outlook, historic and implied volatility, crude price volatility, and exchange rate volatility. The proposed Ensemble Feature Selection (EFS) methodology comprising Boruta and Regularized Random Forest (RRF) algorithms is used to screen the explanatory features. Two advanced Artificial Intelligence techniques—Regularized Greedy Forest (RGF) and Deep Neural Network (DNN)—are used in conjunction with Kernel Principal Component Analysis (KPCA) and Autoencoder (AE) for forecasting. To understand the extent and nature of the influence of the explanatory variables, the Explainable Artificial Intelligence (AI) approach has been used. Statistical checks confirm that our hybrid framework is effective. The results indicate that the relative importance of the explanatory variables in forecasting futures prices differs depending on the company concerned and the period under consideration. • Present a framework to forecast futures prices of Indian stocks during normal and new normal times. • Boruta and Regularized Random Forest algorithm have been adopted to screen the explanatory features. • RGF and DNN have been used in conjunction with KPCA and Autoencoder for forecasting. • XAI approach has been used to understand the extent and nature of influence of the explanatory variables. • The relative importance of the explanatory variables depends on the company concerned, and on the period under consideration. [ABSTRACT FROM AUTHOR]

American U.S. crude oil prices have dropped significantly of late down to a low of less than $30 a barrel in early 2016. At the same time price volatility has increased and crude in storage has reached record amounts in the U.S. America. Low oil prices in particular pose quite a challenge for the survival of U.S. America’s tight-oil industry. In this paper we assess the current profitability and future prospects of this industry. The question could be broadly stated as: should producers stop operation immediately or continue in the hope that prices will rise in the medium term? Our assessment is based on a stochastic volatility model with three risk factors, namely the oil spot price, the long-term oil price, and the spot price volatility; we allow for these sources of risk to be correlated and display mean reversion. We then use information from spot and futures West Texas Intermediate (WTI) oil prices to estimate this model. Our aim is to show how the development of the oil price in the future may affect the prospective revenues of firms and hence their operation decisions at present. With the numerical estimates of the model’s parameters we can compute the value of an operating tight-oil field over a certain time horizon. Thus, the present value (PV) of the prospective revenues up to ten years from now is $37.07/bbl in the base case. Consequently, provided that the cost of producing a barrel of oil is less than $37.07 production from an operating field would make economic sense. Obviously this is just a point estimate. We further perform a Monte Carlo (MC) simulation to derive the risk profile of this activity and calculate two standard measures of risk, namely the value at risk (VaR) and the expected shortfall (ES) (for a given confidence level). In this sense, the PV of the prospective revenues will fall below $22.22/bbl in the worst 5% of the cases; and the average value across these worst scenarios is $19.77/bbl. Last we undertake two sensitivity analyses with respect to the spot price and the long-term price. The former is shown to have a stronger impact on the field’s value than the latter. This bodes well with the usual time profile of tight oil production: intense depletion initially, followed by steep decline thereafter.

The hypothesis that speculative behaviour was the cause of the instability of commodity prices has brought renewed interest in futures markets. In this paper, the hedging effectiveness of European and US wheat futures markets were studied to test whether they were affected by the price instability observed after 2007. Indirectly, this could also be thought as a test of whether the increasing presence of speculators in futures markets have made them divorced from physical markets. A multivariate GARCH model was applied to compute optimal hedging ratios. No important evidence was found of a change in the hedging effectiveness after 2007. [ABSTRACT FROM AUTHOR]