VOLATILITY SPILLOVERS BETWEEN FOREIGN EXCHANGE, COMMODITY AND FREIGHT FUTURES PRICES: IMPLICATIONS FOR HEDGING STRATEGIES

In many studies the assumption is made that traders only encounter one type of price risk. In reality, however, traders are exposed to multiple price risks, and often have several relevant derivative instruments available with which to hedge price uncertainty. In this study, commodity, foreign exchange, and freight futures contracts are analyzed for their effectiveness in reducing price uncertainty for international grain traders. A theoretical model is developed for a representative European importer to depict a realistic trading problem encountered by an international grain trading corporation exposed to more than one type of price risk. The traditional method of estimating hedge ratios by Ordinary Least Squares (OLS) is compared to the Seemingly Unrelated Regression (SUR) and the multivariate GARCH (MGARCH) methodology, which takes into account time-varying variances and covariances between the cash and futures markets. Explicit modeling of the time-variation in futures hedge ratios via the MGARCH methodology, using all derivatives and taking into account dependencies between markets results in a significant reduction in price risk for grain traders. The results also confirm that the unique, but underutilized, freight futures market is a potentially useful mechanism for reducing price uncertainty for international grain traders. The research undertaken in this study provides valuable information about reducing price uncertainty for international grain traders and gives a better understanding of the linkages between closely related markets.hedging, multivariate GARCH, foreign exchange, freight and commodity futures, Financial Economics, International Relations/Trade,

VOLATILITY SPILLOVERS BETWEEN FOREIGN EXCHANGE, COMMODITY AND FREIGHT FUTURES PRICES: IMPLICATIONS FOR HEDGING STRATEGIES

In many studies the assumption is made that traders only encounter one type of price risk. In reality, however, traders are exposed to multiple price risks, and often have several relevant derivative instruments available with which to hedge price uncertainty. In this study, commodity, foreign exchange, and freight futures contracts are analyzed for their effectiveness in reducing price uncertainty for international grain traders. A theoretical model is developed for a representative European importer to depict a realistic trading problem encountered by an international grain trading corporation exposed to more than one type of price risk. The traditional method of estimating hedge ratios by Ordinary Least Squares (OLS) is compared to the Seemingly Unrelated Regression (SUR) and the multivariate GARCH (MGARCH) methodology, which takes into account time-varying variances and covariances between the cash and futures markets. Explicit modeling of the time-variation in futures hedge ratios via the MGARCH methodology, using all derivatives and taking into account dependencies between markets results in a significant reduction in price risk for grain traders. The results also confirm that the unique, but underutilized, freight futures market is a potentially useful mechanism for reducing price uncertainty for international grain traders. The research undertaken in this study provides valuable information about reducing price uncertainty for international grain traders and gives a better understanding of the linkages between closely related markets.hedging, multivariate GARCH, foreign exchange, freight and commodity futures, Marketing, F3, C3, G1,

Price volatility forecasts for agricultural commodities:an application of volatility models,option implieds and composite approaches forfutures prices of corn and wheat

There has been substantial research effort aimed to forecast futures price return volatilities of financial and commodity assets. Some part of this research focuses on the performance of time-series models (in particular ARCH models) versus option implied volatility models. A significant part of the literature related to this topic shows that volatility forecast accuracy is not easy to estimate regardless of the forecasting model applied. This paper examines the volatility accuracy of volatility forecast models for the case of corn and wheat futures price returns. The models applied here are a univariate GARCH, a multivariate ARCH (the BEKK model), an option implied and a composite forecast model. The composite model includes time-series (historical) and option implied volatility forecasts. The results show that the option implied model is superior to the historical models in terms of accuracy and that the composite forecast model was the most accurate one (compared to the alternative models) having the lowest mean-square-errors. Given these findings it is recommended to use a composite forecast model if both types of data are available i.e. the time-series (historical) and the option implied. In addition, the results of this paper are consistent to that part of the literature that emphasizes the difficulty on being accurate about forecasting asset price return volatility. This is because the explanatory power (coefficient of determination) calculated in the forecast regressions were relatively low.Agricultural commodities, BEKK model, multivariate GARCH, Samuelson hypothesis, theory of storage.

The sharp peak-flat trough pattern and critical speculation

We find empirically a characteristic sharp peak-flat trough pattern in a large set of commodity prices. We argue that the sharp peak structure reflects an endogenous inter-market organization, and that peaks may be seen as local ``singularities'' resulting from imitation and herding. These findings impose a novel stringent constraint on the construction of models. Intermittent amplification is not sufficient and nonlinear effects seem necessary to account for the observations.Comment: 20 pages, 6 figures (only fig.4 and 6 available in ps format), 3 tables, European Physical Journal B (in press

An economic view of carbon allowances market

The aim of this work is to bring an econometric approach upon the CO2 market. We identify the specificities of this market, and regarding the carbon as a commodity. We investigate the econometric particularities of CO2 prices behavior and their result of the calibration. We apprehend and explain the reasons of the non-Gaussian behavior of this market focusing mainly upon jump diffusion and generalized hyperbolic distributions. We test these results for the risk modeling of a structured product specific to the carbon market, the swap between two carbon instruments : The European Union Allowances and the Certiified Emission Reductions. We estimate the counterparty risk for this kind of transaction and evaluate the impact of different models upon the risk measure and the allocated capital.Carbon, Normal Inverse Gaussian, CER, EUA, swap.

SUBSTITUTION BETWEEN U.S. AND CANADIAN WHEAT BY CLASS

The importation of hard red winter and durum wheat from Canada has been a source of contention among U.S. wheat growers, due to the likeness between domestic and imported Canadian wheat. It has also been investigated as a source of material injury to the U.S. market. We examine the relative substitution between U.S. and Canadian wheat, by class, by treating wheat as an input in flour production. We find that while U.S. hard red spring wheat and U.S. hard red winter wheat are economic substitutes, there is limited price substitution between U.S. and Canadian durum and U.S. and Canadian hard red spring wheat. Quality differences from the millers' perspective may be the reason driving the import demand for hard red spring and durum wheat from Canada.International Relations/Trade,

Three essays on commodity markets

This dissertation consists of three essays that investigate issues in agricultural commodity futures and cash markets. The first essay uses price discovery measures and intraday data to quantify the proportional contribution of nearby and deferred contracts in price discovery in the corn and live cattle futures markets. On average, nearby contracts reflect information more quickly than deferred contracts in the corn market but have a relatively less dominant role in the live cattle market. In both markets, the nearby contract loses dominance when its relative volume share dips below 50%, which typically occurs when the nearby is close to maturity. Regression results indicate that the share of price discovery is mainly related to trading volume and time to expiration in both markets. In the corn market, the price discovery share between nearby and deferred contracts is also related to inverse carrying charges, crop year differences, USDA announcements, market crashes, and commodity index position rolls. Differences between corn and live cattle markets are consistent with differences in the contracts’ liquidity and commodity storability. The second essay investigates the effect of algorithmic trading activity, as measured by quoting, on the corn, soybean, and live cattle commodity futures market quality. Using the CME’s limit-order-book data and a heteroskedasticity-based identification approach, we find more intensive algorithmic quoting (AQ) is beneficial in multiple dimensions of market quality. On average, AQ improves pricing efficiency and mitigates short-term volatility, but its effects on liquidity costs are somewhat mixed. Increased AQ significantly narrows effective spreads in the corn and soybean markets, but not in the less traded live cattle futures market. The narrowing in effective spreads emerges from a reduction in adverse selection costs as more informed traders lose their market advantage. There also is evidence that liquidity provider revenues increase with heightened AQ activity in the corn futures market, albeit the effect is not statistically significant in the soybean and live cattle futures markets. The third essay investigates how export prices and sales responses to exchange rate movements are affected by the level of the stocks-to-use ratio. The analysis is performed in the corn, soybean, and wheat export markets using Threshold Vector Autoregressive (TVAR) models and monthly data for the January 1990-December 2019 period. Both importer and exporter exchange rates are considered in our analysis. Results show that the effects of both importer and exporter exchange rates on corn export prices and sales are either insignificant or have small economic value due to the relatively small export share of production. In the more export-oriented soybean and wheat markets, an increase in the value of the dollar relative to other exporters’ currencies causes an expected and significant decrease in the export price, but export sales are not significantly affected which reflects the low substitutability between the U.S. exports and competitors’ exports in terms of marketing seasons and crop classes. The effects of importer exchange rates present significant threshold effects in soybean and wheat markets as export prices and sales are more responsive in the low regime of stocks-to-use ratio. Similar threshold effects are also found in the exporter exchange rate impacts on corn export prices and sales. However, the impacts across regimes are not largely different in economic value

Style and Performance of Agricultural Market Advisory Services

This paper describes the degree of marketing activeness of market advisory programs for corn and soybeans, and analyzes the relationship between activeness degree and pricing performance. The data set employed consists of advisory programs tracked by the AgMAS Project at the University of Illinois between 1995 and 2001. Cluster analysis was conducted to group the programs according to their degree of activeness. Panel data regression models were estimated to evaluate the relationship between activeness degree and pricing performance. In the corn market, point estimates indicate a positive effect of the degree of activeness on pricing performance, but this effect is of small magnitude and statistically insignificant. For soybeans,there is a stronger positive relationship between activeness degree and performance, with an estimated effect of activeness on performance larger in magnitude and statistically significant. This positive relationship suggests that active marketing programs are based on superior information and/or analytical skills.Agribusiness, Marketing,

Quantitative Techniques for Spread Trading in Commodity Markets

This thesis investigates quantitative techniques for trading strategies on two commodities, the difference of whose prices exhibits a long-term historical relationship known as mean-reversion. A portfolio of two commodity prices with very similar characteristics, the spread may be regarded as a distinct process from the underlying price processes so deserves to be modeled directly. To pave the way for modeling the spread processes, the fundamental concepts, notions, properties of commodity markets such as the forward prices, the futures prices, and convenience yields are described. Some popular commodity pricing models including both one and two factor models are reviewed. A new mean-reverting process to model the commodity spot prices is introduced. Some analytical results for this process are derived and its properties are analyzed. We compare the new one-factor model with a common existing one-factor model by applying these two models to price West Texas Intermediate (WTI) crude oil, and discuss its advantages and disadvantages. We investigate the recent behavioral change in the location spread process between WTI crude oil and Brent oil. The existing three major approaches to price a spread process namely cointegration, one-factor and two-factor models fail to fully capture these behavioral changes. We, therefore, extend the one-factor and two-factor spread models by including a compound Poisson process where jump sizes follow a double exponential distribution. We generalize the existing one-factor mean-reverting dynamics (Vasicek process) by replacing the constant diffusion term with a nonlinear term to price the spread process. Applying the new process to the empirical location spread between WTI and Brent crude oils dataset, it is shown how the generalized dynamics can rigorously capture the most important characteristics of the spread process namely high volatility, skewness and kurtosis. To consider the recent structural breaks in the location spread between WTI and Brent, we incorporate regime switching dynamics in the generalized model and Vasicek process by including two regimes. We also introduce a new mean-reverting random walk, derive its continuous time stochastic differential equation and obtain some analytical results about its solution. This new mean-reverting process is compared with the Vasicek process and its advantages discussed. We showed that this new model for spread dynamics is capable of capturing the possible skewness, kurtosis, and heavy tails in the transition density of the price spread process. Since the analytical transition density is unknown for this nonlinear stochastic process, the local linearization method is deployed to estimate the model parameters. We apply this method to empirical data for modeling the spread between WTI crude oil and West Texas Sour (WTS) crude oil. Finally, we apply the introduced trading strategies to empirical data for the location spread between WTI and Brent crude oils, analyze, and compare the profitability of the strategies. The optimal trading strategies for the spread dynamics in the cointegration approach and the one-factor mean-reverting process are discussed and applied to our considered empirical dataset. We suggest to use the stationary distribution to find optimal thresholds for log-term investment strategies when the spread dynamics is assumed to follow a Vasicek process. To incorporate essential features of a spread process such as skewness and kurtosis into the spread trading strategies, we extend the optimal trading strategies by considering optimal asymmetric thresholds