A controversial debate between financial speculation and changes in agricultural commodity spot prices

Some research works state that speculation with agricultural commodities on the futures market has risen agricultural commodity spot prices. This research work analyzes the causal relationships between spot prices of corn, wheat, and soybean and agricultural commodity futures trading activities. These causal relationships between agricultural commodity spot prices and financial variables are tested for Granger-causality. Model results show that causal relationships have been found among changes in “volume traded” and “open positions” of futures contracts and changes in spot prices for corn. These results do not show that financial speculation might be a major driver of rising agricultural commodity prices.

Parameter estimation of structural commodity price models

Commodity price modeling is normally approached in terms of structural time-series models, in which the different components (states) have a financial interpretation. The parameters of these models can be estimated using maximum likelihood. This approach results in a non-linear parameter estimation problem and thus a key issue is how to obtain reliable initial estimates. In this paper, we focus on the initial parameter estimation problem for the Schwartz-Smith two-factor model commonly used in asset valuation. We propose the use of a two-step method. The first step considers a univariate model based only on the spot price and uses a transfer function model to obtain initial estimates of the fundamental parameters. The second step uses the estimates obtained in the first step to initialize a re-parameterized state-space-innovations based estimator, which includes information related to future prices. The second step refines the estimates obtained in the first step and also gives estimates of the remaining parameters in the model. This paper is part tutorial in nature and gives an introduction to aspects of commodity price modeling and the associated parameter estimation problem.

Better cross hedges with composite hedging? Hedging equity portfolios using financial and commodity futures

Unless a direct hedge is available, cross hedging must be used. In such circumstances portfolio theory implies that a composite hedge (the use of two or more hedging instruments to hedge a single spot position) will be beneficial. The study and use of composite hedging has been neglected; possibly because it requires the estimation of two or more hedge ratios. This paper demonstrates a statistically significant increase in out-of-sample effectiveness from the composite hedging of the Amex Oil Index using S&P500 and New York Mercantile Exchange crude oil futures. This conclusion is robust to the technique used to estimate the hedge ratios, and to allowance for transactions costs, dividends and the maturity of the futures contracts.

Using common features to understand the behavior of metal-commodity prices and forecast them at different horizons

The objective of this article is to study (understand and forecast) spot metal price levels and changes at monthly, quarterly, and annual horizons. The data to be used consists of metal-commodity prices in a monthly frequency from 1957 to 2012 from the International Financial Statistics of the IMF on individual metal series. We will also employ the (relatively large) list of co-variates used in Welch and Goyal (2008) and in Hong and Yogo (2009) , which are available for download. Regarding short- and long-run comovement, we will apply the techniques and the tests proposed in the common-feature literature to build parsimonious VARs, which possibly entail quasi-structural relationships between different commodity prices and/or between a given commodity price and its potential demand determinants. These parsimonious VARs will be later used as forecasting models to be combined to yield metal-commodity prices optimal forecasts. Regarding out-of-sample forecasts, we will use a variety of models (linear and non-linear, single equation and multivariate) and a variety of co-variates to forecast the returns and prices of metal commodities. With the forecasts of a large number of models (N large) and a large number of time periods (T large), we will apply the techniques put forth by the common-feature literature on forecast combinations. The main contribution of this paper is to understand the short-run dynamics of metal prices. We show theoretically that there must be a positive correlation between metal-price variation and industrial-production variation if metal supply is held fixed in the short run when demand is optimally chosen taking into account optimal production for the industrial sector. This is simply a consequence of the derived-demand model for cost-minimizing firms. Our empirical evidence fully supports this theoretical result, with overwhelming evidence that cycles in metal prices are synchronized with those in industrial production. This evidence is stronger regarding the global economy but holds as well for the U.S. economy to a lesser degree. Regarding forecasting, we show that models incorporating (short-run) commoncycle restrictions perform better than unrestricted models, with an important role for industrial production as a predictor for metal-price variation. Still, in most cases, forecast combination techniques outperform individual models.

Using common features to understand the behavior of metal-commodity prices and forecast them at different horizons

The objective of this article is to study (understand and forecast) spot metal price levels and changes at monthly, quarterly, and annual frequencies. Data consists of metal-commodity prices at a monthly and quarterly frequencies from 1957 to 2012, extracted from the IFS, and annual data, provided from 1900-2010 by the U.S. Geological Survey (USGS). We also employ the (relatively large) list of co-variates used in Welch and Goyal (2008) and in Hong and Yogo (2009). We investigate short- and long-run comovement by applying the techniques and the tests proposed in the common-feature literature. One of the main contributions of this paper is to understand the short-run dynamics of metal prices. We show theoretically that there must be a positive correlation between metal-price variation and industrial-production variation if metal supply is held fixed in the short run when demand is optimally chosen taking into account optimal production for the industrial sector. This is simply a consequence of the derived-demand model for cost-minimizing firms. Our empirical evidence fully supports this theoretical result, with overwhelming evidence that cycles in metal prices are synchronized with those in industrial production. This evidence is stronger regarding the global economy but holds as well for the U.S. economy to a lesser degree. Regarding out-of-sample forecasts, our main contribution is to show the benefits of forecast-combination techniques, which outperform individual-model forecasts - including the random-walk model. We use a variety of models (linear and non-linear, single equation and multivariate) and a variety of co-variates and functional forms to forecast the returns and prices of metal commodities. Using a large number of models (N large) and a large number of time periods (T large), we apply the techniques put forth by the common-feature literature on forecast combinations. Empirically, we show that models incorporating (short-run) common-cycle restrictions perform better than unrestricted models, with an important role for industrial production as a predictor for metal-price variation.

Determinantes del precio spot del cobre en las bolsas de metales

Incluye Bibliografía

Price risk management in the copper market using commodity derivatives and options strategies

Metals price risk management is a key issue related to financial risk in metal markets because of uncertainty of commodity price fluctuation, exchange rate, interest rate changes and huge price risk either to metals’ producers or consumers. Thus, it has been taken into account by all participants in metal markets including metals’ producers, consumers, merchants, banks, investment funds, speculators, traders and so on. Managing price risk provides stable income for both metals’ producers and consumers, so it increases the chance that a firm will invest in attractive projects. The purpose of this research is to evaluate risk management strategies in the copper market. The main tools and strategies of price risk management are hedging and other derivatives such as futures contracts, swaps and options contracts. Hedging is a transaction designed to reduce or eliminate price risk. Derivatives are financial instruments, whose returns are derived from other financial instruments and they are commonly used for managing financial risks. Although derivatives have been around in some form for centuries, their growth has accelerated rapidly during the last 20 years. Nowadays, they are widely used by financial institutions, corporations, professional investors, and individuals. This project is focused on the over-the-counter (OTC) market and its products such as exotic options, particularly Asian options. The first part of the project is a description of basic derivatives and risk management strategies. In addition, this part discusses basic concepts of spot and futures (forward) markets, benefits and costs of risk management and risks and rewards of positions in the derivative markets. The second part considers valuations of commodity derivatives. In this part, the options pricing model DerivaGem is applied to Asian call and put options on London Metal Exchange (LME) copper because it is important to understand how Asian options are valued and to compare theoretical values of the options with their market observed values. Predicting future trends of copper prices is important and would be essential to manage market price risk successfully. Therefore, the third part is a discussion about econometric commodity models. Based on this literature review, the fourth part of the project reports the construction and testing of an econometric model designed to forecast the monthly average price of copper on the LME. More specifically, this part aims at showing how LME copper prices can be explained by means of a simultaneous equation structural model (two-stage least squares regression) connecting supply and demand variables. A simultaneous econometric model for the copper industry is built: {█(Q_t^D=e^((-5.0485))∙P_((t-1))^((-0.1868) )∙〖GDP〗_t^((1.7151) )∙e^((0.0158)∙〖IP〗_t ) @Q_t^S=e^((-3.0785))∙P_((t-1))^((0.5960))∙T_t^((0.1408))∙P_(OIL(t))^((-0.1559))∙〖USDI〗_t^((1.2432))∙〖LIBOR〗_((t-6))^((-0.0561))@Q_t^D=Q_t^S )┤ P_((t-1))^CU=e^((-2.5165))∙〖GDP〗_t^((2.1910))∙e^((0.0202)∙〖IP〗_t )∙T_t^((-0.1799))∙P_(OIL(t))^((0.1991))∙〖USDI〗_t^((-1.5881))∙〖LIBOR〗_((t-6))^((0.0717) Where, Q_t^D and Q_t^Sare world demand for and supply of copper at time t respectively. P(t-1) is the lagged price of copper, which is the focus of the analysis in this part. GDPt is world gross domestic product at time t, which represents aggregate economic activity. In addition, industrial production should be considered here, so the global industrial production growth that is noted as IPt is included in the model. Tt is the time variable, which is a useful proxy for technological change. A proxy variable for the cost of energy in producing copper is the price of oil at time t, which is noted as POIL(t ) . USDIt is the U.S. dollar index variable at time t, which is an important variable for explaining the copper supply and copper prices. At last, LIBOR(t-6) is the 6-month lagged 1-year London Inter bank offering rate of interest. Although, the model can be applicable for different base metals' industries, the omitted exogenous variables such as the price of substitute or a combined variable related to the price of substitutes have not been considered in this study. Based on this econometric model and using a Monte-Carlo simulation analysis, the probabilities that the monthly average copper prices in 2006 and 2007 will be greater than specific strike price of an option are defined. The final part evaluates risk management strategies including options strategies, metal swaps and simple options in relation to the simulation results. The basic options strategies such as bull spreads, bear spreads and butterfly spreads, which are created by using both call and put options in 2006 and 2007 are evaluated. Consequently, each risk management strategy in 2006 and 2007 is analyzed based on the day of data and the price prediction model. As a result, applications stemming from this project include valuing Asian options, developing a copper price prediction model, forecasting and planning, and decision making for price risk management in the copper market.

Market efficiency of commodity futures in India

This paper aims to examine the market efficiency of the commodity futures market in India, which has been growing phenomenally for the last few years. We estimate the long-run equilibrium relationship between the multi-commodity futures and spot prices and then test for market efficiency in a weak form sense by applying both the DOLS and the FMOLS methods. The entire sample period is from 2 January 2006 to 31 March 2011. The results indicate that a cointegrating relationship is found between these indices and that the commodity futures market seems to be efficient only during the more recent sub-sample period since July 2009.

Volatility spillovers for spot, futures, and ETF prices in energy and agriculture

The agricultural and energy industries are closely related, both biologically and financially. The paper discusses the relationship and the interactions on price and volatility, with special focus on the covolatility spillover effects for these two industries. The interaction and covolatility spillovers or the delayed effect of a returns shock in one asset on the subsequent volatility or covolatility in another asset, between the energy and agricultural industries is the primary emphasis of the paper. Although there has already been significant research on biofuel and biofuel-related crops, much of the previous research has sought to find a relationship among commodity prices. Only a few published papers have been concerned with volatility spillovers. However, it must be emphasized that there have been numerous technical errors in the theoretical and empirical research, which needs to be corrected. The paper not only considers futures prices as a widely-used hedging instrument, but also takes an interesting new hedging instrument, ETF, into account. ETF is regarded as index futures when investors manage their portfolios, so it is possible to calculate an optimal dynamic hedging ratio. This is a very useful and interesting application for the estimation and testing of volatility spillovers. In the empirical analysis, multivariate conditional volatility diagonal BEKK models are estimated for comparing patterns of covolatility spillovers. The paper provides a new way of analyzing and describing the patterns of covolatility spillovers, which should be useful for the future empirical analysis of estimating and testing covolatility spillover effects.

Assessing the impact of agricultural drought on maize prices in Kenya with the approach of the SPOT-VEGETATION NDVI remote sensing

The high cost of maize in Kenya is basically driven by East African regional commodity demand forces and agricultural drought. The production of maize, which is a common staple food in Kenya, is greatly affected by agricultural drought. However, calculations of drought risk and impact on maize production in Kenya is limited by the scarcity of reliable rainfall data. The objective of this study was to apply a novel hyperspectral remote sensing method to modelling temporal fluctuations of maize production and prices in five markets in Kenya. SPOT-VEGETATION NDVI time series were corrected for seasonal effects by computing the standardized NDVI anomalies. The maize residual price time series was further related to the NDVI seasonal anomalies using a multiple linear regression modelling approach. The result shows a moderately strong positive relationship (0.67) between residual price series and global maize prices. Maize prices were high during drought periods (i.e. negative NDVI anomalies) and low during wet seasons (i.e. positive NDVI anomalies). This study concludes that NDVI is a good index for monitoring the evolution of maize prices and food security emergency planning in Kenya. To obtain a very strong correlation for the relationship between the wholesale maize price and the global maize price, future research could consider adding other price-driving factors into the regression models.

Pairs trading profits in commodity futures markets

This study employs a pairs trading investment strategy on daily commodity futures returns. The study reveals that pairs trading in similarly related commodity futures earns statistically significant excess returns with commensurate volatility. The excess returns from pairs trading in commodity futures are unrelated to conventional market risk factors and they are not associated with classic contrarian investing. The evidence of pairs trading reflect compensation to arbitrageurs for enforcing the law of one price in similarly related market efficiency.

Distribution of benefits of irrigation induced technological changes and commodity markets : new evidence

A plethora of literature exists on irrigation development. However, only a few studies analyse the distributional issues associated with irrigation induced technological changes (IITC) in the context of commodity markets. Furthermore, these studies deal with only the theoretical arguments and to date no proper investigation has been conducted to examine the long-term benefits of adopting modern irrigation technology. This study investigates the long-term benefit changes of irrigation induced technological changes using data from Sri Lanka with reference to rice farming. The results show that (1) adopting modern technology on irrigation increases the overall social welfare through consumption of a larger quantity at a lower cost (2) the magnitude, sensitivity and distributional gains depend on the price elasticity of demand and supply as well as the size of the marketable surplus (3) non-farm sector gains are larger than farm sector gains (4) the distribution of the benefits among different types of producers depend on the magnitude of the expansion of the irrigated areas as well as the competition faced by traditional farmers (5) selective technological adoption and subsidies have a detrimental effect on the welfare of other producers who do not enjoy the same benefits (6) the short-term distributional effects are more severe than the long-term effects among different groups of farmers.