Evaluating the Dynamic Nature of Market Risk

This study examines the systematic risk present in major crops for the United States and three corn-belt states. An index of commodities is used in conjunction with cash receipts to generate dynamic estimates of the systematic risk for each crop and state. In our study, we find that beta estimates from a time varying parameter model (FLS) and OLS formulation are substantially different. From our graphs of betas over time, one gains insight into the changing nature of risk and the impact of institutional and macroeconomic events. Systematic risk is shown to increase for most crops over the analyzed period with significant changes in volatility after the collapse of the Bretton Woods Accord.Systematic risk, flexible least squares, single index model, farm policy, macroeconomics, Agribusiness, Agricultural Finance, Consumer/Household Economics, Demand and Price Analysis, Farm Management, Financial Economics, Institutional and Behavioral Economics, Marketing, Risk and Uncertainty,

Pricing Weather Derivatives

This paper presents a general method for pricing weather derivatives. Specification tests find that a temperature series for Fresno, California follows a mean-reverting Brownian motion process with discrete jumps and ARCH errors. Based on this process, we define an equilibrium pricing model for cooling degree day weather options. Comparing option prices estimated with three methods: a traditional burn-rate approach, a Black-Scholes-Merton approximation, and an equilibrium Monte Carlo simulation reveals significant differences. Equilibrium prices are preferred on theoretical grounds, so are used to demonstrate the usefulness of weather derivatives as risk management tools for California specialty crop growers.derivative, jump-diffusion process, mean-reversion, volatility, weather, Demand and Price Analysis,

Forecasting the real price of oil in a changing world: a forecast combination approach : [Version November 13, 2013]

The U.S. Energy Information Administration (EIA) regularly publishes monthly and quarterly forecasts of the price of crude oil for horizons up to two years, which are widely used by practitioners. Traditionally, such out-of-sample forecasts have been largely judgmental, making them difficult to replicate and justify. An alternative is the use of real-time econometric oil price forecasting models. We investigate the merits of constructing combinations of six such models. Forecast combinations have received little attention in the oil price forecasting literature to date. We demonstrate that over the last 20 years suitably constructed real-time forecast combinations would have been systematically more accurate than the no-change forecast at horizons up to 6 quarters or 18 months. MSPE reduction may be as high as 12% and directional accuracy as high as 72%. The gains in accuracy are robust over time. In contrast, the EIA oil price forecasts not only tend to be less accurate than no-change forecasts, but are much less accurate than our preferred forecast combination. Moreover, including EIA forecasts in the forecast combination systematically lowers the accuracy of the combination forecast. We conclude that suitably constructed forecast combinations should replace traditional judgmental forecasts of the price of oil

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,

A Comparison of Threshold Cointegration and Markov-Switching Vector Error Correction Models in Price Transmission Analysis

We compare two regime-dependent econometric models for price transmission analysis, namely the threshold vector error correction model and Markov-switching vector error correction model. We first provide a detailed characterization of each of the models which is followed by a comprehensive comparison. We find that the assumptions regarding the nature of their regime-switching mechanisms are fundamentally different so that each model is suitable for a certain type of nonlinear price transmission. Furthermore, we conduct a Monte Carlo experiment in order to study the performance of the estimation techniques of both models for simulated data. We find that both models are adequate for studying price transmission since their characteristics match the underlying economic theory and allow hence for an easy interpretation. Nevertheless, the results of the corresponding estimation techniques do not reproduce the true parameters and are not robust against nuisance parameters. The comparison is supplemented by a review of empirical studies in price transmission analysis in which mostly the threshold vector error correction model is applied.price transmission, market integration, threshold vector error correction model, Markov-switching vector error correction model, comparison, nonlinear time series analysis, Agricultural Finance,

A State Dependent Regime Switching Model of Dynamic Correlations

Replaced with revised version of paper 07/29/09.dynamic correlations, regime switching, state dependent probabilities, thresholds, spillovers, Research Methods/ Statistical Methods,

Empirical tests of a simple pricing model for sugar futures

Price Theory;Estimation

TIME-VARYING MULTIPRODUCT HEDGE RATIO ESTIMATION IN THE SOYBEAN COMPLEX: A SIMPLIFIED APPROACH

In developing optimal hedge ratios for the soybean processing margin, many authors have illustrated the importance of considering the interactions between the cash and futures prices for soybeans, soybean oil, and soybean meal. Conditional as well as time-varying hedge ratios have been examined, but in the case of multiproduct time-varying hedge ratios, the difficulty in estimation has been found to often outweigh any improvement in hedging effectiveness. This research examines the hedging effectiveness of the Risk Metrics procedure for estimating a time-varying covariance matrix for developing optimal hedge ratios for the soybean processing margin. The Risk Metrics method allows for a time-varying covariance matrix while being considerably easier to implement than multivariate GARCH (MGARCH) procedures. The Risk Metrics procedure has been advocated for use in developing Value-at-Risk estimates. While it provided considerable out-of-sample improvement in hedging effectiveness relative to a constant correlation MGARCH procedure, the Risk Metrics method provided only minimal improvement over a naive (1-to-1) hedging strategy. However, this research does illustrate the potential for the Risk Metrics methodology as a viable alternative to MGARCH procedures in a multiproduct hedging context.Marketing,