Gabriel J. Power

Short- and Long-Run Determinants of Commodity Price Volatility

To explain price volatility in the U.S. agricultural, energy, and metal futures markets, we estimate a model of common and commodity-specific, high- and low-frequency factors by building on the spline-GARCH model of Engle and Rangel (2008). A better model fit results from allowing the unconditional variance to slowly change over time. Moreover, the persistence of volatility shocks is shown to be much weaker than what standard GARCH models imply. Combining the volatility results with monthly macroeconomic indicator data, we find that decomposing realized volatility into high- and low-frequency components better reveals the impact of slowly-evolving aggregate variables on price volatility. Moreover, over the period 1990--2005, most of the macroeconomic variables had similar effects within the same commodity category (e.g. grain), but their effects differed across commodity groups (e.g. grain versus livestock). Over the period 2006--2009, however, commodity-specific factors dominated common factors. Copyright 2013, Oxford University Press.

Risk-Reducing Effectiveness of Revenue versus Yield Insurance in the Presence of Government Payments

Government farm support programs such as Loan Deficiency Payments (LDP) and Counter-Cyclical Payments (CCP) have payoff structures that effectively make them costless price insurance instruments. A combination of these payments with yield insurance may provide a viable alternative to revenue insurance. This paper finds that, contrary to expectations, the revenue product analyzed is uniformly superior to yield insurance under both current (2002) and proposed (2008) Farm Bill structures of government payments. Given minor adjustments, however, yield insurance combined with government payments can provide more effective risk management than revenue insurance in production areas with low yield–price correlation.

The Impact of the Average Crop Revenue Election (ACRE) Program on the Effectiveness of Crop Insurance

Purpose - The purpose of this paper is to analyze the effect of the 2008 Farm Bills average crop revenue election (ACRE) program on the risk-reducing effectiveness of crop insurance products. Design/methodology/approach - Three crop/region combinations are examined, representing regions with both high and low price-yield correlation regions. Actual production history (APH) and crop revenue coverage (CRC) insurance instruments are considered separately under the 2002 Farm Bill and under ACRE. Monte Carlo simulations, combined with the copula approach, are used to simulate net wealth distributions and to calculate the corresponding expected utilities. The outcomes are evaluated using certainty-equivalent wealth based on different risk premium assumptions. Findings - Crop insurance contracts appear to be more effective under the 2002 Farm Bill than under ACRE, especially for crops characterized by low yield-price correlation. CRC insurance is found to be more effective than APH insurance for all crop/region combinations considered. Research limitations/implications - The paper only considers a static framework and farm-level insurance contracts. Further research could investigate how ACRE affects decoupled income support, whether the results change if Supplemental Revenue Assistance is included, or how different the outcomes might be for multiple-crop farms. Practical implications - The results suggest that risk-reducing effectiveness decreases under ACRE and that no reasonable adjustment to APH base price can make APH competitive with CRC for any crop/regions considered. Originality/value - The risk-reducing effectiveness of the 2008 Farm Bills ACRE program is analyzed, and as a methodological contribution the copula approach is used to model the multivariate distribution of yields and prices.

Market volatility and the dynamic hedging of multi-commodity price risk

Commodity cash and futures prices experienced a severe boom-and-bust cycle between 2006 and 2009. Increases in commodity price volatility have raised concerns about the usefulness of commodity futures and options as risk management tools. Dynamic hedging strategies have the potential to improve risk management when conditional (co)variances depart significantly from their unconditional, long-run counterparts and may be useful to decision-makers despite their greater complexity and higher transaction costs. We propose a Nonparametric Copula-based Generalized Autoregressive Conditional Heteroscedastic (NPC-GARCH) approach to estimate time-varying hedge ratios, and evaluate the benefits of dynamic hedging during four sub-periods between 2000 and 2011 using a stylized Texas cattle feedlot management problem. The NPC-GARCH approach allows for a flexible, nonlinear and asymmetric dependence structure between cash and futures prices for different commodities. We find that NPC-GARCH dynamic hedging performs better than either static, GARCH-Dynamic Conditional Correlation (DCC) or GARCH-Baba, Engle, Kraft and Kroner (BEKK) hedging in terms of lower tail risk (expected shortfall), but that there is no significant difference between hedging approaches in terms of portfolio variance reduction.

Revealing the impact of index traders on commodity futures markets

Commodity futures prices and volatility increased dramatically from 2006 to 2008, following a period during which index traders, a class of large investment funds, took on massive commodity futures positions. This article presents a method to reveal the extent to which index trader trading activity (volume) might have caused increases in futures price volatility. This approach is useful when position-level data are incomplete or confidential, as with index trader position data. The method is applied to leading agricultural commodity futures data. The impact of index traders is identified during their period of greatest activity, that is, 2005 to 2006, using aggregated volume data that are filtered using wavelet transforms. The filtering decision rule is guided by the Commodity Futures Trading Commissions (CFTC) finding that index traders do not engage in short-run trades. A joint model of futures (filtered) volume and (unfiltered) price volatility is estimated by 2SLS to account for the endogeneity of prices and volume. As a robustness check, both log-range and GARCH measures of volatility are used. The evidence provides no support for the claim that index traders have increased price volatility for storable commodities (grains/oilseeds) and only weak support in the case of nonstorable commodities (meats).

US rural land value bubbles

Real estate bubbles have attracted much attention in the research literature following substantial growth in prices. Given the recent subprime mortgage crisis, this article is concerned with the related problem of possible bubbles in rural land values, given that at the margin urban real estate exerts pressure on rural land markets. Land is the most important financial asset for the agricultural economy, and income risk caused by farmland price volatility creates economic hardship for rural communities. Farmland valuation remains to a large extent a puzzle as it is only partially explained by fundamentals including inflation. This article tests the hypothesis of a price bubble in the real option value to future land development using land value data for all 48 contiguous US states and finds strong evidence that a short-run bubble has been active in recent years.

Measuring infrastructure investment option value

Purpose - – The purpose of this paper is to propose a risk-based framework to estimate the option value of infrastructure investment, accounting for the stochastic behavior of both financial and physical (engineering) variables. Design/methodology/approach - – This study uses a real-options approach and computes the optimal investment dates and option values using Least Squares Monte Carlo, both the original Longstaff – Schwartz algorithm and the constrained Least Squares approach of Le tourneau – Stentoft. Findings - – Real-option value for infrastructure investment is substantial. It is beneficial to model jointly financial and engineering risks to better understand the timing and real-option value of infrastructure investment. The analysis further shows which variables are option value drivers. Research limitations/implications - – Future work could integrate financing constraints into the model, consider path dependency in the physical state variables or integrate sovereign risk, expropriation risk, operational risk or other project risks. Practical implications - – Financial practitioners and investment managers interested in infrastructure risk finance or project finance will benefit from a novel framework to analyze infrastructure investments in which engineering and financial risks interact in a tractable way. Social implications - – Public decision-makers will benefit from a better understanding of what determines the value of infrastructure investments, how real-option value affects optimal investment timing and how both are determined by financial and engineering risks. Originality/value - – The analysis considers financial and engineering risks in a single framework to better understand option value in infrastructure investment. The framework and findings are useful both to risk finance and project finance practitioners and investors as well as engineers and public sector decision-makers.

Commodity futures price volatility, convenience yield and economic fundamentals

Commodity price volatility increased during 2006 to 2011 first with the commodity bull cycle of 2006 to 2008 and then with the credit freeze crisis and Great Recession. This letter uses both high- and low-frequency data over 1990 to 2011 to examine the link between economic fundamentals and measures of realized volatility and convenience yield computed from estimated futures price forward curves. The possible influence of institutional investors (index traders) is also examined. Affine term structure models are estimated using Intercontinental Exchange (ICE) futures prices on cotton, a commodity for which economic fundamentals can be readily identified and measured. The results, robust across specifications, suggest that the determinants of volatility, but not convenience yield, changed during the period 2006 to 2011. There is no evidence that index traders are responsible for increased volatility or changes in convenience yield.

Testing for changes in option-implied risk aversion

Purpose - – The purpose of this paper is to investigate and test for changes in investor risk aversion and the stochastic discount factor (SDF) using options data on the West Texas Intermediate crude oil futures contract during the 2007-2011 period. Design/methodology/approach - – Risk aversion functions and SDFs are estimated using parametric approaches before and after four specific dates of interest. The dates are: the summer 2008 end of the bull market regime; the late 2008 credit freeze trough; the BP Deepwater Horizon explosion; and the Libyan uprising. Findings - – Absolute risk aversion functions and SDFs are significantly flatter (less decreasing in wealth) after the end of the bull market and the credit freeze trough. After these two market reversals, oil market participants were less risk-averse for low levels of wealth but more risk-averse for high wealth levels. Oil market investors also increased their valuation of anticipated future wealth in average states of nature relative to very high or very low-asset return states after reversals. The BP explosion and the Libyan uprising led to steeper risk aversion functions (decreasing more rapidly in wealth) and SDF. Oil market investors were more risk-averse for lower future wealth, but less risk-averse for higher future wealth. Oil market investors increased their valuation of anticipated future wealth in extreme states of nature relative to average states of nature after both dates. Originality/value - – Documenting statistically and economically significant changes in oil market investors’ attitude toward risk and inter-temporal appetite for risk in relation to changes in financial and political conditions.

Quantitative finance for agricultural commodities: discussion and extension

Purpose - – The purpose of this paper is to review three papers in this issue and contribute new results on commodity futures prices and volume using wavelet analysis. Design/methodology/approach - – The paper uses time series econometrics including variance ratio tests, fractional integration estimators, and wavelet transforms. Findings - – The role of time horizon is emphasized in the discussion of the three papers, and wavelet methods are shown to be a useful tool to better understand time horizon-specific risk. Moreover, changes in the time horizon of futures trading are documented and discussed. Originality/value - – In addition to discussing three papers on quantitative finance for agricultural commodities, this paper also looks at how the analysis and management of short-term and long-term risk may differ. To this end, wavelet transform-based time series methods are reviewed and applied.