Cory Walters

Bio: Cory Walters is an academic researcher from University of Nebraska–Lincoln. The author has contributed to research in topics: Crop insurance & Subsidy. The author has an hindex of 5, co-authored 22 publications receiving 109 citations.

Asymmetric Price Volatility Transmission between U.S. Biofuel, Corn, and Oil Markets

Abstract: Linkages between agricultural commodity and energy prices have become more complex with increased ethanol production. The concern is whether the new corn–ethanol links lead to volatilityspillover transmission between food and energy prices. We investigate asymmetric volatility spillovers between oil, corn, and ethanol prices using a BEKK-multivariate-GARCH approach. Additionally, we use daily, weekly, and monthly futures prices to examine whether the use of different-frequency data leads to inconsistent results. The results support the existence of asymmetric volatility transmission between corn and ethanol prices. Furthermore, the volatilityspillover effects are different for the different-frequency prices, and positive and negative price changes generate inconsistent results.

Crop Insurance, Land Allocation, and the Environment

Abstract: Government programs that help agricultural producers manage risk may have environmental consequences. In recent years, premium subsidies for crop insurance have been increased substantially to encourage greater producer participation. Using detailed, producer-level crop insurance contract data in four regions, we investigate whether adverse environmental effects have resulted from these increased subsidies. We find some association between environmental effects and insurance contracts. On average, however, we find that environmental effects are generally small and as often beneficial as adverse. More importantly, we find that results are specific to local conditions and to particular environmental indicators and may be hidden in aggregate analysis.

Risk implications from the selection of rainfall index insurance intervals

Abstract: The purpose of this paper is to empirically examine the financial outcomes from forage production and RI-PRF insurance interval for two locations in Nebraska. Both locations provide historical forage production and precipitation data, allowing the authors to examine the relation between RI-PRF net income and forage production.,The authors focus on evaluating the producer net income and risk (measured as variance of net income) by examining the relation between farm precipitation and production and comparing multiple insurance intervals to no insurance. Each insurance interval will likely have a different relation (basis risk) between observed production and return from insurance and, therefore, a different impact on the variance of net incomes. The impact on variance of net incomes identifies the risk-reducing aspects of RI-PRF insurance intervals. The authors then rank each scenario into four mutually exclusive zones that describe the risk-reducing effectiveness and whether the subsidy is working correctly.,The authors found both risk increasing and decreasing insurance intervals exist at both locations. One insurance scenario (low in BBR) provided the highest net income while increasing risk, suggesting a profit maximizing opportunity. RI-PRF reduces net income risk with intervals insuring during high expected precipitation (growing season); while net income risk increases with intervals insuring low expected precipitation (non-growing season, winter months). The farmer would want to insure during the high expected precipitation months, which coincides with the growing season, since RI-PRF lowers the net income risk. For the government, removing net income risk increasing intervals improves the allocation of government resources.,In this paper, the authors modeled the relation between RI-PRF interval selection using the historical forage production data at two locations in Nebraska. The use of historical forage production data allowed the authors to precisely identify the risk-reducing effectiveness of RI-PRF interval selection.

Net income risk, crop insurance and hedging

Abstract: Purpose At the beginning of the production year producers face a complex risk management decision environment given by risks specific to their operation, multiple crop insurance contracts and hedging opportunities. The purpose of this paper is to provide a producer-level framework for risk management decision making, focusing on the interaction between crop insurance and hedging. Design/methodology/approach The authors develop a Monte Carlo simulation model that generates a producer’s net income (NI) distribution that incorporates historical producer risk, price-yield correlation via a copula, price risk, and production costs. The authors evaluate the NI distribution through a modified Modern Portfolio Theory (MPT) decision framework. The authors use the modified MPT decision framework to explore tradeoffs between expected NI and farm ruin (defined as 1 or 5 percent expected shortfall) from different crop insurance contracts and pre-harvest hedging options. Findings Only revenue protection and the highest two levels of coverage level exist on the efficient frontier. The level of hedging on the efficient frontier ranges from 0 to 55 percent of Actual Production History. The authors find that increasing coverage level 5 percent (from 80 to 85 percent) negatively impacts the optimal hedging amount by 26 percentage points (from 35 to 9 percent). Originality/value The model provides the precise identification of financial benefits from different risk management strategies by incorporating producer-level historical yield data, using a copula to capture yield-price dependency structure and producer production cost in generating the NI distribution. This model can be applied to any producer’s characteristics and data.

Asymmetric Price Volatility Transmission between U.S. Biofuel, Corn, and Oil Markets

Abstract: Linkages in agriculture and energy markets have been reinforced with the increase in ethanol agribusinesses. An important concern has been whether corn-ethanol-oil linkages transfer instability and risk from energy markets to already volatile agricultural markets and lead to price volatility spillover effects between food and ethanol markets. The objective of this article is to investigate the potential volatility spillover effects between crude oil and U.S. corn and ethanol prices. In this article, a VARMEA BEKK multivariate Asymmetric GARCH (MVAGARCH) time-series approach with daily, weekly, and monthly commodity futures price frequencies was used to assess the extent of price volatility transmission between the agriculture and energy markets. We found asymmetric volatility spillover effects for the corn and ethanol markets. Overall we found the corn market responds differently to the shocks from the crude oil and ethanol markets depending on the dataset frequency. All dataset frequencies showed evidence of volatility spillover effects from corn to the ethanol market, but only with the daily frequency did we find volatility spillover effect from ethanol to the corn market. Also, we found that ethanol and corn return volatility responds differently to the positive and negative shocks in the crude oil, ethanol, and corn markets. These results are robust to the frequency of the dataset used in this study.

Maladaptive outcomes of climate insurance in agriculture

Abstract: Agricultural insurance programs are currently being championed by international donors in many developing countries. They are acclaimed as promising instruments for coping with climate risk. However, research on their impacts has mainly focused on economic considerations. Studies on broader social and ecological consequences are sparse and have produced ambiguous and inconclusive results. We address this knowledge deficit by (a) advocating for a holistic view of social-ecological systems and vulnerability when considering insurance impacts; (b) offering a systematic overview highlighting the potential beneficial and adverse effects of ‘climate insurance’ in agriculture, particularly where programs target intensifying agricultural production; and (c) suggesting preliminary principles for avoiding maladaptive outcomes, including specific recommendations for designing appropriate impact studies and insurance programs. Our synopsis brings together scientific knowledge generated in both developing and developed countries, demonstrating that agricultural insurance programs shape land-use decisions and may generate serious economic, social, and ecological consequences. If insurance is to be an appropriate tool for mitigating the impacts of climate change, it needs to be carefully developed with specific local social-ecological contexts and existing risk coping strategies in mind. Otherwise, it is liable to create long-term maladaptive outcomes and undermine the ability of these systems to reduce vulnerability.

Major Uses of Land in the United States, 2012

Abstract: The United States has a total land area of about 2.3 billion acres. In 2012, the major land uses were grassland pasture and rangeland at 655 million acres (29 percent of U.S. total); forest-use land at 632 million (28 percent); cropland at 392 million acres (17 percent); special uses (primarily parks and wildlife areas) at 316 million acres (14 percent); miscellaneous uses (such as wetlands, tundra, and unproductive woodlands) at 196 million acres (9 percent); and urban land at 70 million acres (3 percent). This study presents findings from the most recent (2012) inventory of U.S. major land uses, drawing on data from USDA, the U.S. Census Bureau, public land management and conservation agencies, and other sources. The data are collected for each State to estimate the use of several broad classes and subclasses of agricultural and nonagricultural land over time. National and regional trends in land use are compared with earlier major land-use estimates.

Volatility Spillover Between Oil And Agricultural Commodity Markets

Abstract: Globally, instability in the energy market is quickly reflected in the food market. Therefore, there exists a direct relationship between oil and food. This study adopts variance in causality tests on daily data from 01 January 1986 to 7 June 2012 in order to identify the causality of the food price crisis. The data is divided into three sub-periods: the pre-crisis period (01 January 1986 to 31 December 2005), the crisis period (01 January 2006-31 December 2008) and the post crises period (01 January 2009 - 7 June 2012). See figure1 The first part of the causality in variance analysis adopts the newly developed Hafner and Herwartz (2006) approach. It uses both univariate GARCH and the multivariate GARCH-BEKK model introduced by Engle and Kroner (1995) to analyse volatility spillover effects of the food crisis on the selected agricultural commodity markets (wheat, corn, soybeans, and sugar). In the multivariate analysis volatility spillovers have been taken into account by including a dummy variable in the conditional variance specification. The results show that the persistence of the volatility doesn't change significantly during and following the food price crisis. The second part of the causality in variance analysis uses a Granger test to determine whether the oil market influences the agriculture market, or vice versa. The test results show that while there is no risk of transmission between oil and the selected agricultural commodity markets (wheat, corn, soybeans, and sugar) in the pre-crisis period, during the crises the agriculture market's volatility - with the exception of wheat - spills into the oil market. After the crises, only the soybean market spills over to the oil market, while there is no effect in the other markets. This paper aims to foster an awareness and understanding of national food security issues. This understanding builds a knowledge-based society and helps policymakers make strategic plans that can be tailored to the unique challenges and resources of the region. It promotes innovation, discovery and economic diversification by using the newly developed multivariate garch model thereby showing that the dynamics of volatility transmission change significantly before, during, and after a food price crisis. During the crisis, risk transmission emerged as another dimension of the dynamic interrelationships between energy and agricultural markets.