Some insurance firms are faced with the unique challenge of managing risks that are large, infrequent, and potentially highly correlated within geographic regions and/or across product lines. An example of this is crop insurance, which includes weather risk, and leads to a portfolio of risks with high variance. A solution to this problem is undertaken in this study, through using a combination of pooling and private reinsurance in a portfolio approach. This approach takes advantage of offsetting risks across regions, in order to reduce risk in a cost effective manner. An asset liability management (ALM) approach is used to examine the entire crop insurance sector for Canada. This is the first study to focus on pooling for an entire insurance sector in a country, and it uses all major crops from 1978-2009, across 10 regions (provinces). Chapter two develops an innovative insurance portfolio under a full premium pool, combining a self managed insurance pool and private reinsurance using the coefficient of variation (CV) of the loss coverage ratio (LCR), Model 3. Results show that this portfolio approach reduces risk across regions. Chapter three, in contrast to chapter two, uses a reinsurance premium pool, where regions contribute only a portion of their risk to a reinsurance pool. An improved insurance portfolio model is developed in chapter three, using combinatorial optimization with a genetic algorithm to combine a self managed reinsurance pool and private reinsurance, Model C. Results show that this reinsurance portfolio model efficiently reduces risk. Chapter four uses a similar approach to chapter three, except that it allows for dependence (correlation) across regions. Results for this model (Model CC) are consistent with those of chapter three, indicating the effectiveness of the portfolio approach when correlation is present across regions. Overall, the portfolio models developed in each of the three chapters (Model 1, Model C, and Model CC), produce acceptable surplus, survival probability, and deficit at ruin, indicating that the portfolio approach using pooling is efficient for reducing risk. Beyond crop insurance, the portfolio models can be applied to other large natural disaster and weather related insurance, and other portfolio applications.

Numerous authors note failure of private insurance markets to provide affordable and comprehensive crop insurance. Economic logic suggests that index contracts potentially may have some advantages when compared with traditional (farm based) crop insurance. It is also a matter of common knowledge that weather is an important production factor and at the same time one of the greatest sources of risk in agriculture. Hence introduction of crop insurance contracts, based on weather indexes, might be a reasonable approach to mitigate problems, associated with traditional crop insurance products, and possibly lower the cost of insurance for end users. In spite of the fact that before the financial crisis of 2008-09 market for weather derivatives was the fastest growing derivatives market in the USA, agricultural producers didn’t express much interest in application of weather derivatives to management of their systematic risk. There are several reasons for that, but the most important one is the presence of high basis risk, which is represented by its two major components: technological (i.e. goodness of fit between yield and weather index) and geographical basis. Majority of the researchers is focusing either on pricing of weather derivatives or on mitigation of geographical basis risk. At the same time the number of papers researching possible ways to decrease technological basis is quite limited, and always assumes linear dependency between yields and weather variables, while estimating the risk reducing efficiency of weather contracts, which is obviously large deviation from reality. The objective of this study is to estimate the risk reducing efficiency of crop insurance contracts, based on weather derivatives (indexes) in the state of Texas. The distributions of representative farmer’s profits with the proposed contracts are compared to the distributions of profits without a contract. This is done to demonstrate the risk mitigating effect of the proposed contracts. Moreover the study will try to account for a more complex dependency structures between yields and weather variables through usage of copulas, while constructing joint distribution of yields and weather data. Selection of the optimal copula will be implemented in the out-of-sample efficient framework. An effort will be done to identify the most relevant periods of year, when weather has the most significant influence on crop yields, which should be included in the model, and to discover the most effective copula to model joint weather/yield risk. Results suggest that effective insurance of crop yields in the state of Texas by the means of proposed weather derivatives is possible. Besides, usage of data-mining techniques allows for more accurate selection of the time periods to be included in the model than ad hoc procedure previously used in the literature. Finally selection of optimal copula for modeling of joint weather/yield distribution should be crop and county specific, while in general Clayton and Frank copula of Archimedean copula family provide the best out-of-sample metric results.

Dissertation (Ph.D.)--Auburn University, 2006. / Abstract. Includes bibliographic references.

I analyzed the effects of Agriculture Risk Coverage (ARC) and Revenue Protection crop insurance (RP) on the RP coverage level by certainty equivalents and certainty equivalent returns. ARC is a commodity program that falls under Title I of the 2014 farm bill and triggers a payment for a participating producer once his actual revenue falls below a band of 76-86 percent of a calculated expected revenue. RP is a revenue-based crop insurance program that allows for a producer to sign up for one of eight different coverage levels ranging from 50-85 percent in 5 percent increments. This leads to the idea that in order to maximize his utility, a fully-informed, expected-utility maximizing producer should not choose to select full coverage RP but rather select the 75 percent RP and pair it with the ARC program. This analysis is conducted under the conceptual frameworks of expected-utility and cumulative prospect theory.

The federal crop insurance program provides crop insurance for millions of acres and many commodities every year. The Risk Management Agency of the USDA is responsible for determining the premium rates for these covered commodities. Currently, the quality of soil is not considered when determining baseline yields and expected premium rates. This study utilizes the moment-based maximum entropy method to assess the effect of incorporating soil in the rate making methodology. Several moments of upland cotton yield in Arkansas, Mississippi, and Texas are conditioned on weather, irrigation, and soil control variables. Ultimately, I find evidence of mispriced premium rates for counties in all three states for both irrigated and non-irrigated upland cotton yield.

Hay auctions have generally been understudied due to their unique market structure. Therefore, the factors that influence the price of hay at auction markets are not well-known. The price of hay at auction markets reflects the various characteristics that differentiate each lot of hay sold. This study is aimed at analyzing the determinants of Central Kentucky hay prices. A hedonic price model is estimated using data collected from a Central Kentucky hay auction. Known hay attributes include forage species, form, bale weight, and nutritive value. An important aspect of this analysis is to determine whether the quality measures of the hay are significant factors in determining hay prices in this auction setting. While price discovery of hay is important, it is also important to know about the insurance that is available to producers. Insurance for hay production is very limited with only two insurance programs available to Kentucky producers. An evaluation of the Noninsured Crop Disaster Assistance Program is conducted by simulating yields from an alfalfa producer and alfalfa trials from University of Kentucky Agriculture Research Centers in Princeton and Lexington, Kentucky. This analysis reveals the effectiveness of the coverage levels offered through the program for alfalfa producers in Kentucky.

<p>Changing temperatures and precipitation patterns from climate change could be a major risk to crop yields. Producers have technology options for mitigating climate change risk. One technology is Drainage Water Recycling (DWR), which involves diverting subsurface water to ponds where it is stored for later irrigation. Crop insurance could interfere with DWR by providing producers with another option to manage climate-change risk. It is hypothesized there exists a spillover effect from crop insurance, which inhibits climate-change technology adoption. The analysis investigates the DWR investment decision from a producer’s viewpoint using real options analysis. The analysis considers two policy regimes: one where crop insurance is not in effect and one where crop insurance is in effect. In a Poisson jump process, it further considers the insurance effect of producer’s returns jumping when facing a crop disaster. Results indicate crop insurance has a minimal effect on DWR adoption, and in many scenarios, the DWR adoption thresholds are too large for a producer to invest for climate-change mitigation. The benchmark DWR adoption scenario requires a revenue of more than double the conventional revenue of $649 per acre before a producer would consider adopting. </p>

In recent years, agricultural magazine articles have positioned crop insurance requirements as a barrier to conservation adoption. Our research uses a mixed-methods design with Midwest conventional corn farmers to identify if crop insurance is a hindrance to adoption. Qualitative data was analyzed in Nvivo using thematic coding and quantitative data was analyzed using Stata statistical software. Our results indicate that crop insurance is not a direct barrier to adoption. Rather, farmers identify distinct and complimentary outcomes for risk-management from participating in both crop insurance and conservation. These findings reflect broader perspectives on Midwest conventional corn producers’ beliefs and rationale for using crop insurance and/or conservation practices.

Many economists and policy analysts have conducted studies on crop insurance. Three research gaps are identified: i) moral hazard in prevented planting (PP), ii) choice of PP and planting a second crop, and iii) selecting margin protection in the Dairy Margin Protection Program (MPP-Dairy). The first essay analyzes the existence of moral hazard in PP. The PP provision is defined as the “failure to plant an insured crop by the final planting date due to adverse events”. If the farmer decides not to plant a crop, the farmer receives a PP indemnity. Late planting (LP) is an option for the farmer to plant a crop while maintaining crop insurance after the final planting date. Crop insurance may alter farmers’ behavior in selecting PP or LP and could increase the likelihood of PP claims even though farmers can choose LP. This study finds evidence that a farmer with higher insurance coverage tends to choose PP more often (moral hazard). Spatial panel models attest to the existence of moral hazard in PP empirically. If a farmer chooses PP, s/he receives the PP indemnity and may either leave the acreage unplanted or plant a second crop, e.g., soybean for corn. If the farmer plants a second crop after the PP claim, the farmer receives a 35% of PP payment. The current PP provision fails to provide farmers with an incentive to plant a second crop; 99.9% of PP claiming farmers do not plant a second crop. Adjusting PP indemnity payment may encourage farmers to plant a second crop. The second essay explores this question using a stochastic simulation and suggests to increase the PP payment by 10%-15%. The third essay investigates why Wisconsin dairy farmers purchase more supplementary protection than California farmers in a MPP-Dairy introduced in the 2014 Farm Bill. MPP-Dairy provides dairy producers with margin protection when the national dairy margin is below a farmer selected threshold. This study determines whether conditional probabilities regarding regional and national margins have a role in farmer’s decision-making to purchase supplementary coverages using Copula models. Results indicate that Wisconsin farmers have higher conditional probabilities and purchase more buy-up coverages.

Texas is the leading U.S. producer of cotton, and the U.S. is the largest international market supplier of cotton. Risks and uncertainties plague Texas cotton producers with unpredictable weather, insects, diseases, and price variability. Risk management studies have examined the risk reducing capabilities of alternative management strategies, but few have looked at the interaction of using several strategies in different combinations. The research in this study focuses on managing risk faced by cotton farmers in Texas using irrigation, put options, and yield insurance. The primary objective was to analyze the interactions of irrigation, put options, and yield insurance as risk management strategies on the economic viability of a 1,000 acre cotton farm in the Lower Rio Grande Valley (LRGV) of Texas. The secondary objective was to determine the best combination of these strategies for decision makers with alternative preferences for risk aversion. Stochastic values for yields and prices were used in simulating a whole-farm financial statement for a 1000 acre furrow irrigated cotton farm in the LRGV with three types of risk management strategies. Net returns were simulated using a multivariate empirical distribution for 16 risk management scenarios. The scenarios were ranked across a range of risk aversion levels using stochastic efficiency with respect to a function. Analyses for risk averse decision makers showed that multiple irrigations are preferred, and that yield insurance is strongly preferred at lower irrigation levels. The benefits to purchasing put options increase with yields, so they are more beneficial when higher yields are expected from applying more irrigation applications.