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The purpose of this paper is to analyze the effects of data aggregation and farm‐level crop acreage on the level of natural hedge, i.e. the level of price‐yield correlations, which is an important issue in risk modeling and management.

Swiss FADN data for five crops covering the period 2002‐2009 are used to estimate price‐yield correlations at the farm‐ as well as on an aggregated level. Tobit regressions are used to estimate empirical relationships between the level of natural hedge and the underlying crop acreage.

Price‐yield correlations differ significantly between farm‐ and aggregated‐level. More specifically, the natural hedge observed at the farm‐level is much smaller, i.e. correlations are closer to zero. Taking correlations from aggregated levels thus leads to an underestimation of farm‐level revenue variability. Furthermore, it is found that larger farms have a stronger natural hedge. For instance, a 1 percent increase in area under maize and intensive barley leads to a change in the correlation by −0.02 and −0.08, respectively.

The natural hedge is often approximated with correlations observed at more aggregated levels, e.g. the county level. The results show that this implies errors in risk assessment and modeling as well as insurance applications. Thus, farm‐level estimates should be used. The here presented relationship between price‐yield correlations and farm‐level crop acreage can be used to derive better information on levels of the natural hedge.

Even though the effects of data aggregation on price‐yield correlations have been discussed in earlier research, this paper is the first to also account for on‐farm effects of underlying crop acreage on levels of natural hedge. It is found that this simple relationship can be useful in risk management and modeling applications.

This study estimates the probability density function of the government’s net income from reinsuring crop insurance for corn, wheat, and soybeans. Based on 1997 data, it is estimated there is a 5% probability that the government will need to reimburse at least $1 billion to insurance companies, and that the fair value of the government’s reinsurance services to insurance firms equals $78.7 million. In addition, various hedging strategies are examined for their potential to reduce the government’s reinsurance risk. The risk reduction achievable by hedging is appreciable, but use of derivative contracts alone is clearly no panacea.

The purpose of this paper is to analyze the effect of the 2008 Farm Bill's average crop revenue election (ACRE) program on the risk‐reducing effectiveness of crop insurance products.

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.

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.

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.

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.

The risk‐reducing effectiveness of the 2008 Farm Bill's ACRE program is analyzed, and as a methodological contribution the copula approach is used to model the multivariate distribution of yields and prices.

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.

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.

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).

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.

This paper examines the effect of overconfident yield forecasting (optimism bias) on crop insurance coverage level choices across both yield and revenue insurance.

This study simulates a representative producer’s preferred coverage level for both yield and revenue insurance under three potential models of decision-making and four potential manifestations of overconfident yield forecasting. The study then uses this framework to examine how coverage level choices change as overconfidence increases (decreases).

As overconfidence increases, producers prefer lower levels of crop insurance coverage than they would otherwise prefer, with extreme overconfidence inducing farmers to buy no insurance at all. While overconfidence affects cross-coverage demand for revenue and yield insurance similarly, this effect is more pronounced for yield insurance. Cross-coverage level demand for revenue insurance is relatively stable across changes in the correlation between prices and yields.

This research has important implications for crop insurance subsidy design and crop insurance demand modeling.

There is a growing body of literature suggesting that producers are overconfident with regard to their future yield risk and that this bias reduces their willingness to pay for risk management tools such as crop insurance. This is the first study to look at how such overconfidence affects cross-coverage level demand for crop insurance.

Returns to a model farm are simulated to assess the impact of marketing and insurance risk management tools as measured by mean net returns and returns at 5% value‐at‐risk (VaR). Results indicate that revenue insurance strategies and strategies involving a combination of price and yield protection provide substantial downside revenue protection, while mean net returns only modestly differ from the benchmark harvest sale strategy when considering all years between 1986 and 2000.

The purpose of this paper is to use simulation analysis to assess farmer choice between crop insurance and supplemental revenue options as proposed during development of the Agricultural Act of 2014.

The certainty equivalent of wealth is used to rank farm choices and assess the effects of supplemental revenue options on the crop insurance plan and coverage level chosen by the producer under a range of farm attributes. The risk-reducing effectiveness of the select programs is also examined through their impact on the farm revenue distribution. The dependence structure of yield and prices is modeled by applying copula techniques on historical data.

Farm program supplemental revenue programs generally have no effect on crop insurance choices. Crop insurance supplemental revenue programs typically reduce crop insurance coverage at high coverage levels. An individual plan of crop insurance combined with a supplemental revenue insurance plan may substitute for incumbent area crop insurance plans.

The analysis provides insights into farmers’ possible choices by focussing on alternative crops and farm attributes and extensive scenarios, using current data, crop insurance plans and programs contained in the 2014 Farm Bill and related bills. The results should be of value to policy officials and producers in regards to the design and use of risk management tools.

The purpose of this paper is to perform an analysis of potential benefits from usage of the futures markets for the farmers. The national commodity exchanges were established in India in the year 2003-2004. Though there has been a spectacular growth in trading volumes in these exchanges, participation of farmers in these markets has been very low. Efforts are being made to increase the awareness and participation of farmers in these markets. As such efforts are being made, it is critical to analyse the potential benefits from usage of the futures markets for the farmers. Our study performs such an analysis for soybean farmers in the Dewas district of Madhya Pradesh state in India.

The authors estimate the optimal hedge ratios in futures markets for farmers in different scenarios characterised by varying levels of different parameters relevant to the farmer. For these optimal hedge ratios, we then estimate the benefits from hedging defined as the change in certainty equivalent income (CEI) due to hedging.

Results indicate that the CEI gain due to hedging is positively related to the farmer’s risk aversion and inversely related to farmer’s price expectations and transaction costs. Also, only when the risk aversion is high, the CEI gain is positively related to the natural hedge. Thus, for a farmer with high risk aversion, hedging acts as a substitute to the natural hedge.

This is the first study that analyses the hedging for farmer in the Indian context by considering yield risk while doing so. Also, their study establishes a relationship between risk aversion, the natural hedge and benefits from hedging in futures markets for the farmer.

The purpose of this paper is to evaluate the risk management benefits provided by the supplemental coverage option (SCO) insurance plan which was created in the 2014 Farm Bill. Specifically, the marginal expected utility benefits are compared with the potential additional subsidy cost introduced by the new program for a stylized example of a corn producer.

The paper uses a stylized simulation model examines the preferred insurance program choice for a typical Midwestern corn farmer. The expected utility of the farmer is calculated under their preferred insurance program choice both with and without the availability of the SCO program, and compared to the case where crop insurance is not available. Scenarios are examined for a range of farmer risk aversion levels, different levels of correlation between farm-level and county-level corn yields, and case with and without insurance premium subsidies.

The SCO program is found to enter into the preferred insurance program choice for risk averse farmers. As risk aversion increases, farmers are estimated to prefer higher coverage levels for individual products along with SCO coverage. While the availability of existing crop insurance programs are shown to substantially increase the expected utility of farmers, the marginal impact of adding SCO to the crop insurance program is relatively small. Furthermore, the additional expected benefits generated by SCO are shown to include both risk management and expected return components. With subsidies removed, the estimated marginal benefits provided by SCO are reduced significantly.

The findings of this paper can help inform the policy debate for future farm bills as agricultural support programs continue to evolve. The results in this paper can also be used to help explain farm-level decision making related to crop insurance program choices.

This paper contributes to the literature by documenting a new, federally supported risk management programs made available to farmers in the 2014 Farm Bill and evaluates the marginal benefits the SCO program offers US crop producers.

The purpose of this paper is to assess how direct payments (DPs) of the Common Agricultural Policy affect income and revenue variability faced by Italian farmers.

Balanced farm-level panel data are used to construct coefficients of variation over the period 2003-2012. Nonlinear robust regression techniques are used to measure the effect of DP, farm size, fixity in resources, labor intensity, farm production orientation, and specialization on the variability of farm income (FI) and farm revenue. This is done on the overall sample as well as on subsamples of farms located in different regions and belonging to different types of farming.

DPs have mixed effects on the variability of FI. While a negative and significant relationship is found on the whole national sample, this is not generally the case when models are run on the considered subsamples. On the contrary, DPs have always significant variability increasing effects on revenue. This suggests that DPs reduce the degree of risk that farmers face allowing them to engage in riskier activities. Thus, DPs are less effective than expected in terms of income stabilization because these distort farmers’ risk management behavior. Because of this, DPs could constrain the development of markets for risk management instruments and reduce the effectiveness of policies supporting the use of these instruments.

The analysis is inspired by El Benni et al. (2012) but uses a different approach, applies it to a different country, and yields different results. Volatility measures are calculated over more years, and the paper accounts for differences in farm production orientation and is not based on an unbalanced panel of farms. Because of these differences, the authors obtained different results regarding the correlation between DP and income and, even more, revenue variability. Finally, comparing the results of models referring to FI and farm revenue improves the author’s understanding of the impact of DP on farmers’ risk management behavior and allows interesting policy considerations.