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The purpose of this paper is to examine farmer preference for swine price index insurance in China focusing on whether Chinese farmers are willing to consider purchasing swine price index insurance, the premium they would like to pay, as well as the extend of heterogeneity in their preferences.

A sample of 443 swine farmers in Jiangsu and Henan provinces is collected and analyzed. An Ordered Probit model is used to analyze farmers’ willingness to buy swine price index insurance and a Tobit model is used to analyze farmers’ willingness to pay (WTP) for insurance premium.

Results show that some farmers are not willing to purchase swine price index insurance. However, WTP of majority of farmers is higher than what is prescribed in the current insurance policy. Factors affecting farmers’ willingness to buy varied between two provinces. Experience in purchasing traditional swine insurance and risk perception affect farmers’ willingness to buy in Jiangsu province, while joining agricultural cooperatives, experience in purchasing traditional swine insurance and understanding of swine price index insurance affect farmers’ willingness to buy in Henan province. Farmers with non-agricultural income, longer years of swine breeding, higher degree of specialization, experience in purchasing traditional insurance, higher understanding of swine price index insurance and trust in local governments, stronger risk perception and risk preference, and not being a member of agricultural cooperatives have higher WTP.

Few studies have been conducted on swine price index insurance in China. Even less information, to the authors’ knowledge, is available on farmer preferences. The research provides a timely contribution to understand the Chinese swine price index insurance market from the perspectives of farmers.

The purpose of this paper is to demonstrate how crop price insurance premium can be calculated using an option pricing model and how insurers can transfer underwriting risks in the futures market.

Based on data from spot and futures market in China, this paper develops an improved B-S model for the calculation of crop price insurance premium and tests the possibility of hedging underwriting risks by insurance firms in the futures market.

The authors find that spot price of crops in China can be estimated with agricultural commodity futures prices, and can be taken as the insured price for crop price insurance. The authors also find that improved B-S model yields better estimation of crop price insurance premium than traditional B-S model when spot price does not follow geometric Brownian motion. Finally, the authors find that hedging can be one good alternative for insurance firms to manage underwriting risks.

This paper develops an improved B-S model that is data-driven in nature. Insured price of the crop price insurance, or the exercise price used in the B-S model, is estimated from a co-integration model built on spot and futures market price series. Meanwhile, distributional patterns of spot price series, one important factor determining the applicability of B-S model, is factored into the improved B-S model so that the latter is more robust and friendly to data with varied distributions. This paper also verifies the possibility of hedging of underwriting risks by insurance firms in the futures market.

The purpose of this paper is to introduce a continuous time version of the speculative storage model of Deaton and Laroque (1992) and to use for pricing derivatives, in particular insurances on agricultural prices.

The methodology of financial engineering is used in order to find the partial differential equations that the dynamics of derivative prices have to satisfy. Furthermore, by using the Monte-Carlo method (and Feynman-Kac theorem) the insurance prices is computed.

Results of this paper show that insurance prices (and derivative prices in general) are heavily influenced by market structure, in particular, the demand function specifications. Furthermore, through an empirical analysis, the performance of the continuous time speculative storage model is compared with the geometric Brownian motion model. It is shown that the speculative storage model outperforms the actual data.

Since the agricultural insurances in many countries are subsidised by government, the results of this paper can be used by policy makers to measure changes in agricultural insurance premiums in scenarios that market experiences changes in demand. In the same manner, insurance companies and investors can use the results of this paper to better price agricultural derivatives.

The issue of agricultural insurance pricing (in general derivative pricing) is of great concern to policy makers, investors and insurance companies. To the author’s knowledge, an approach which uses the methodology of financial engineering to compute the insurance prices (in general derivatives) is new within the literature.

The purpose of this paper is to present readers with information on the state of provision of agricultural insurance as a means of protecting financial investment in agricultural productivity in Ghana.

The paper reviews interventions in the provision of agricultural insurance in Ghana and then examines what is currently being done in this area. The paper looks at issues arising from empirical evidence on agricultural insurance provision and links them to scholarly articles on these issues.

This paper shows that there has been considerable effort from the German Development Cooperation, the Ghana National Insurance Commission and government ministries and agencies, the Insurance sector in Ghana and stakeholder institutions leading to the creation of an agricultural insurance provider in Ghana. It is, however, evident from the results that the system is facing major challenges resulting primarily from the inability of the state to provide the needed policy and regulatory support that will assist the insurance sector in the development and delivery of the agricultural insurance products.

Even though there has been some research that has touched on agricultural insurance in Ghana, none of them has actually examined the current systems of providing the insurance since its inception. The paper therefore fills the gap of providing information on the current ongoing interventions for the provision of agricultural insurance for individuals and organizations that invest in the agricultural sector in Ghana.

Large-scale farm management in China has developed rapidly in recent years. Large-scale farmers face substantial operating risks, requiring extensive price risk management. However, the agricultural insurance and futures markets in China are incomplete. This study aims to analyze the price-risk-management behaviors of large-scale farmers under incomplete market conditions, with a focus on the interconnections between large scale farmers' subjective preferences (risk preferences, time preferences), liquidity constraints and their price risk management.

The authors construct an analysis framework to reveal the impact of large-scale farmers' risk preferences, time preferences and liquidity conditions on their price-risk-management behaviors under incomplete market conditions. Using data from field surveys and subjective preference experiments involving 409 large-scale grain farmers in China, an empirical analysis was conducted using the bivariate probit model.

The results show that risk-averse farmers will use risk transfer (such as contract farming) and risk diversification (such as multi-period sales) to avoid price risk. However, farmers subject to liquidity constraints and strong time preferences will not choose risk diversification, and the interaction between time preferences and liquidity constraints will strengthen this decision. The larger the farm-management scale, the greater the impact.

The authors focus on rapidly developed large-scale farm management in China. Appropriate price risk management is required by large-scale farmers due to their substantial operating risks. Considering the incomplete conditions of agricultural insurance and futures markets, the results of this study will help identify behavioral characteristics of large-scale farmers and optimize their price-risk-management strategies, further stabilizing large-scale farm management.

The purpose of this paper is twofold. First, the author proposes a financial engineering framework to model commodity prices based on market demand processes and demand functions. This framework explains the relation between demand, volatility and the leverage effect of commodities. It is also shown how the proposed framework can be used to price derivatives on commodity prices. Second, the author estimates the model parameters for agricultural commodities and discuss the implications of the results on derivative prices. In particular, the author see how leverage effect (or inverse leverage effect) is related to market demand.

This paper uses a power demand function along with the Cox, Ingersoll and Ross mean-reverting process to find the price process of commodities. Then by using the Ito theorem the constant elastic volatility (CEV) model is derived for the market prices. The partial differential equation that the dynamics of derivative prices satisfy is found and, by the Feynman-Kac theorem, the market derivative prices are provided within a Monte-Carlo simulation framework. Finally, by using a maximum likelihood estimator, the parameters of the CEV model for the agricultural commodity prices are found.

The results of this paper show that derivative prices on commodities are heavily affected by the elasticity of volatility and, consequently, by market demand elasticity. The empirical results show that different groups of agricultural commodities have different values of demand and volatility elasticity.

The results of this paper can be used by practitioners to price derivatives on commodity prices and by insurance companies to better price insurance contracts. As in many countries agricultural insurances are subsidised by the government, the results of this paper are useful for setting more efficient policies.

Approaches that use the methodology of financial engineering to model agricultural prices and compute the derivative prices are rather new within the literature and still need to be developed for further applications.

This paper aims to develop a market-driven mechanism for commodity price insurance in developing countries lacking access to futures markets or other forms of hedging products.

The model incorporates futures, exchange rate and local basis risk under the Black-Scholes framework to develop quanto (quantity adjusting option). When the domestic price of a commodity in a developing country is strongly correlated to the price in a futures market, price support premiums can be estimated. The authors use daily corn futures prices, exchange rate MXP/USD, and prices of corn and sorghum at several locations in Mexico.

The authors calculated the price insurance premium at various local markets in Mexico for corn and sorghum. The results are consistent with those for the USA, showing that relative price premiums are similar.

The results provide a benchmark to estimate the net welfare effects of government programs for agricultural price support.

The model shows that privately provided agricultural price insurance is feasible under certain conditions for developing countries without an established futures market.

This paper provides market-based agricultural options in Mexico which contributes to the existing government price support program.

The purpose of this paper is to address some of the fundamental issues surrounding crop insurance ratemaking, from the perspective of the reinsurer, through the development of a scientific pricing framework.

The generating process of the historical loss cost ratio's (LCR's) are reviewed, and the Erlang mixture distribution is proposed. A modified credibility approach is developed based on the Erlang mixture distribution and the liability weighted LCR, and information from the observed data of the individual region/province is integrated with the collective experience of the entire crop reinsurance program in Canada.

A comprehensive data set representing the entire crop insurance sector in Canada is used to show that the Erlang mixture distribution captures the tails of the data more accurately compared to conventional distributions. Further, the heterogeneous credibility premium based on the liability weighted LCR's is more conservative, and provides a more scientific approach to enhance the reinsurance pricing.

Credibility models are in the early stages of application in the area of agriculture insurance, therefore, the credibility models presented in this paper could be verified with data from other geographical regions.

The credibility-based Erlang mixture model proposed in this paper should be useful for crop insurers and reinsurers to enhance their ratemaking frameworks.

This is the first paper to introduce the Erlang mixture model in the context of agricultural risk modeling. Two modified versions of the Bühlmann-Straub credibility model are also presented based on the liability weighted LCR to enhance the reinsurance pricing framework.

The purpose of this paper is to propose an improved reinsurance pricing framework, which includes a crop yield forecasting model that integrates weather variables and crop production information from different geographically correlated regions using a new credibility estimator, and closed form reinsurance pricing formulas. A yield restatement approach to account for changing crop mix through time is also demonstrated.

The new crop yield forecasting model is empirically analyzed based on detailed farm-level data from Manitoba, Canada, covering 216 crop varieties from 19,238 farms from 1996 to 2011. As well, corresponding weather data from 30 stations, including daily temperature and precipitation, are considered. Algorithms that combine screening regression, cross-validation and principal component analysis are evaluated for the purpose of achieving efficient dimension reduction and model selection.

The results show that the new yield forecasting model provides significant improvements over the classical regression model, both in terms of in-sample and out-of-sample forecasting abilities.

The empirical analysis is limited to data from the province of Manitoba, Canada, and other regions may show different results.

This research is useful from a risk management perspective for insurers and reinsurers, and the framework may also be used to develop improved weather risk management strategies to help manage adverse weather events.

This is the first paper to integrate a credibility estimator for crop yield forecasting, and develop a closed form reinsurance pricing formula.