Search results

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.

Farmer exchange rate (FER) is the ratio between a farmer's income and expenditure and is also an indicator of farmers’ welfare. There is little research regarding its use in risk modeling in crop insurance. This study seeks to propose a design for a household margin insurance scheme of the agricultural sector based on FER.

This research employs various risk modeling concepts, i.e. value at risk, loss models and premium calculation, to construct the proposed model. The standard linear, static and time-varying copula models are used to identify the dependency between variables involved in calculating FER.

First, FER can be considered as the primary variable for risk modeling in agricultural household margin insurance because it demonstrates farmers’ financial ability. Second, temporal dependence estimated using the time-varying copula can minimize errors, reduce the premium rate and result in a tighter guarantee's level of security.

This research extends the previous similar studies related to the use of index ratio in margin insurance loss modeling. Its authenticity is in the use of FER, which represents the farmers' trading capability. FER determines farmers’ losses by considering two aspects: the farmers’ income rate and their ability to fulfill their life and farming needs. Also, originality exists in the use of the time-varying copulas in identifying the dependence of the indices involved in calculating FER.

Since 1980, the principal form of crop loss assistance in the United States has been provided through the Federal Crop Insurance Program. The Federal Crop Insurance Act of 1980 was intended to replace disaster programs with a subsidized insurance program that farmers could depend on in the event of crop losses. Crop insurance was seen as preferable to disaster assistance because it was less costly and hence could be provided to more producers, was less likely to encourage moral hazard, and less likely to encourage producers to plant crops on marginal lands. Despite substantial growth in the program, the crop insurance program has failed to replace other disaster programs as the sole form of assistance. Over the past 20 years, producers received an estimated $15 billion in supplemental disaster payments in addition to $22 billion in crop insurance indemnities.

Examine the usability of rainfall and temperature outputs of a regional climate model (RCM) and meteorological drought indices to develop a macro-level risk transfer product to compensate the government of Central Java, Indonesia, for drought-related disaster payments to rice farmers.

Based on 0.5° gridded rainfall and temperature data (1960–2015) and projections of the WRF-RCM (2016–2040), the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) are calculated for Central Java over different time spans. The drought indices are correlated to annual and seasonal rice production, based on which a weather index insurance structure is developed.

The six-month SPI correlates best with the wet season rice production, which generates most output in Central Java. The SPI time series reveals that drought severity increases in future years (2016–2040) and leads to higher payouts from the weather index structure compared to the historical period (1960–2015).

The developed methodology in using SPI for historical and projected periods allows the development of weather index insurance in other regions which have a clear link between rainfall deficit and agricultural production volatility.

Meteorological drought indices are a viable alternative for weather index insurance, which is usually based on rainfall amounts. RCM outputs provide valuable insights into future climate variability and drought risk and prolong the time series, which should result in more robust weather index insurance products.

After several reforms of the common agricultural policy, domestic product prices and farm incomes have become more volatile in the EU. Risk-averse farmers are therefore seeking income stabilizing measures. Margin insurance is among the feasible options but is not yet established in the EU. The purpose of this paper is to explore such an insurance under EU conditions for a major crop.

The paper explores conditions for a viable margin insurance. It presents a modeled-loss trigger for a margin insurance scheme using wheat production in Austria as the case study.

While margin insurance products are widely used in the USA, such products are not available in the EU. Basis risk seems to be an important reason. An exploration of wheat production in Austria shows that heterogeneity among farms is relevant. The authors demonstrate an approach aiming to lower basis risks.

This paper presents a technically feasible approach to handle the basis risk of a margin insurance under EU conditions. Before such a product can be placed on the market, further research on systemic risk is needed. Market research is necessary to fine-tune the details of the product to meet the actual demand of farmers. Further empirical validation of the modeled losses is needed. Legal implications are not explored in this paper.

The insurance product presented here demonstrates a concept that is established in the USA under EU conditions. It is motivated by several shortcomings of income risk mitigation approaches in the EU.

Income risk may be seen as a problem of social policy. The approach shows that it can be addressed by market-oriented instruments.

To the authors’ knowledge, this paper is the first to propose a tool to handle basis risk for margin insurance products in agriculture in the EU. A special feature of the proposed approach is that it is not limited to a single product such as wheat.

This paper presents the systematic process framework to develop the suitable crop insurance for each agriculture farming region which has individual differences of associated crop, climate condition, including applicable technology to be implemented in crop insurance practice. This paper also studies the adoption of new insurance scheme to assess the willingness to join crop insurance program.

Crop insurance development has been performed through IDDI conceptual framework to illustrate the specific crop insurance diagram. Area-yield insurance as a type of index-based insurance advantages on reducing basis risk, adverse selection and moral hazard. This paper therefore aims to develop area-yield crop insurance, at a provincial level, focusing on rice insurance scheme for the protection of flood. The diagram demonstrates the structure of area-yield rice insurance associates with selected machine learning algorithm to evaluate indemnity payment and premium assessment applicable for Jasmine 105 rice farming in Ubon Ratchathani province. Technology acceptance model (TAM) is used for new insurance adoption testing.

The framework produces the visibly informative structure of crop insurance. Random Forest is the algorithm that gives high accuracy for specific collected data for rice farming in Ubon Ratchathani province to evaluate the rice production to calculate an indemnity payment. TAM shows that the level of adoption is high.

This paper originates the framework to generate the viable crop insurance that suitable to individual farming and contributes the idea of technology implementation in the new service of crop insurance scheme.

The purpose of this paper is to examine the efficiency of current subsidized crop insurance products (SCIPs) in Miyun county, in northern China, by testing the existence of adverse selection.

The authors examine the efficiency of SCIPs from a farmers' point of view by testing the existence of adverse selection using nonparametric tests and logit regression on data from individual interviews in Miyun County. Due to adverse selection, producers with more risk should be more likely to buy crop insurance in pursuit of the benefits from SCIPs.

However, both methods conclude no existence of adverse selection, which indicates that the programs are inefficient. This may not be surprising based on the issues of concern cited above. The opportunities and challenges of a growing interest in weather index insurance are discussed toward the end of the paper.

This paper weaves together literature on traditional and weather index insurance, along with primary data from a survey, to examine the pros and cons of each type of insurance. Implications for policy makers are to compare the tradeoffs between concerns of the two types of insurance examined here, traditional and weather index insurance. Ultimately, the political goals might best determine which option is most desirable.

The aim of this study is to estimate the technical efficiency of the massive and economically important crop of rice in Ecuador, and then conduct a comparison between groups of farmers with and without insurance.

The authors use an input-oriented data envelopment analysis approach (DEA) to estimate technical efficiency scores. The DEA is combined with the double bootstrap approach in Simar and Wilson (2007) to study factors that may affect technical efficiency. This method overcomes the traditional two-stage DEA approach frequently used in the efficiency literature. The authors thus research the role of insurance on rice efficiency production using this technique and sizeable field-level survey data from 376 rice farmers distributed in five provinces during the 2019 winter cycle in Ecuador.

Most uninsured rice farmers operate with increasing returns to scale, which means that farms improve their resource use efficiency by increasing their size. However, since scale efficiencies are relatively high, it appears that inefficiencies are explained by inadequate input use. Also, the authors find evidence that insured farmers have a negative relationship with technical efficiency in rice production. In other results, when exploring the influence of additional variables on efficiency, the authors find that parameters related to transplanting, high education, farm size and some locations are positive and statistically significant.

The results of this work are relevant for policymakers interested in evaluating technology performance, risk management instruments and farm efficiency in an industry in a developing country such as rice production in Ecuador.

This paper is the first attempt to estimate farm-level technical efficiency employing the double bootstrap approach to assess the efficiency and its determinants of Ecuadorian rice producers.

Maize production in China is exposed to pronounced yield risks, in particular weather risk, which is one of the most important and least controllable sources of risk in agriculture. The purpose of this paper is to analyze the extent to which weather index-based insurance can contribute to reducing the revenue risk in maize production caused by yield variations. An average farm producing maize is analyzed for each of eight Chinese provinces, six of which are part of the Northern Plains of China.

Data are based on the Statistical Yearbook of China and the Chinese Meteorological Administration. The used method of insurance pricing is burn analysis. Hedging effectiveness of precipitation index-based insurance is measured by the relative reduction of the standard deviation (SD) and the Value at Risk of maize revenues.

Results reveal that precipitation index-based insurance can cause a reduction of up to 15.2 percent of the SD and 38.7 percent of the Value at Risk with a 90 percent confidence level of maize revenues in the study area. However, there are big differences in the hedging efficiencies of precipitation index-based insurance measured at different weather stations in the various provinces. Therefore, it is recommended for insurance providers to analyze the hedging effectiveness of weather index-based insurance with regard to the geographical location of their reference weather station if they would like to offer weather index-based insurance products.

The absence of individual, long-term yield data in the study area prevents the evaluation of risk on individual farms. Thus, the hedging effectiveness can only be analyzed on an aggregated level of yield data and can rather be modeled for an average farm of a particular province.

To the author's knowledge, this paper is the first that investigates the hedging effectiveness of precipitation index-based insurance designed for reducing revenue risk of maize production in eight Chinese provinces.