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The purpose of this paper is to assess the production risk for winter wheat producers in Beijing, China, particularly in its 13 districts.

A parametric approach is used to model wheat‐yield distribution for samples and the Kolmogorov‐Smirnov test is used to choose the most appropriate yield distribution. Parameters of the special yield distribution are estimated through the maximum likelihood estimation approach.

The Burr distribution is found to be the most appropriate parametric distribution to model winter wheat‐production risks for the districts of Beijing, except in the districts of Fengtai and Shunyi. Findings also show that the Johnson family distribution is the most appropriate model for these two districts (SB for the Fengtai District and SU for the Shunyi District). The wheat‐production loss ratios of the Beijing districts are between 6 and 15 percent, which is considered medium range in most regions. The highest production risks are located in the Western regions of Beijing (Mentougou and Fengtai) while the lowest production risk is located in the Southeastern region of Beijing (Daxing District).

To generate an objective yield trend and an accurate production risk assessment, linear moving average, instead of linear (or quadratic) regression, is used in this paper.

Hail risk management is essential for successful farm management in German fruit production, particularly because hail events and associated losses have increased in recent years. The purpose of this paper is to conduct a detailed risk analysis comparing different strategies to manage hail risk, taking into account farmers’ risk aversion and farm-specific conditions.

Within an expected utility framework, two different strategies for managing hail risk are compared: one belonging to the group of financial instruments (hail insurance) and the other to the group of technical instruments (anti-hail net). A unique data set comprising a ten-year time series of orchard-specific hail damage and hail insurance data is used.

For orchards with low local hail risk and low yield potential, not using hail risk mitigation is most efficient. For orchards with high local hail risk and high yield potential, anti-hail nets provide the highest certainty equivalents. For orchards with high local risk, but low yield potential, hail insurance is most efficient. For orchards, with low local risk, but high yield potential, the certainty equivalents are higher for anti-hail net, when the farmer is risk neutral or slightly risk-averse. With increasing risk aversion, hail insurance is most efficient, which can be explained by the greater degree of the instrument’s flexibility.

The novelty of the study lies in the direct comparison of the risk effects of anti-hail nets and hail insurance in fruit production.

The financial and economic turmoil that resulted from the Global Financial Crisis (GFC), included a marked increase in the volatility in real estate markets. Property asset prices were impacted by the real economy and market sentiment, particularly concerning the determination of risk. In an economic downturn, the perception of investment risk becomes increasingly important relative to overall total returns, and thus impacts on yields and performance of assets. In a recovery phase, and particularly within an environment of historically low government bonds, risk and return compete for importance. The aim of this paper is to assess the interrelationships and impacts on pricing between real estate risk, yield modelling outcomes and market sentiment in selective European city office markets.

This paper specifically considers the modelling of commercial property pricing in relation to the appetite for risk in the financial markets. The paper expands on previous work by determining a specific measure of risk pricing in relationship to changing financial market sentiment. The methodology underpinning the research specifically examines the scope for using national and international risk pricing within specific real estate markets in Europe.

This paper addresses whether there is a difference between the impact of risk on the pricing of real estate in international versus regional cities in Europe. The analysis, therefore, determines which city centre office markets in Europe have been most impacted by globalisation including the magnitude on real estate prices and market volatility. The outcome of the paper provides important insights into how changes in risk preferences in the international capital markets have driven and continues to drive yield movements under different market conditions.

The paper considers the driving forces which have led to the volatile movements of yields, emanating from the GFC.

This paper considers the property market effects on pricing of commercial real estate and the drivers in selected European cities.

The outcome of the paper provides important insights into how changes in risk preferences in the international capital markets have driven and continue to drive the yield movements in different real estate markets in Europe.

The purpose of this paper is to propose a risk-sharing model to coordinate the decision-making behavior of players in a cassava-based bioethanol supply chain under random yield and demand environment, so as to mitigate the yield and demand uncertainty risk and improve the bioethanol supply chain resiliency and performance.

The decision-making behavior under three models, namely, centralized model, decentralized model and risk-sharing model, are analyzed. An empirical test of the advantages and feasibility of the proposed risk-sharing model, as well as the test of yield uncertainty risk, risk-sharing coefficients and randomly fluctuating cassava market price on the decision-making behavior and performances are provided.

Though the proposed risk-sharing model cannot achieve the supply chain performance in the centralized model, it does help to encourage the farmers and the company to increase the supply of cassava and achieve the Pareto improvement of both players compared to the decentralized model. In particular, these improvements will be enlarged as the yield uncertainty risk is higher.

The findings will help decision makers in the bioethanol supply chain to understand how to mitigate the yield uncertainty risk and improve the supply chain resiliency under yield and demand uncertainty environment. It will also be conducive to ensure the supply of feedstock and the development of the bioethanol industry.

The proposed risk-sharing model incorporates the yield uncertainty risk, the random market demand and the hierarchical decision-making behavior structure of the bioethanol supply chain in the model.

Due to the low crop insurance participation by grain growers in the Pacific Northwest, the performance of insurance programs and the futures market is assessed in this area. Revenue insurance, combined with the futures and government programs, is identified as the optimal risk management portfolio. Although yield risk level, decision maker’s risk preference, and actuarial fairness of premiums can all affect farmers’ choices, the current subsidy policy is most influential. The varying subsidy levels induce farmers’ subsidy‐seeking incentive and suppress the risk‐reducing incentive. There is little diversification effect from growing two crops in the rotation instead of one.

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.

A simulation methodology is applied to the loan loss reserve process of an agricultural lender. Weaknesses of the point‐estimate approach to estimating loan loss reserves are addressed with a “bottom‐up” model. Modeling includes consideration of the producer’s and the lender’s diversification efforts. Implementation of this model will provide the lender a better understanding of the institution’s portfolio risk, as well as the credit risk associated with each loan. This study compares the lender’s loan loss estimates to a distribution of losses with associated probabilities. The comparative results could provide the lender a basis for setting probability levels for determining the regulatory required level of loan loss reserve.

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.

Community supported agriculture (CSA) programs allow consumers to buy a share of a farm’s production while providing working capital and risk management benefits for farmers. Several different types of CSA arrangements have emerged in the market with terms varying in the degree to which consumers share in the farm’s risk. No-arbitrage principles of futures and options pricing suggest that CSA shares should be priced to reflect the degree of risk transfer. The paper aims to discuss these issues.

The authors evaluate the three most common share types using a cross-sectional data set of 226 CSA farms from New England to determine if there is empirical evidence in support of the theoretical price relationship between share types.

The degree of risk transfer from farmers to consumers has a significant effect on the share price. There are statistically significant returns to scale and higher prices for organics. Farm characteristics and product offerings predict which type of shares is offered for sale.

The data set does not contain information pertaining to actual deliveries, expected deliveries, variance of expected deliveries, or covariance information; thus differences in share prices could be due to differences in these uncontrolled factors.

This paper provides empirical evidence that CSA share prices reflect the degree of risk transferred from the producer to the consumer. It also highlights challenges in conducting empirical work pertaining to CSA contracting.

In the light of past financial and economic turmoil, there has been a marked increase in the volatility in real estate markets. This has impacted on the pricing of property assets, partly through market sentiment and particularly concerning risk. It also limits modelling accuracy model accuracy. The purpose of this paper is to create a new variable and model to enhance analysis of what drives real estate yields incorporating market sentiment to risk.

This paper specifically considers the modelling of property pricing within a volatile economic environment. The theoretical context begins by analysing the relationship between property yields and government bonds. The analytical context then moves on to specifically include a measurement of risk which stresses its role and importance in investment markets since the Global Financial Crisis. The model thus incorporates macroeconomic and real estate data, together with an international risk multiplier, which is calculated within the paper.

The paper finds the use of measurements of market sentiment and risk are more powerful tools for modelling yields than previous techniques alone.

This is an initial paper outlining the creation of sentiment and risk measurements in the financial market and showing an example of its application to a commercial real estate market. The implication is that this could add a major new explanatory variable to modelling of yields.

The paper highlights the importance of risk in the pricing of commercial real estate, over and above normal variables. It highlights how this can help explain over and undershooting of yields within commercial real estate which would be of great importance in the investment world.

This paper attempts to explicitly measure market sentiment, pricing of risk and how this impacts real estate pricing.