Martin Odening

Can crop yield risk be globally diversified

In 2007 and 2008 world food markets observed a significant price boom. Crop failures simultaneously occurring in some of the world’s major production regions have been quoted as one factor among others for the price boom. Against this background, we analyse the stochasticity of crop yields in major production areas. The analysis is exemplified for wheat, which is one of the most important crops worldwide. Particular attention is given to the stochastic dependence of yields in different regions. Thereby we address the question of whether local fluctuations of yields can be smoothed by international agricultural trade, i.e. by global diversification. The analysis is based on the copula approach, which requires less restrictive assumptions compared with linear correlations. The use of copulas allows for a more reliable estimation of extreme yield shortfalls, which are of particular interest in this application. Our calculations reveal that a production shortfall, such as in 2007, is not a once in a lifetime event. Instead, from a statistical point of view, similar production conditions will occur every 15 years.

Analysis of rainfall derivatives using daily precipitation models: opportunities and pitfalls

This study examines rainfall variability and its implications for wheat production risk in northeast Germany. The hedging effectiveness of rainfall options and the role of geographical basis risk are analyzed using a daily precipitation model. Simpler pricing methods such as the burn analysis and the index value simulation serve as benchmarks for comparison. It is found that the choice of statistical approach may lead to considerable differences in the estimation results. Daily precipitation models should be used with some caution in the context of derivative pricing because they tend to underestimate rainfall variability. This is unexpected, because daily simulation models are usually preferred in the context of temperature‐based weather indexes.

Systemic Weather Risk and Crop Insurance: The Case of China

The supply of affordable crop insurance is hampered by the existence of systemic weather risk which results in large risk premiums. In this article, we assess the systemic nature of weather risk for 17 agricultural production regions in China and explore the possibility of spatial diversification of this risk. We simulate the buffer load of hypothetical temperature-based insurance and investigate the relation between the size of the buffer load and the size of the trading area of the insurance. The analysis makes use of a hierarchical Archimedean copula approach (HAC) which allows flexible modeling of the joint loss distribution and reveals the dependence structure of losses in different insured regions. Our results show a significant decrease of the required risk loading when the insured area expands. Nevertheless, a considerable part of undiversifiable risk remains with the insurer. We find that the spatial diversification effect depends on the type of the weather index and the strike level of the insurance. Our findings are relevant for insurers and insurance regulators as they shed light on the viability of private crop insurance in China.

Management of climate risks in agriculture–will weather derivatives permeate?

It is a matter of common knowledge that weather represents the major source of uncertainty in crop production. It is to be expected that weather fluctuations will increase in the future due to climate change. Traditionally, farmers tried to protect themselves against weather-related yield variations by buying insurances. More recently, there has been a discussion regarding the use of weather derivatives to safeguard against volumetric risks. Although weather derivatives display advantages over traditional insurances, there is only a relatively small market for these products in agriculture. This is partly attributed to the fact that it is unclear whether and to what extent weather derivatives are a useful instrument of risk management in agriculture. This study applies real yield and weather data from Northeast Germany in order to quantify the risk-reducing effect that can be achieved in wheat production by using precipitation options. To do so stochastic simulation is used. The hedging effectiveness is controlled by the contract design (index, strike level, tick size). However, the local basis risk and the geographical basis risk remain with the farmer. We separate both causes of basis risk and reveal the extent of each. This enables conclusions regarding the design of weather derivatives; thus the question dealt with here is relevant both for farmers and for potential sellers of weather derivatives.

Path-Dependence without Increasing Returns to Scale and Network Externalities

Abstract The paper presents a simple model of a locked-in situation. Recent literature on path-dependence has explained locked-in situations with increasing returns to scale or network externalities. The model of this paper is a model of a single firm (there is no network of different agents) with a given size (there are no economies of scale). Our analyses shows that complementarity of a firms assets and sunk costs can be sufficient for path-dependence.

On the Systemic Nature of Weather Risk

Systemic weather risk is a major obstacle for the formation of private (non- subsidized) crop insurance. This paper explores the possibility of spatial diversification of insurance by estimating the joint occurrence of unfavorable weather conditions in different locations. For that purpose copula methods are employed that allow an adequate description of stochastic dependencies between multivariate random variables. The estimation procedure is applied to weather data in Germany. Our results indicate that indemnity payments based on temperature as well as on cumulative rainfall show strong stochastic dependence even at a national scale. Thus the possibility to reduce risk exposure by increasing the trading area of the insurance is limited. Irrespective of their economic implications our results pinpoint the necessity of a proper statistical modeling of the dependence structure of multivariate random variables. The usual approach of measuring stochastic dependence with linear correlation coefficients turned out to be questionable in the context of weather insurance as it may overestimate diversification effects considerably.

Economic Hysteresis in Hog Production

German hog production only responds in a very limited way to price fluctuations in the pork market. The hog production concentrates on a few regions though it is not bound to special natural conditions such as soil quality. Furthermore, the volume of production does not vary over time. Relatively high market risks, sunk costs and the flexibility of the decision maker to defer investments characterize decision problems in hog production. Thus the real option approach is chosen to explain the inertia in production capacity. By the use of panel data of specialized hog farms from the German Farm Accountancy Data Network, an empirical investment model is estimated. Formally, the model has the structure of a generalized ordered probit model. This approach allows to test for economic hysteresis in the adjustment of hog production capacity. The results confirm that uncertainty and flexibility widen the optimal range of inaction.

Indifference Pricing of Weather Derivatives

Weather derivatives are difficult to price due to the nontradability of weather and the absence of liquid secondary markets for these contracts. We use the concept of indifference pricing to develop a model for calculating the willingness to pay for weather insurance. Compared with other approaches, indifference pricing is less ambitious since it does not attempt to predict a transacted market price. The application of indifference pricing in the case of German crop producers shows that their willingness to pay for weather insurance depends on the production program and varies regionally. This suggests the development of tailored insurance products. Copyright 2008, Oxford University Press.

Using extreme value theory to estimate value‐at‐risk

This study examines problems that may occur when conventional Value‐at‐Risk (VaR) estimators are used to quantify market risks in an agricultural context. For example, standard VaR methods, such as the variance‐covariance method or historical simulation, can fail when the return distribution is fat tailed. This problem is aggravated when long‐term VaR forecasts are desired. Extreme Value Theory (EVT) is proposed to overcome these problems. The application of EVT is illustrated by an example from the German hog market. Multi‐period VaR forecasts derived by EVT are found to deviate considerably from standard forecasts. We conclude that EVT is a useful complement to traditional VaR methods.

Portfolio effects and the willingness to pay for weather insurances

Since the mid-1990s, agricultural economists have discussed the relevance of index-based insurances, also called “weather derivatives”, as hedging instruments for volumetric risks in agriculture. Motivated by the question of how weather derivatives should be priced for agricultural firms, this paper describes an extended risk-programming model which can be used to determine farmers’ willingness to pay (demand function) for weather derivative’s farm-specific risk reduction capacity and the individual farmer’s risk acceptance. Applying it to the exemplary case of a Brandenburg farm reveals that even a highly standardized contract which is based on the accumulated rainfall at the capital’s meteorological station in Berlin-Tempelhof generates a relevant willingness to pay. Our findings suggest that a potential underwriter could even add a loading on the actuarially fair price which exceeds the level of traditional insurances. Since translation costs are low compared to insurance contracts, this finding indicates there may be a relevant trading potential.