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This paper aims to improve the delimitation of plant growth stages in the context of weather index insurance design. We propose a data-driven phase division that minimizes estimation errors in the weather-yield relationship and investigate whether it can substitute an expert-based determination of plant growth phases. We combine this procedure with various statistical and machine learning estimation methods and compare their performance.

Using the example of winter barley, we divide the complete growth cycle into four sub-phases based on phenology reports and expert instructions and evaluate all combinations of start and end points of the various growth stages by their estimation errors of the respective yield models. Some of the most commonly used statistical and machine learning methods are employed to model the weather-yield relationship with each selected method we applied.

Our results confirm that the fit of crop-yield models can be improved by disaggregation of the vegetation period. Moreover, we find that the data-driven approach leads to similar division points as the expert-based approach. Regarding the statistical model, in terms of yield model prediction accuracy, Support Vector Machine ranks first and Polynomial Regression last; however, the performance across different methods exhibits only minor differences.

This research addresses the challenge of separating plant growth stages when phenology information is unavailable. Moreover, it evaluates the performance of statistical and machine learning methods in the context of crop yield prediction. The suggested phase-division in conjunction with advanced statistical methods offers promising avenues for improving weather index insurance design.

The purpose of this paper is to investigate and compare the impact of order imbalance on returns, liquidity and price volatility in agricultural futures markets on an intraday basis. The authors examine whether order imbalance is more powerful to explain variations in asset prices compared to other indicators of trading activity, particularly trading volume.

Using Chicago Mercantile Exchange best bid best offer data, the impact of order imbalance is analyzed via regression analyses. The analyses are carried out for corn, wheat, soy, live cattle and lean hogs in March 2008 and March 2016.

Results confirm the positive relation between order imbalance and returns as well as between order imbalance and price volatility as suggested by market microstructure models. Order imbalance, however, does not generally outperform trading volume as an explanatory variable.

For some contracts, returns can be predicted using lagged order imbalance. This offers the opportunity to derive profitable trading strategies.

This paper is one of the first attempts to explore the relationship between order imbalance and returns, liquidity and volatility for agricultural commodity futures on an intraday basis, accounting for the increased trading volume and for the high speed at which new information enters the market in an electronic trading environment.

Liquidity is an important aspect of market efficiency. The purpose of this paper is threefold: first, this paper aims to discuss indicators that provide information about liquidity in agricultural land markets. Second, this paper aims to reflect on determinants of market liquidity and analyze the relationship with land prices. Third, this paper aims to conduct an empirical analysis for Germany that illustrates these concepts and allows hypothesis testing.

This study reviews liquidity dimensions and measurement in financial markets and derives indicators applicable to farmland markets. In an empirical analysis, this study exhibits the spatial and temporal variability of land market liquidity in Lower Saxony, a German federal state with the highest agricultural production value. This study uses a rich dataset that includes 72,547 sale transactions of arable land between 1990 and 2018. The research focuses on volume-based (number of transactions, volume and turnover) and time-based (trading frequency and durations) measures. A panel vector autoregression and Granger causality tests are applied to investigate the relation between land turnover and land prices.

The paper confirms the thinness of farmland markets but also reveals regional and temporal heterogeneity of land market liquidity. This study finds that the relation between market liquidity and prices is ambiguous. This study concludes that a high demand from expanding farms absorbs supply shocks regardless of the current price level in agricultural land markets.

Even though the relevance of agricultural land markets’ thinness is widely acknowledged in the literature, this paper is one of the first attempts to measure liquidity in agricultural land markets and to explain its relationship with land prices.

The numerical solution of contact problems via the penalty method yields approximate satisfaction of contact constraints. The solution can be improved using augmentation schemes. However their efficiency is strongly dependent on the value of the penalty parameter and usually results in a poor rate of convergence to the exact solution. In this paper we propose a new method to perform the augmentations. It is based on estimated values of the augmented Lagrangians. At each augmentation the converged state is used to extract some data. Such information updates a database used for the Lagrangian estimation. The prediction is primarily based on the evolution of the constraint violation with respect to the evolution of the contact forces. The proposed method is characterised by a noticeable efficiency in detecting nearly exact contact forces, and by superlinear convergence for the subsequent minimisation of the residual of constraints. Remarkably, the method is relatively insensitive to the penalty parameter. This allows a solution which fulfils the constraints very rapidly, even when using penalty values close to zero.

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.

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.

The purpose of this paper is to review some challenges of insuring weather risk in agriculture and to discuss potential remedies for these problems.

The paper is developed as a narrative on weather insurance based largely on existing literature.

Weather risks show characteristics that often violate classical requirements for insurability. First, some weather risks, particularly slowly emerging weather perils like drought, are spatially correlated and cause systemic risks. Second, climatic change may increase the volatility of weather variables and lead to non-stationary loss distributions, which causes difficulties in actuarial ratemaking. Third, limited availability of yield and weather data hinders the estimation of reliable loss distributions.

Some of the approaches discussed in this review, such as time diversification, local test procedures and the augmentation of observational data by expert knowledge, can be useful for crop insurance companies to improve their risk management and product design.

This study provides background and development information regarding weather insurance and also presents statistical tools and actuarial methods that support the assessment of weather risks as well as the design of weather and yield insurance products.

The purpose of this paper is to assess the losses of weather‐related insurance at different regional levels. The possibility of spatial diversification of insurance is explored by estimating the joint occurrence on unfavorable weather conditions in different locations, looking particularly at the tail behavior of the loss distribution.

Joint weather‐related losses are estimated using copulas. Copulas avoid the direct estimation of multivariate distributions but allow for much greater flexibility in modeling the dependence structure of weather risks compared with simple correlation coefficients.

Results indicate that indemnity payments based on temperature as well as on cumulative rainfall show strong stochastic dependence even at a large regional scale. Thus the possibility to reduce risk exposure by increasing the trading area of insurance is limited.

The empirical findings are limited by a rather weak database. In that case the estimation of high‐dimensional copulas leads to large estimation errors.

The paper includes implications for the quantification of systemic weather risk which is important for the rate making of crop insurance and reinsurance.

This paper's results highlight how important the choice of the statistical approach is when modeling the dependence structure of weather risks.

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.

The purpose of this paper is to examine the aggregate demand for single- and multi-year crop insurance contracts and to discuss market potential for multi-year crop insurances.

In this paper the authors develop a dynamic discrete choice model of insurance alternatives, in which single- and multi-year insurance contracts are offered to heterogeneous risk averse farmers. The farmers determine their insurances choices based on inter-temporal utilities.

The results show that in a competitive insurance market with heterogeneous risk averse farmers, there is simultaneous demand for both insurance contracts. Moreover, the introduction of multi-year contracts enhances the market penetration of insurance products.

The effect of introducing multi-year crop insurance is moderate when applying the model to US corn production. In practice, however, the increase of insurance demand could be more pronounced because we did not consider marketing and administrative costs and thus ignore this cost reduction potential of multi-year insurance.

This study adds to the literature analyzing the feasibility of multi-year crop insurance and also shows that there is market potential for multi-year crop insurance.