Meteorological forecasts and the pricing of weather derivatives

In usual pricing approaches for weather derivatives, forward-looking information such as meteorological weather forecasts is not considered. Thus, important knowledge used by market participants is ignored in theory. By extending a standard model for the daily temperature, this paper allows the incorporation of meteorological forecasts in the framework of weather derivative pricing and is able to estimate the information gain compared to a benchmark model without meteorological forecasts. This approach is applied for temperature futures referring to New York, Minneapolis and Cincinnati with forecast data 13 days in advance. Despite this relatively short forecast horizon, the models using meteorological forecasts outperform the classical approach and more accurately forecast the market prices of the temperature futures traded at the Chicago Mercantile Exchange (CME). Moreover, a concentration on the last two months or on days with actual trading improves the results.Weather forecasting, weather risk, price forecasting, nancial markets, temperature futures, CME

Estimating Investment Equations in Imperfect Capital Markets

Numerous studies have tried to provide a better understanding of firm-level investment behaviour using econometric models. The model specification of more recent studies has been based on two main approaches. The first, the real options approach, focuses on irreversibility and uncertainty in perfect capital markets; of particular interest is the range of inaction caused by sunk costs. The second, the neo-institutional finance theory, emphasises capital market imperfections and firms’ released liquidity constraints. Empirical applications of the latter theory often refer to linear econometric models to prove these imperfections and thus do not account for the range of inaction caused by irreversibility. In this study, a generalised Tobit model based on an augmented q model is developed with the intention of considering the coexistence of irreversibility and capital market imperfections. Simulation-based experiments allow investigating the properties of this model. It can be shown how disregarding irreversibility reduces effectiveness of simpler linear models.q model, uncertainty, capital market imperfections, generalised Tobit model

Pre-harvest prediction of wheat’s protein content in Northeast Germany for market players based on weather information

Auf dem Weizenmarkt sind Schwankungen des Qualitätsmerkmals „Proteingehalt“ für Marktakteure eine große Herausforderung, da vom Handel und nachgelagerten Bereichen eine gleichbleibende Qualität verlangt wird. Als bestimmende Faktoren für die Variabilität des Protein­gehalts gelten auch meteorologische Faktoren, weshalb dem Verständnis ihrer Wirkungsweise eine große Bedeutung zukommt. In diesem Beitrag wird daher der Einfluss der Wetterparameter „Temperatur“, „Niederschlag“ und „Sonnenscheindauer“ auf den Proteingehalt des Weizens untersucht und ein Vorernte-Vorhersagemodell für den Proteingehalt von Weizen im Nordosten Deutschlands entwickelt. Dazu wird ein Random Intercept Modell auf Basis von Wetterinformationen von 16 Wetterstationen des Bundeslandes Mecklenburg-Vorpommern und dazugehörigen Mittelwerten von Proteindaten aus 148.800 Weizenproben aus den Jahren 2004 bis 2015, die von umliegenden Landwirten an den Landhändler geliefert wurden, geschätzt. Das marginale R² beträgt 0,523 und das konditionale R² liegt bei 0,540. Folglich können 52,3% der jährlichen Varianz im Proteingehalt durch die im Modell enthaltenen Variablen erklärt werden. Die Temperatur im Juni hat den höchsten, positiven Einfluss auf den Proteingehalt des Weizens. Die Wirkung der Niederschläge ist negativ. Die Sonnenscheindauer hat einen statistisch signifikanten Einfluss auf die Proteinbildung. Allerdings kann keine einheitliche Wirkungsrichtung der Sonnenscheindauer über alle Frühjahrsmonate ermittelt werden.On the wheat market, fluctuations in the quality characteristic protein content are a major challenge for market operators since trade requires constant protein content. The determining factors for the annual variability of the protein content are meteorological factors. For this reason, understanding of the factors’ mode of action is of importance. In this article, the influence of weather parameters, such as temperature, precipitation and sunshine duration, on the wheat’s protein content is investigated and a pre-harvest prediction model for the protein content of wheat in north-east Germany is developed. For this purpose, a random intercept model based on weather information from 16 weather stations of the German federal state of Mecklenburg-Western Pomerania and protein data from the corresponding mean protein contents of 148,800 protein samples over the years 2004 to 2015 supplied by the surrounding farmers to the land trader, is estimated. The marginal R² is 0.523 and the conditional R² amounts to 0.540. Thus, 52.3% of the annual variance in the protein content can be explained by the variables contained in the model. Temperature in June has the highest positive effect on the protein content of wheat. The effect of precipitation is negative. The sunshine dura­tion has a significant influence on protein formation. However, no uniform direction of action of the sunshine duration can be determined over all spring months

Die Bestimmung optimaler Anbaustrategien

Nach wie vor bestimmen Landwirte ihr Produktionsprogramm ohne Einsatz expliziter Optimierungsmodelle. Dabei werden insbesondere Preis- und Ertragsunsicherheiten nicht ausreichend berücksichtigt. Eine realitätsgetreue Berücksichtigung der Unsicherheit hinsichtlich der Einzeldeckungsbeiträge ist aber technisch möglich. Gleichzeitig können dadurch – wie dieser Beitrag zeigt – die Planungsergebnisse erheblich verbessert werden.Peer Reviewe

Rubber vs. oil palm: an analysis of factors influencing smallholders' crop choice in Jambi, Indonesia

The rapid expansion of the oil palm area in many tropical countries has raised concerns about its negative impact on local communities, food security, and on the environment. While the expansion of oil palm in early stages was mainly driven by large private and public companies, it is expected that smallholders will outnumber large estates in the near future. For policy formulation it is hence important to better understand who these smallholders are and why they have started to cultivate oil palm. In this paper, we used a rich dataset collected in the province of Jambi, which is one of the most important production areas for oil palm, to analyse smallholders’ decision making by combining qualitative, quantitative, and experimental methods. We identified agricultural expertise, lacking flexibility in labour requirements, availability of seedlings, and investment costs as the major constraints for farmers to cultivate oil palm. Important reasons for oil palm cultivation are the higher returns to labour and the shorter immature phase of oil palm. We also showed that oil palm farmers are neither risk-averse nor risk-loving, rather, they appear to be risk-neutral

Meteorological forecasts and the pricing of weather derivatives

In usual pricing approaches for weather derivatives, forward-looking information such as meteorological weather forecasts is not considered. Thus, important knowledge used by market participants is ignored in theory. By extending a standard model for the daily temperature, this paper allows the incorporation of meteorological forecasts in the framework of weather derivative pricing and is able to estimate the information gain compared to a benchmark model without meteorological forecasts. This approach is applied for temperature futures referring to New York, Minneapolis and Cincinnati with forecast data 13 days in advance. Despite this relatively short forecast horizon, the models using meteorological forecasts outperform the classical approach and more accurately forecast the market prices of the temperature futures traded at the Chicago Mercantile Exchange (CME). Moreover, a concentration on the last two months or on days with actual trading improves the results

Estimating Investment Equations in Imperfect Capital Markets

Numerous studies have tried to provide a better understanding of firm-level investment behaviour using econometric models. The model specification of more recent studies has been based on two main approaches. The first, the real options approach, focuses on irreversibility and uncertainty in perfect capital markets; of particular interest is the range of inaction caused by sunk costs. The second, the neo-institutional finance theory, emphasises capital market imperfections and firms’ released liquidity constraints. Empirical applications of the latter theory often refer to linear econometric models to prove these imperfections and thus do not account for the range of inaction caused by irreversibility. In this study, a generalised Tobit model based on an augmented q model is developed with the intention of considering the coexistence of irreversibility and capital market imperfections. Simulation-based experiments allow investigating the properties of this model. It can be shown how disregarding irreversibility reduces effectiveness of simpler linear models

Evaluating the role of financial flexibility in farmers' investment decisions using latent class analysis

The global structural change in the agricultural sector entails adaptation processes, which often involve leveraged investments resulting in decreasing equity ratios of farms. Lower equity ratios can be followed by a reduction in the financial flexibility of farms. If additional investments with debt capital are made, the financial flexibility may be further restricted. The question that arises is if farm managers already consider the financial flexibility when making investment decisions. In the present study, farmers are faced with hypothetical investment alternatives in a discrete choice experiment. The investment alternatives differ in their profitability, the risk involved and in their impact on the farm's financial flexibility. The estimation of a latent class model, with four classes, reveals that in all classes the amount of debt capital necessary for the investment is relevant for the farmers' decision. In three of the four classes, the farmers' utility of an investment alternative decreases cetris paribus if the amount of debt capital increases