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

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

Das Risikoreduzierungspotenzial von Wetterderivaten im Ackerbau: Einfachindizes versus Mischindizes

Der Einsatz von Wetterderivaten ist mit einem Basisrisiko behaftet. Dies schmälert das Risikoreduzierungspotenzial und stellt möglicherweise ein Hemmnis bei der Verbreitung dieser Risikomanagementinstrumente in der Landwirtschaft dar. Als ein Ansatz zur Verringerung des Basisrisikos werden Mischindizes vorgeschlagen, die sich aus mehreren Wettervariablen zusammensetzen. Dieser Beitrag vergleicht die risikoreduzierende Wirkung eines temperaturindexbasierten und eines niederschlagsindexbasierten Wetterderivates mit der eines Derivates, das auf einem, aus beiden Wettervariablen gebildeten, Mischindex basiert. Die Grundlage für diesen Vergleich bilden empirische Ertragszeitreihen aus der Winterweizenproduktion von 32 Betrieben in Mitteldeutschland sowie Tagestemperatur- und Tagesniederschlagsdaten ausgewählter Wetterstationen über mehrere Jahre. Die Hedgingeffektivität wird mit Hilfe von Johnsons (1960) Hedging Modell maximiert. Die Ergebnisse belegen empirisch, dass die Verbesserung des Risikoreduzierungspotenzials durch mischindexbasierte Wetterderivate gegenüber Wetterderivaten mit Einfachindizes signifikant ist. Allerdings ist die risikoreduzierende Wirkung eines Wetterderivates basierend auf einem Mischindex nicht signifikant höher als die gleichzeitige Verwendung mehrerer Wetterderivate mit unterschiedlichen einfachen Indizes. Außerdem zeigt der Beitrag, dass betriebsindividuell optimal ausgestaltete Wetterindizes gerade bei Mischindizes ein signifikant höheres Risikoreduzierungspotenzial haben als standardisierte Wetterindizes. Die hier behandelte Fragestellung kann sowohl für Landwirte als auch für potenzielle Anbieter von Wetterderivaten relevant sein. Weather derivatives are impaired with a basis risk that reduces the risk reduction potential and possibly hinders the introduction of these risk management instruments in the agricultural sector. A frequently suggested approach to reduce the basis risk is the use of mixed indices composed of several weather variables. The present study compares the risk-reduction potential of a temperature index-based and a precipitation index-based weather derivative to a derivative based on a mixed index including the two weather variables temperature and precipitation. This comparison is based on empirical winter wheat yield data of arable farms in Central Germany as well as on daily weather data of individual weather stations over several years. The hedging effectiveness is maximized using the hedging model by Johnson (1960). The results empirically prove that the improvement of the risk reduction potential of weather derivatives based on a mixed index improves significantly in comparison to single-index derivatives. However, it is more advantageous to use several weather derivatives based on a simple index at the same time than using one derivative based on a mixed index if the weather variables of the mixed index were measured at just one weather station. Hence, providers of weather derivatives would do better by offering different weather derivatives based on a simple index than derivatives that are based on a mixed index. In particular this is worth considering with regard to the fact that weather derivatives based on simple indices will surely attract the interest of other sectors more easily. Furthermore, by showing that farm-individual optimally designed weather indices have a significantly higher risk reduction potential than standardized weather indices, this study provides an important progress for the question about the design of weather derivatives. Hence, providers of weather derivative should better offer different weather derivatives with single index-based, farm-individual, optimally designed indices than a derivative based on a mixed index. The focus of the present study may be relevant for farmers as well as for potential providers of weather derivatives

Health and environmental implications of food consumption: What do people want to know?

Contradicting rational choice, people sometimes do not want information even if it is free (information avoidance). People may want to enjoy the pleasure of unhealthy food wrapped in plastic packaging today and do not want to be informed about the long-term consequences for their health and the environment. Using a quota-representative survey of the German population (N=1,000), we aim to identify behavioral determinants that are associated with information avoidance in the food sector. Participants are asked to what extent they do (not) want to receive information in the realm of the food sector using 10 different hypothetical situations. Information avoidance is measured on a 5-point Likert scale (1=“Definitely want to know” to 5=“Definitely don’t want to know”). This procedure ensures that relatively poor people do not give systematically biased answers due to a lack of financial resources. Our findings can be summarized as follows: First, preferences/personality traits can partly predict information avoidance. For example, positive reciprocity, altruism, internal locus of control, and self-esteem are negatively associated with information avoidance. The relevance of trust and patience is limited to environmental-related consequences. Second, individuals are less willing to forgo information the better they rate their health status. Dietary habits can better predict information avoidance than diseases such as food allergy, cardiovascular, cholesterol, or diabetes. Third, women and men are more likely not to avoid information if they find it useful. Risk-seeking, altruism, and positive reciprocity are, roughly speaking, stronger negatively pronounced with men—trust is stronger negatively pronounced with women

Das Risikoreduzierungspotenzial von Wetterindexversicherungen im Agribusiness – Die Bedeutung des Aggregationsniveaus von Ertragszeitreihen

Wetterindexversicherungen werden in der Landwirt-schaft bisher nur verhalten eingesetzt. Als ein wesentlicher Grund hierfür werden Basisrisiken angesehen, die bei der Anwendung beim Landwirt verbleiben. Wetterindexversicherungen können allerdings auch für Unternehmen des Agribusiness interessant sein, die Erträge mehrerer landwirtschaftlicher Betriebe in sich aggregieren. Dieser Beitrag untersucht am Beispiel eines Zucker verarbeitenden Unternehmens die Bedeutung des Aggregationsniveaus von Ertrags-zeitreihen für das Risikoreduzierungspotenzial von Wetterindexversicherungen. Die Grundlage hierfür bilden die einzelbetrieblichen Zuckerertragszeitreihen von 40 Zuckerrüben produzierenden Betrieben sowie die aggregierte Zuckerertragszeitreihe aller rund 5 000 Zuckerrüben produzierenden Betriebe in Nord-deutschland, die ihre Zuckerrüben an das betreffende Zucker verarbeitende Unternehmen liefern. Diese hoch aggregierte Zuckerertragszeitreihe beschreibt gleichzeitig das Mengenrisiko des Zucker verarbeitenden Unternehmens. Unsere Ergebnisse belegen empirisch, dass eine Hedgingeffektivität von Wetterindexversicherungen auf unterschiedlichen Aggregationsniveaus vorhanden ist und das infolge des Aggregierens der Zuckerertragszeitreihen das Basisrisiko bei der Anwendung von Wetterindexversicherungen geschmälert und das Risikoreduzierungspotenzial gesteigert wird. Wenn das Risikoreduzierungspotenzial von Wetterindexversicherungen aus Wirksamkeitsanalysen abgeleitet wird, die mit Ertragszeitreihen einzelner landwirtschaftlicher Betriebe arbeiten, kann es für Unternehmen des Agribusiness daher unterschätzt werden. Die hier behandelte Fragestellung ist sowohl für Unternehmen des Agribusiness als auch für potenzielle Anbieter von Wetterindexversicherungen relevant. Weather index-based insurance is not sufficiently used in agriculture as of yet. Basis risks are considered to be a major reason for this. By the use of weather index-based insurance, basis risks remain with the farmer. However, weather index-based insurance can be interesting for agribusiness companies, specifically those which aggregate yields of several farms amongst themselves. This paper investigates the importance of the aggregation level of yield time series for the hedging effectiveness of weather index-based insurance following the example of a sugar processing company. This investigation is based on empirical sugar beet yield data from 40 sugar beet producing farms in Northern Germany. Furthermore, we work with the aggregated sugar yield time series of roughly 5,000 farms, which account for sugar beets used in the sugar processing company in question. At the same time, this highly aggregated yield time series describes the quantity risk of the sugar processing company. Our results empirically show that in consequence of aggregating the sugar yield time series, basis risk is diminished and risk reduction potential is raised through the use of weather index-based insurance. The risk reduction potential of weather index-based insurance can, therefore, be underestimated if it is derived from studies pertaining to yield time series at the individual farm level. The focus of the present study may be relevant for agribusiness companies, as well as for potential providers of weather derivatives

Improved program planning with formal models? The case of high risk crop farming in Northeast Germany

Production program planning, Optimization, Risk, Time series analysis, C1, C61, M11, Q12,