In this paper we investigate price and volatility risk originating in linkages between energy and agricultural commodity prices in Germany and study their dynamics over time. We propose an econometric approach to quantify the volatility and correlation risk structure, which has a large impact for investment and hedging strategies of market participants as well as for policy makers. Volatilities and their short and long run linkages (spillovers) are analyzed using a dynamic conditional correlation GARCH model as well as a multivariate multiplicative volatility model. Our approach provides a exible and accurate tting procedure for volatility and correlation risk.

Weather influences our daily lives and choices and has an enormous impact on corporate revenues and earnings. Weather derivatives differ from most derivatives in that the underlying weather cannot be traded and their market is relatively illiquid. The weather derivative market is therefore incomplete. This paper implements a pricing methodology for weather derivatives that can increase the precision of measuring weather risk. We have applied continous autoregressive models (CAR) with seasonal variation to model the temperature in Berlin and with that to get the explicite nature of non-arbitrage prices for temperature derivatives. We infer the implied market price from Berlin cumulative monthly temperature futures that are traded at the Chicago Mercantile Exchange (CME), which is an important parameter of the associated equivalent martingale measures used to price and hedge weather future/options in the market. We propose to study the market price of risk, not only as a piecewise constant linear function, but also as a time dependent object. In all of the previous cases, we found that the market price of weather risk is different from zero and shows a seasonal structure. With the extract information we price other exotic options, such as cooling/heating degree day temperatures and non-standard maturity contracts.

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. To meet the increasing global demand for renewable energy such as wind energy, more and more new wind parks are installed worldwide. Finding a suitable location, however, requires a detailed and often costly analysis of the local wind conditions. Plain average wind speed maps cannot provide a precise forecast of wind power because of the non-linear relationship between wind speed and production. In this paper, we suggest a new approach of assessing the local wind energy potential: Meteorological reanalysis data are applied to obtain long-term low-scale wind speed data at turbine location and hub height; then, with actual high-frequency production data, the relation between wind data and energy production is determined via a five parameter logistic function. The resulting wind energy index allows for a turbine-specific estimation of the expected wind power at an unobserved location. A map of wind power potential for whole Germany exemplifies the approach. Terms of use: Documents in