Weather influences our daily lives and choices and has an enormous impact on cooperate 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 applied continous autoregressive models (CAR) with seasonal variation to model the temperature in Berlin and with that to get 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. In any 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 contract with crazy maturities.
Weather derivatives weather risk weather forecasting seasonality continuous autoregressive model stochastic variance CAT index CDD index HDD index market price of risk risk premium CME