Density forecasting for weather derivative pricing

Density Forecasting for Weather Derivative Pricing: A Comparison of GARCH and Atmospheric Models

Weather derivatives enable energy companies to protect themselves against weather risk. Weather ensemble predictions are generated from atmospheric models and consist of multiple future scenarios for a weather variable. They can be used to forecast the density of the payoff from a weather derivative. The mean of the density is the fair price of the derivative, and the distribution about the mea...

Memory time varying models for weather derivative pricing

We present a generalisation of the double long memory ARFIMA-FIGARCH model introducing time-varying memory coefficients both in the mean and in the variance. The model satisfies the empirical evidence of changing memory observed in average temperature series and can provide useful improvements in the forecasting, simulation and pricing issues related to weather derivatives. We provide an applic...

Stochastic Models for Pricing Weather Derivatives using Constant Risk Premium

‎Pricing weather derivatives is becoming increasingly useful‎, ‎especially in developing economies‎. ‎We describe a statistical model based approach for pricing  weather derivatives by modeling and forecasting daily average temperatures data which exhibits long-range dependence‎. ‎We pre-process the temperature data by filtering for seasonality and volatility an...

Long-range Weather Forecasting

Forecasting Space Weather

Forecasting space weather events presents the ultimate challenge to a space physics model. Not only should physical constraints be satisfied, but also practical issues such as timeliness, accuracy, and reliability must be adequately addressed. To address these needs, modern space weather forecasters and users rely on a great variety of space weather models, ranging from simple regressions and e...