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 for both mean and 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 application related to the forecast and simulation of temperature indices used for pricing of weather options