Forecasting temperature indices with timevarying long-memory models
The hedging of weather risks has become extremely relevant in recent years, promoting the diffusion of weather derivative contracts. The pricing of such contracts require the development of appropriate models for the prediction of the underlying weather variables. Within this framework, we present a modification 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 provide useful improvements in the forecasting, simulation and pricing issues related to weather derivatives. We present an application related to the forecast and simulation of temperature indices used for pricing of weather options.
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