Forecasting Temperature Indices Density with Time-Varying Long-Memory Models
Article first published online: 30 DEC 2011
Copyright © 2011 John Wiley & Sons, Ltd.
Journal of Forecasting
Volume 32, Issue 4, pages 339–352, July 2013
How to Cite
Caporin, M. and Preś, J. (2013), Forecasting Temperature Indices Density with Time-Varying Long-Memory Models. J. Forecast., 32: 339–352. doi: 10.1002/for.1272
- Issue published online: 19 JUN 2013
- Article first published online: 30 DEC 2011
- weather forecasting;
- weather derivatives;
- long-memory time series;
- time-varying long memory;
- derivative pricing
The hedging of weather risks has become extremely relevant in recent years, promoting the diffusion of weather-derivative contracts. The pricing of such contracts requires the development of appropriate models for the prediction of the underlying weather variables. Within this framework, a commonly used specification is the ARFIMA-GARCH. We provide a generalization of such a model, introducing time-varying memory coefficients. Our model satisfies the empirical evidence of the changing memory level observed in average temperature series, and provides useful improvements in the forecasting, simulation, and pricing issues related to weather derivatives. We present an application related to the forecast and simulation of a temperature index density, which is then used for the pricing of weather options. Copyright © 2011 John Wiley & Sons, Ltd.