Forecasting temperature indices with timevarying long-memory models
AbstractThe 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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Bibliographic InfoPaper provided by Dipartimento di Scienze Economiche "Marco Fanno" in its series "Marco Fanno" Working Papers with number 0088.
Length: 59 pages
Date of creation: 2008
Date of revision:
weather forecasting; weather derivatives; long memory time series; time-varying long memory; derivative pricing;
Find related papers by JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
- G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
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