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Forecasting temperature indices with timevarying long-memory models

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Author Info

  • Massimiliano Caporin

    ()
    (Università di Padova)

  • Juliusz Pres

    (Szczecin University of Technology)

Abstract

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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File URL: http://economia.unipd.it/sites/decon.unipd.it/files/20090088.pdf
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Bibliographic Info

Paper provided by Dipartimento di Scienze Economiche "Marco Fanno" in its series "Marco Fanno" Working Papers with number 0088.

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Length: 59 pages
Date of creation: 2008
Date of revision:
Handle: RePEc:pad:wpaper:0088

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Related research

Keywords: weather forecasting; weather derivatives; long memory time series; time-varying long memory; derivative pricing;

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