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Modelling and forecasting wind speed intensity for weather risk management

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  • Massimiliano Caporin

    ()
    (Università di Padova)

  • Juliusz Pres

    (Szczecin University of Technology)

Abstract

The modelling of wind speed is a traditional topic in meteorological research, where the main interest is on the short-term forecast of wind speed intensity and direction. More recently, this theme has received some interest in the quantitative finance literature for its relationship with electricity production by wind farms. In fact, electricity producers are interested in long-range forecasts and simulation of wind speed for two main reasons: to evaluate the profitability of building a wind farm in a given location and to offset the risks associated with the variability of wind speed for an already operating wind farm. In this paper, we contribute to the increasing literature regarding environmental finance by comparing three approaches that are capable of forecasting and simulating the long run evolution of wind speed intensity (direction is not a concern, given that the recent turbines can rotate to follow wind direction): the Auto Regressive Gamma process, the Gamma Auto Regressive process, and the ARFIMA-FIGARCH model. We provide both in-sample and out-of-sample comparisons of the models, as well as some examples for the pricing of wind speed derivatives using a model-based Monte Carlo simulation approach.

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Bibliographic Info

Paper provided by Dipartimento di Scienze Economiche "Marco Fanno" in its series "Marco Fanno" Working Papers with number 0106.

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Length: 38 pages
Date of creation: Jan 2010
Date of revision:
Handle: RePEc:pad:wpaper:0106

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Keywords: Gamma Auto Regressive; Auto Regressive Gamma; ARFIMA-FIGARCH; wind speed modelling; wind speed simulation;

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Cited by:
  1. Huurman, Christian & Ravazzolo, Francesco & Zhou, Chen, 2012. "The power of weather," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 56(11), pages 3793-3807.
  2. Andrea Monticini & Francesco Ravazzolo, 2011. "Forecasting the intraday market price of money," Working Paper, Norges Bank 2011/06, Norges Bank.
  3. Caporin, Massimiliano & Fontini, Fulvio, 2014. "The Value of Protecting Venice from the Acqua Alta Phenomenon under Different Local Sea Level Rises," MPRA Paper 53779, University Library of Munich, Germany.
  4. Monica Billio & Roberto Casarin & Francesco Ravazzolo & Herman K. van Dijk, 2011. "Combining Predictive Densities using Bayesian Filtering with Applications to US Economics Data," Tinbergen Institute Discussion Papers 11-003/4, Tinbergen Institute.
  5. Monica Billio & Roberto Casarin & Francesco Ravazzolo & Herman K. van Dijk, 2011. "Combining Predictive Densities using Nonlinear Filtering with Applications to US Economics Data," Tinbergen Institute Discussion Papers 11-172/4, Tinbergen Institute.
  6. Caporin, Massimiliano & Ranaldo, Angelo & Velo, Gabriel G., 2014. "Precious Metals Under the Microscope: A High-Frequency Analysis," Working Papers on Finance, University of St. Gallen, School of Finance 1409, University of St. Gallen, School of Finance.
  7. Caporin, Massimiliano & Ranaldo, Angelo & Velo, Gabriel G., 2013. "Stylized Facts and Dynamic Modeling of High-frequency Data on Precious Metals," Working Papers on Finance, University of St. Gallen, School of Finance 1318, University of St. Gallen, School of Finance.

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