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Pricing the Weather Derivatives in the Presence of Long Memory in Temperatures

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  • Hélène Hamisultane

    ()
    (EconomiX - CNRS : UMR7166 - Université de Paris X - Nanterre)

Abstract

Weather derivatives are financial contracts for which the underlying is not a traded asset. Therefore, they cannot be priced by the traditional financial theory based on the hedging portfolio and on the arbitrage-free argument. Some authors suggest to use the actuarial pricing approach to value the weather derivatives. But this method suffers from the fact that it is only based on the modelling of the temperature. The market information is not necessary to value the weather derivatives by this approach. On the contrary, the financial method needs to infer the market price of weather risk since the market is incomplete for the weather derivatives. We suggest in this paper to compute and to compare the prices stemming from the both approaches by using the New York weather futures quotations. Prices are calculated on the basis that the daily average temperature has a long memory since tests reveal its presence in the serie.

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Bibliographic Info

Paper provided by HAL in its series Working Papers with number halshs-00079197.

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Date of creation: 2006
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Handle: RePEc:hal:wpaper:halshs-00079197

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Related research

Keywords: weather derivatives; incomplete market; long memory; ARFIMA process; FIGARCH process; LMSV process; fractional Brownian motion; PDE; Monte-Carlo simulations;

References

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  1. Bollerslev, Tim & Ole Mikkelsen, Hans, 1996. "Modeling and pricing long memory in stock market volatility," Journal of Econometrics, Elsevier, Elsevier, vol. 73(1), pages 151-184, July.
  2. Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996. "Fractionally integrated generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, Elsevier, vol. 74(1), pages 3-30, September.
  3. Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, University of Chicago Press, vol. 81(3), pages 637-54, May-June.
  4. Sowell, Fallaw, 1992. "Maximum likelihood estimation of stationary univariate fractionally integrated time series models," Journal of Econometrics, Elsevier, Elsevier, vol. 53(1-3), pages 165-188.
  5. Breidt, F. Jay & Crato, Nuno & de Lima, Pedro, 1998. "The detection and estimation of long memory in stochastic volatility," Journal of Econometrics, Elsevier, Elsevier, vol. 83(1-2), pages 325-348.
  6. Eckhard Platen, 2001. "Arbitrage in Continuous Complete Markets," Research Paper Series, Quantitative Finance Research Centre, University of Technology, Sydney 72, Quantitative Finance Research Centre, University of Technology, Sydney.
  7. Fred Espen Benth, 2003. "On arbitrage-free pricing of weather derivatives based on fractional Brownian motion," Applied Mathematical Finance, Taylor & Francis Journals, Taylor & Francis Journals, vol. 10(4), pages 303-324.
  8. Deo, Rohit S. & Hurvich, Clifford M., 2001. "On The Log Periodogram Regression Estimator Of The Memory Parameter In Long Memory Stochastic Volatility Models," Econometric Theory, Cambridge University Press, Cambridge University Press, vol. 17(04), pages 686-710, August.
  9. Black, Fischer, 1976. "The pricing of commodity contracts," Journal of Financial Economics, Elsevier, Elsevier, vol. 3(1-2), pages 167-179.
  10. Eckhard Platen & Jason West, 2003. "Fair Pricing of Weather Derivatives," Research Paper Series, Quantitative Finance Research Centre, University of Technology, Sydney 106, Quantitative Finance Research Centre, University of Technology, Sydney.
  11. Hélène Hamisultane, 2007. "Extracting Information from the Market to Price the Weather Derivatives," Working Papers halshs-00079192, HAL.
  12. Jackwerth, Jens Carsten & Rubinstein, Mark, 1996. " Recovering Probability Distributions from Option Prices," Journal of Finance, American Finance Association, American Finance Association, vol. 51(5), pages 1611-32, December.
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Cited by:
  1. Best, Peter & Stone, Roger & Sosenko, Olena, 2007. "Climate risk management based on climate modes and indices - the potential in Australian agribusinesses," 101st Seminar, July 5-6, 2007, Berlin Germany, European Association of Agricultural Economists 9257, European Association of Agricultural Economists.

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