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Estimating the Payoffs of Temperature-based Weather Derivatives

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Author Info
Adam Clements () (QUT)
A S Hurn () (QUT)
K A Lindsay () (Glasgow)

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Abstract

Temperature-based weather derivatives are written on an index which is normally defined to be a nonlinear function of average daily temperatures. Recent empirical work has demonstrated the usefulness of simple time-series models of temperature for estimating the payoffs to these instruments. This paper argues that a more direct and parsimonious approach is to model the time-series behaviour of the index itself, provided a sufficiently rich supply of historical data is available. A data set comprising average daily temperature spanning over a hundred years for four Australian cities is assembled. The data is then used to compare the actual payoffs of temperature-based European call options with the expected payoffs computed from historical temperature records and two time-series approaches. It is concluded that expected payoffs computed directly from historical records perform poorly by comparison with the expected payoffs generated by means of competing time-series models. It is also found that modeling the relevant temperature index directly is superior to modeling average daily temperatures.

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Paper provided by National Centre for Econometric Research in its series NCER Working Paper Series with number 33.

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Length: 18
Date of creation: 26 Aug 2008
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Handle: RePEc:qut:auncer:2008-22

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Related research
Keywords: Temperature; Weather Derivatives; Cooling Degree Days; Time-series Models.;

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Find related papers by JEL classification:
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Fred Espen Benth & Jūratė Šaltytė-Benth, 2005. "Stochastic Modelling of Temperature Variations with a View Towards Weather Derivatives," Applied Mathematical Finance, Taylor and Francis Journals, vol. 12(1), pages 53-85, March. [Downloadable!] (restricted)
  2. Sean D. Campbell & Francis X. Diebold, 2004. "Weather Forecasting for Weather Derivatives," CFS Working Paper Series 2004/10, Center for Financial Studies. [Downloadable!]
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  3. Robert F. Engle & Gary G.J. Lee, 1993. "A Permanent and Transitory Component Model of Stock Return Volatility," University of California at San Diego, Economics Working Paper Series 92-44r, Department of Economics, UC San Diego. [Downloadable!]
  4. Eckhard Platen & Jason West, 2003. "Fair Pricing of Weather Derivatives," Research Paper Series 106, Quantitative Finance Research Centre, University of Technology, Sydney. [Downloadable!]
  5. Peter Alaton & Boualem Djehiche & David Stillberger, 2002. "On modelling and pricing weather derivatives," Applied Mathematical Finance, Taylor and Francis Journals, vol. 9(1), pages 1-20, March. [Downloadable!] (restricted)
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Cited by:
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  1. Adam Clements & A S Hurn & K A Lindsay, 2008. "Developing analytical distributions for temperature indices for the purposes of pricing temperature-based weather derivatives," NCER Working Paper Series 34, National Centre for Econometric Research. [Downloadable!]
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