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Density forecasting for weather derivative pricing

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  • Taylor, James W.
  • Buizza, Roberto

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  • Taylor, James W. & Buizza, Roberto, 2006. "Density forecasting for weather derivative pricing," International Journal of Forecasting, Elsevier, vol. 22(1), pages 29-42.
    • Handle: RePEc:eee:intfor:v:22:y:2006:i:1:p:29-42
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    References listed on IDEAS

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    1. Granger, C. W. J. & White, Halbert & Kamstra, Mark, 1989. "Interval forecasting : An analysis based upon ARCH-quantile estimators," Journal of Econometrics, Elsevier, vol. 40(1), pages 87-96, January.
    2. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    3. Sean D. Campbell & Francis X. Diebold, 2005. "Weather Forecasting for Weather Derivatives," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 6-16, March.
    4. Taylor, James W. & Buizza, Roberto, 2003. "Using weather ensemble predictions in electricity demand forecasting," International Journal of Forecasting, Elsevier, vol. 19(1), pages 57-70.
    5. Peter Alaton & Boualem Djehiche & David Stillberger, 2002. "On modelling and pricing weather derivatives," Applied Mathematical Finance, Taylor & Francis Journals, vol. 9(1), pages 1-20.
    6. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    7. Dorje Brody & Joanna Syroka & Mihail Zervos, 2002. "Dynamical pricing of weather derivatives," Quantitative Finance, Taylor & Francis Journals, vol. 2(3), pages 189-198.
    8. M. Davis, 2001. "Pricing weather derivatives by marginal value," Quantitative Finance, Taylor & Francis Journals, vol. 1(3), pages 305-308, March.
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    Cited by:

    1. Meng, Xiaochun & Taylor, James W., 2022. "Comparing probabilistic forecasts of the daily minimum and maximum temperature," International Journal of Forecasting, Elsevier, vol. 38(1), pages 267-281.
    2. José Hernández & Fernando Carvajal-Serna, 2017. "Risk coverage in the face of hydrological variability in a run-off hydraulic power plant using weather derivatives," Lecturas de Economía, Universidad de Antioquia, Departamento de Economía, issue 87, pages 191-222, Julio - D.
    3. Cees Diks & Valentyn Panchenko & Dick van Dijk, 2008. "Partial Likelihood-Based Scoring Rules for Evaluating Density Forecasts in Tails," Tinbergen Institute Discussion Papers 08-050/4, Tinbergen Institute.
    4. Rosella Castellano & Roy Cerqueti & Giulia Rotundo, 2020. "Exploring the financial risk of a temperature index: a fractional integrated approach," Annals of Operations Research, Springer, vol. 284(1), pages 225-242, January.
    5. Dupuis, Debbie J., 2011. "Forecasting temperature to price CME temperature derivatives," International Journal of Forecasting, Elsevier, vol. 27(2), pages 602-618, April.
    6. Hernández Arango, José Miguel & Carvajal-Serna, Luis Fernando, 2017. "Cobertura al riesgo ante la variabilidad hidrológica en una central hidráulica a filo de agua usando derivados climáticos," Revista Lecturas de Economía, Universidad de Antioquia, CIE, issue 87, pages 191-222, March.
    7. Oleg Kolesnikov & Alexander Markov & Daulet Smagulov & Sergejs Solovjovs, 2019. "Cyber bonds and their pricing models," Papers 1911.06698, arXiv.org.
    8. Cui, Hairong & Zhou, Ying & Dzandu, Michael D. & Tang, Yinshan & Lu, Xunfa, 2019. "Is temperature-index derivative suitable for China?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    9. Diks, Cees & Panchenko, Valentyn & van Dijk, Dick, 2011. "Likelihood-based scoring rules for comparing density forecasts in tails," Journal of Econometrics, Elsevier, vol. 163(2), pages 215-230, August.
    10. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
    11. Ahčan, Aleš, 2012. "Statistical analysis of model risk concerning temperature residuals and its impact on pricing weather derivatives," Insurance: Mathematics and Economics, Elsevier, vol. 50(1), pages 131-138.
    12. Kosater, Peter, 2006. "Cross-city hedging with weather derivatives using bivariate DCC GARCH models," Discussion Papers in Econometrics and Statistics 2/06, University of Cologne, Institute of Econometrics and Statistics.
    13. Eirini Konstantinidi & Gkaren Papazian & George Skiadopoulos, 2015. "Modeling the Dynamics of Temperature with a View to Weather Derivatives," World Scientific Book Chapters, in: Anastasios G Malliaris & William T Ziemba (ed.), THE WORLD SCIENTIFIC HANDBOOK OF FUTURES MARKETS, chapter 17, pages 511-544, World Scientific Publishing Co. Pte. Ltd..
    14. repec:hal:journl:peer-00834423 is not listed on IDEAS
    15. Pierre-Julien Trombe & Pierre Pinson & Henrik Madsen, 2012. "A General Probabilistic Forecasting Framework for Offshore Wind Power Fluctuations," Energies, MDPI, vol. 5(3), pages 1-37, March.
    16. Dupuis, Debbie J., 2011. "Forecasting temperature to price CME temperature derivatives," International Journal of Forecasting, Elsevier, vol. 27(2), pages 602-618.
    17. Zhu, Hui-Ming & Li, ZhaoLai & You, WanHai & Zeng, Zhaofa, 2015. "Revisiting the asymmetric dynamic dependence of stock returns: Evidence from a quantile autoregression model," International Review of Financial Analysis, Elsevier, vol. 40(C), pages 142-153.
    18. Fuhrer, Jurg & Beniston, Martin & Calanca, Pierluigi & Torriani, Daniele Simone, 2007. "Alternative Hedging Strategies in Maize Production to Cope with Climate Variability and Change," 101st Seminar, July 5-6, 2007, Berlin Germany 9275, European Association of Agricultural Economists.

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