IDEAS home Printed from https://ideas.repec.org/p/ags/pugtwp/332293.html
   My bibliography  Save this paper

Model Comparison for Temperature-based Weather Derivatives in Mainland China

Author

Listed:
  • Zong, Lu
  • Ender, Manuela

Abstract

In this paper, we provide a comprehensive comparison of two models for the simulation and pricing of temperature-based weather derivatives. The model of Alaton et al (2002) and the CAR model of Benth et al (2007) are applied to temperature data from twelve cities in Mainland China. The objective of this paper is to analyse whether the CAR model, as a more advanced model has a better performance in fitting the daily average temperature (DAT). A higher accuracy of the CAR model can be found indeed in terms of normality of residuals and in terms of smaller relative errors. However, the shortcomings of both models are revealed in this study as well. The Chinese cities involved cover all seven climatic zones in the standard of climatic regionalization that is used as a partition of China to get representative clusters.

Suggested Citation

  • Zong, Lu & Ender, Manuela, 2013. "Model Comparison for Temperature-based Weather Derivatives in Mainland China," Conference papers 332293, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    • Handle: RePEc:ags:pugtwp:332293
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/332293/files/6233.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fred Espen Benth & Jurate Saltyte-Benth, 2005. "Stochastic Modelling of Temperature Variations with a View Towards Weather Derivatives," Applied Mathematical Finance, Taylor & Francis Journals, vol. 12(1), pages 53-85.
    2. Fred Espen Benth & Jūratė Šaltytė Benth & Steen Koekebakker, 2008. "Stochastic Modeling of Electricity and Related Markets," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 6811, December.
    3. 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.
    4. Fred Espen Benth & Jūratė Šaltytė Benth, 2011. "Weather Derivatives and Stochastic Modelling of Temperature," International Journal of Stochastic Analysis, Hindawi, vol. 2011, pages 1-21, July.
    5. Dorje Brody & Joanna Syroka & Mihail Zervos, 2002. "Dynamical pricing of weather derivatives," Quantitative Finance, Taylor & Francis Journals, vol. 2(3), pages 189-198.
    6. Fred ESPEN Benth & Jurate saltyte Benth, 2007. "The volatility of temperature and pricing of weather derivatives," Quantitative Finance, Taylor & Francis Journals, vol. 7(5), pages 553-561.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    2. Samuel Asante Gyamerah & Philip Ngare & Dennis Ikpe, 2018. "Regime-Switching Temperature Dynamics Model for Weather Derivatives," International Journal of Stochastic Analysis, Hindawi, vol. 2018, pages 1-15, July.
    3. Dorfleitner, Gregor & Wimmer, Maximilian, 2010. "The pricing of temperature futures at the Chicago Mercantile Exchange," Journal of Banking & Finance, Elsevier, vol. 34(6), pages 1360-1370, June.
    4. Romain Biard & Christophette Blanchet-Scalliet & Anne Eyraud-Loisel & Stéphane Loisel, 2013. "Impact of Climate Change on Heat Wave Risk," Risks, MDPI, vol. 1(3), pages 1-16, December.
    5. Alexandridis, Antonis K. & Kampouridis, Michael & Cramer, Sam, 2017. "A comparison of wavelet networks and genetic programming in the context of temperature derivatives," International Journal of Forecasting, Elsevier, vol. 33(1), pages 21-47.
    6. Lunina, Veronika, 2016. "Joint Modelling of Power Price, Temperature, and Hydrological Balance with a View towards Scenario Analysis," Working Papers 2016:30, Lund University, Department of Economics.
    7. Frank Schiller & Gerold Seidler & Maximilian Wimmer, 2012. "Temperature models for pricing weather derivatives," Quantitative Finance, Taylor & Francis Journals, vol. 12(3), pages 489-500, March.
    8. Zura Kakushadze & Juan Andrés Serur, 2018. "151 Trading Strategies," Springer Books, Springer, number 978-3-030-02792-6, June.
    9. Šaltytė Benth, Jūratė & Benth, Fred Espen, 2012. "A critical view on temperature modelling for application in weather derivatives markets," Energy Economics, Elsevier, vol. 34(2), pages 592-602.
    10. Asante Gyamerah, Samuel & Ngare, Philip & Ikpe, Dennis, 2018. "A Levy Regime-Switching Temperature Dynamics Model for Weather Derivatives," MPRA Paper 89680, University Library of Munich, Germany, revised 10 Jul 2018.
    11. Fred Espen Benth & Jūratė Šaltytė Benth, 2012. "Modeling and Pricing in Financial Markets for Weather Derivatives," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 8457, December.
    12. Elias, R.S. & Wahab, M.I.M. & Fang, L., 2014. "A comparison of regime-switching temperature modeling approaches for applications in weather derivatives," European Journal of Operational Research, Elsevier, vol. 232(3), pages 549-560.
    13. Gülpınar, Nalân & Çanakoḡlu, Ethem, 2017. "Robust portfolio selection problem under temperature uncertainty," European Journal of Operational Research, Elsevier, vol. 256(2), pages 500-523.
    14. Ahmet Göncü, 2013. "Comparison of temperature models using heating and cooling degree days futures," Journal of Risk Finance, Emerald Group Publishing, vol. 14(2), pages 159-178, February.
    15. Rui Zhou & Johnny Siu-Hang Li & Jeffrey Pai, 2019. "Pricing temperature derivatives with a filtered historical simulation approach," The European Journal of Finance, Taylor & Francis Journals, vol. 25(15), pages 1462-1484, October.
    16. Jr‐Wei Huang & Sharon S. Yang & Chuang‐Chang Chang, 2018. "Modeling temperature behaviors: Application to weather derivative valuation," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(9), pages 1152-1175, September.
    17. Larsson, Karl, 2023. "Parametric heat wave insurance," Journal of Commodity Markets, Elsevier, vol. 31(C).
    18. Dupuis, Debbie J., 2011. "Forecasting temperature to price CME temperature derivatives," International Journal of Forecasting, Elsevier, vol. 27(2), pages 602-618.
    19. 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.
    20. Evarest Emmanuel & Berntsson Fredrik & Singull Martin & Yang Xiangfeng, 2018. "Weather derivatives pricing using regime switching model," Monte Carlo Methods and Applications, De Gruyter, vol. 24(1), pages 13-27, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:pugtwp:332293. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/gtpurus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.