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Is temperature-index derivative suitable for China?

Author

Listed:
  • Cui, Hairong
  • Zhou, Ying
  • Dzandu, Michael D.
  • Tang, Yinshan
  • Lu, Xunfa

Abstract

In this paper, we assessed the suitability of temperature derivatives for China through modeling. We assumed that if the physical dynamics of temperature of some cities are identical, then the same types of temperature derivatives can be used in these cities. Nearly twenty years temperature data of forty-seven cities with traded temperature derivatives on the Chicago Mercantile Exchange Group (CME) and seven Chinese cities were collected and analyzed in a two-step approach. Firstly, the AR-EGARCH model capturing the shock asymmetry of the volatility of temperature is used to simulate the dynamics of temperature of the cities. Secondly, the temperature of the cities are classified through cluster analysis based on model parameters from the AR-EGARCH model. The results showed that the fitting effect of the AR-EGARCH model is very good, and only a few cities did not display the shock asymmetry. The model for Nanjing fitted well into one of the categories of the cities in the CME; but the other six Chinese cities belong to new categories, which are different from the cities in the CME. We concluded that HDD and CAT in Europe and CAT∗ in Japan can be used directly in Nanjing, but the existing temperature derivatives in CME were unsuitable for the other six Chinese cities. Recommendations for the establishment of weather derivatives market in China have been proposed.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:phsmap:v:536:y:2019:i:c:s0378437119305576
    DOI: 10.1016/j.physa.2019.04.195
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Joshua D. Woodard & Philip Garcia, 2008. "Basis risk and weather hedging effectiveness," Agricultural Finance Review, Emerald Group Publishing Limited, vol. 68(1), pages 99-117, May.
    4. Groll, Andreas & López-Cabrera, Brenda & Meyer-Brandis, Thilo, 2016. "A consistent two-factor model for pricing temperature derivatives," Energy Economics, Elsevier, vol. 55(C), pages 112-126.
    5. Caporin, Massimiliano & Preś, Juliusz & Torro, Hipolit, 2012. "Model based Monte Carlo pricing of energy and temperature Quanto options," Energy Economics, Elsevier, vol. 34(5), pages 1700-1712.
    6. 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, August.
    7. 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.
    8. Buchholz, Matthias & Musshoff, Oliver, 2014. "The role of weather derivatives and portfolio effects in agricultural water management," Agricultural Water Management, Elsevier, vol. 146(C), pages 34-44.
    9. M. Davis, 2001. "Pricing weather derivatives by marginal value," Quantitative Finance, Taylor & Francis Journals, vol. 1(3), pages 305-308, March.
    10. 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.
    11. 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.
    12. Fred Espen Benth & Jūratė Šaltytė Benth & Steen Koekebakker, 2007. "Putting a Price on Temperature," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(4), pages 746-767, December.
    13. A. Zapranis & A. Alexandridis, 2008. "Modelling the Temperature Time-dependent Speed of Mean Reversion in the Context of Weather Derivatives Pricing," Applied Mathematical Finance, Taylor & Francis Journals, vol. 15(4), pages 355-386.
    14. Jewson,Stephen & Brix,Anders, 2005. "Weather Derivative Valuation," Cambridge Books, Cambridge University Press, number 9780521843713, September.
    15. Fred Espen Benth, 2003. "On arbitrage-free pricing of weather derivatives based on fractional Brownian motion," Applied Mathematical Finance, Taylor & Francis Journals, vol. 10(4), pages 303-324.
    16. Svec, J. & Stevenson, M., 2007. "Modelling and forecasting temperature based weather derivatives," Global Finance Journal, Elsevier, vol. 18(2), pages 185-204.
    17. Melanie Cao & Jason Wei, 2004. "Weather derivatives valuation and market price of weather risk," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 24(11), pages 1065-1089, November.
    18. Roberto Buizza & James W. Taylor, 2004. "A comparison of temperature density forecasts from GARCH and atmospheric models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(5), pages 337-355.
    19. Dorje Brody & Joanna Syroka & Mihail Zervos, 2002. "Dynamical pricing of weather derivatives," Quantitative Finance, Taylor & Francis Journals, vol. 2(3), pages 189-198.
    20. Guo, Hui & Kassa, Haimanot & Ferguson, Michael F., 2014. "On the Relation between EGARCH Idiosyncratic Volatility and Expected Stock Returns," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 49(1), pages 271-296, February.
    21. Massimiliano Caporin & Juliusz Preś, 2013. "Forecasting Temperature Indices Density with Time‐Varying Long‐Memory Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(4), pages 339-352, July.
    22. Taylor, James W. & Buizza, Roberto, 2006. "Density forecasting for weather derivative pricing," International Journal of Forecasting, Elsevier, vol. 22(1), pages 29-42.
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