IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2409.04541.html
   My bibliography  Save this paper

Quantifying Seasonal Weather Risk in Indian Markets: Stochastic Model for Risk-Averse State-Specific Temperature Derivative Pricing

Author

Listed:
  • Soumil Hooda
  • Shubham Sharma
  • Kunal Bansal

Abstract

This technical report presents a stochastic model for pricing weather derivatives and devising hedging strategies tailored to Indian markets. We model temperature dynamics using a modified Ornstein-Uhlenbeck process with jumps to account for sudden shocks, such as heatwaves and coldwaves. Historical data from 12 Indian states (1951-2023) is used for calibration, and Monte Carlo simulations are employed under the risk-neutral measure to price Heating Degree Days (HDD), Cooling Degree Days (CDD), and extreme event options. Sensitivity analysis reveals that a 20% increase in volatility leads to an approximate 4.2% increase in option prices, highlighting the critical impact of volatility on derivative pricing. Results show that HDD options in colder states like Himachal Pradesh are significantly more expensive, with prices reaching up to INR 684,693, while CDD options in hotter states like Gujarat are priced higher, up to INR 262,986. A comprehensive portfolio analysis indicates that investing INR 120,000 in HDD put options in Uttar Pradesh yields an expected payoff of INR 132,369, resulting in a return on investment (ROI) of 10.3%. Conversely, a similar investment in Karnataka yields a negative ROI of -66.7% due to its milder climate. Hedging strategies are tailored to each state's climatic risk, with recommendations to buy 90.66 HDD put options at a strike of 90.89 in Uttar Pradesh and invest in CDD call options in Gujarat. These insights offer practical solutions for managing temperature-related financial risk in energy and agriculture, providing actionable, state-specific hedging strategies for diverse climatic scenarios in India.

Suggested Citation

  • Soumil Hooda & Shubham Sharma & Kunal Bansal, 2024. "Quantifying Seasonal Weather Risk in Indian Markets: Stochastic Model for Risk-Averse State-Specific Temperature Derivative Pricing," Papers 2409.04541, arXiv.org, revised Sep 2024.
    • Handle: RePEc:arx:papers:2409.04541
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2409.04541
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    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.
    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. 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.
    2. Monika Wieczorek-Kosmala, 2020. "Weather Risk Management in Energy Sector: The Polish Case," Energies, MDPI, vol. 13(4), pages 1-21, February.
    3. Dupuis, Debbie J., 2011. "Forecasting temperature to price CME temperature derivatives," International Journal of Forecasting, Elsevier, vol. 27(2), pages 602-618.
    4. 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).
    5. Prabakaran, Sellamuthu & Garcia, Isabel C. & Mora, Jose U., 2020. "A temperature stochastic model for option pricing and its impacts on the electricity market," Economic Analysis and Policy, Elsevier, vol. 68(C), pages 58-77.
    6. Alessio Giorgini & Rogemar S. Mamon & Marianito R. Rodrigo, 2021. "A Stochastic Harmonic Oscillator Temperature Model for the Valuation of Weather Derivatives," Mathematics, MDPI, vol. 9(22), pages 1-15, November.
    7. Heng Xiong & Rogemar Mamon, 2018. "Putting a price tag on temperature," Computational Management Science, Springer, vol. 15(2), pages 259-296, June.
    8. 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.
    9. 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..
    10. 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.
    11. Wei Yuan & Ahmet Göncü & Giray Ökten, 2015. "Estimating sensitivities of temperature-based weather derivatives," Applied Economics, Taylor & Francis Journals, vol. 47(19), pages 1942-1955, April.
    12. 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, August.
    13. 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.
    14. 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.
    15. Turvey, Calum G. & Norton, Michael, 2008. "An Internet-Based Tool for Weather Risk Management," Agricultural and Resource Economics Review, Cambridge University Press, vol. 37(1), pages 63-78, April.
    16. Musshoff, Oliver & Odening, Martin & Xu, Wei, 2006. "Modeling and Hedging Rain Risk," 2006 Annual meeting, July 23-26, Long Beach, CA 21050, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    17. Aur'elien Alfonsi & Nerea Vadillo, 2023. "Risk valuation of quanto derivatives on temperature and electricity," Papers 2310.07692, arXiv.org, revised Apr 2024.
    18. Wolfgang Karl Hardle and Maria Osipenko, 2012. "Spatial Risk Premium on Weather Derivatives and Hedging Weather Exposure in Electricity," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    19. Larsson, Karl, 2023. "Parametric heat wave insurance," Journal of Commodity Markets, Elsevier, vol. 31(C).
    20. L. Kermiche & N. Vuillermet, 2016. "Weather derivatives structuring and pricing: a sustainable agricultural approach in Africa," Applied Economics, Taylor & Francis Journals, vol. 48(2), pages 165-177, January.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:arx:papers:2409.04541. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.