IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i7p1628-d1109309.html
   My bibliography  Save this article

A Stochastic Weather Model for Drought Derivatives in Arid Regions: A Case Study in Qatar

Author

Listed:
  • Jayeong Paek

    (Department of Mathematics, Trent University, Peterborough, ON K9L 0G2, Canada
    Current address: Department of Statistics, Chonnam National University, Gwangju 61186, Republic of Korea.)

  • Marco Pollanen

    (Department of Mathematics, Trent University, Peterborough, ON K9L 0G2, Canada)

  • Kenzu Abdella

    (Department of Mathematics, Trent University, Peterborough, ON K9L 0G2, Canada)

Abstract

In this paper, we propose a stochastic weather model consisting of temperature, humidity, and precipitation, which is used to calculate a reconnaissance drought index ( RDI ) in Qatar. The temperature and humidity models include stochastic differential equations and utilize an adjusted Ornstein–Uhlenbeck (O–U) process. For the precipitation model, a first-order Markov chain is used to differentiate between wet and dry days and the precipitation amount on wet days is determined by a probability distribution. Five different probability distributions were statistically tested to obtain an appropriate precipitation amount. The evapotranspiration used in the RDI calculation incorporates crop coefficient values, depends on the growth stages of the crops, and provides a crop-specific and more realistic representation of the drought conditions. Five different evapotranspiration formulations were investigated in order to obtain the most accurate RDI values. The calculated RDI was used to assess the intensity of drought in Doha, Qatar, and could be used for the pricing of financial drought derivatives, a form of weather derivative. These derivatives could be used by agricultural producers to hedge against the economic effects of droughts.

Suggested Citation

  • Jayeong Paek & Marco Pollanen & Kenzu Abdella, 2023. "A Stochastic Weather Model for Drought Derivatives in Arid Regions: A Case Study in Qatar," Mathematics, MDPI, vol. 11(7), pages 1-18, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:7:p:1628-:d:1109309
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/7/1628/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/7/1628/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Dorje Brody & Joanna Syroka & Mihail Zervos, 2002. "Dynamical pricing of weather derivatives," Quantitative Finance, Taylor & Francis Journals, vol. 2(3), pages 189-198.
    3. Richards, Timothy J. & Manfredo, Mark R. & Sanders, Dwight R., 2004. "Pricing Weather Derivatives," Working Papers 28536, Arizona State University, Morrison School of Agribusiness and Resource Management.
    4. 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.
    5. G. Tsakiris & D. Pangalou & H. Vangelis, 2007. "Regional Drought Assessment Based on the Reconnaissance Drought Index (RDI)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(5), pages 821-833, May.
    6. Dwight R. Sanders, 2004. "Pricing Weather Derivatives," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(4), pages 1005-1017.
    7. Helyette Geman & M. Leonardi, 2005. "Alternative Approaches to Weather Derivatives Pricing," Post-Print halshs-00144304, HAL.
    8. repec:dau:papers:123456789/1386 is not listed on IDEAS
    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. 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.
    2. 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.
    3. Heng Xiong & Rogemar Mamon, 2018. "Putting a price tag on temperature," Computational Management Science, Springer, vol. 15(2), pages 259-296, June.
    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. A. Alexandridis & A. Zapranis, 2013. "Wind Derivatives: Modeling and Pricing," Computational Economics, Springer;Society for Computational Economics, vol. 41(3), pages 299-326, March.
    6. Bertrand, Jean-Louis & Brusset, Xavier & Fortin, Maxime, 2015. "Assessing and hedging the cost of unseasonal weather: Case of the apparel sector," European Journal of Operational Research, Elsevier, vol. 244(1), pages 261-276.
    7. Lim, Terence & Lo, Andrew W. & Merton, Robert C. & Scholes, Myron S., 2006. "The Derivatives Sourcebook," Foundations and Trends(R) in Finance, now publishers, vol. 1(5–6), pages 365-572, April.
    8. Markus Stowasser, 2011. "Modelling rain risk: a multi-order Markov chain model approach," Journal of Risk Finance, Emerald Group Publishing, vol. 13(1), pages 45-60, December.
    9. Wolfgang Karl Härdle & Brenda López Cabrera, 2012. "The Implied Market Price of Weather Risk," Applied Mathematical Finance, Taylor & Francis Journals, vol. 19(1), pages 59-95, February.
    10. 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).
    11. Birgit Lemmerer & Stephan Unger, 2019. "Modeling and pricing of space weather derivatives," Risk Management, Palgrave Macmillan, vol. 21(4), pages 265-291, December.
    12. 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.
    13. repec:hum:wpaper:sfb649dp2014-006 is not listed on IDEAS
    14. 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.
    15. 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.
    16. repec:hum:wpaper:sfb649dp2009-001 is not listed on IDEAS
    17. 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.
    18. Cyr, Don & Kusy, Martin, 2007. "Identification of stochastic processes for an estimated icewine temperature hedging variable," Working Papers 37298, American Association of Wine Economists.
    19. repec:hum:wpaper:sfb649dp2009-046 is not listed on IDEAS
    20. Benth, Fred & Härdle, Wolfgang Karl & López Cabrera, Brenda, 2009. "Pricing of Asian temperature risk," SFB 649 Discussion Papers 2009-046, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    21. Musshoff, Oliver & Hirschauer, Norbert, 2008. "Hedging von Mengenrisiken in der Landwirtschaft – Wie teuer dürfen „ineffektive“ Wetterderivate sein?," German Journal of Agricultural Economics, Humboldt-Universitaet zu Berlin, Department for Agricultural Economics, vol. 57(05), pages 1-12.
    22. 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.
    23. 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.

    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:gam:jmathe:v:11:y:2023:i:7:p:1628-:d:1109309. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.