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Minimizing geographical basis risk of weather derivatives using a multi-site rainfall model

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  • Ritter, Matthias
  • Musshoff, Oliver
  • Odening, Martin

Abstract

Weather risk is one of the main causes for income fluctuation in agriculture. Since 1997, the economic consequences of weather risk can be insured with weather derivatives, which are offered for many different weather events, such as temperature, rainfall, snow or hurricanes. It is well known that the hedging effectiveness of weather derivatives is interfered by the existence of geographical basis risk, i.e., the deviation of weather conditions at different locations. In this paper, we explore how geographical basis risk of rainfall based derivatives can be reduced by regional diversification. Minimizing geographical basis risk requires knowledge of the joint distribution of rainfall at different locations. For that purpose, we estimate a daily multi-site rainfall model from which optimal portfolio weights are derived. We find that this method allows to reduce geographical basis risk more efficiently than simpler approaches as, for example, inverse distance weighting.

Suggested Citation

  • Ritter, Matthias & Musshoff, Oliver & Odening, Martin, 2012. "Minimizing geographical basis risk of weather derivatives using a multi-site rainfall model," 123rd Seminar, February 23-24, 2012, Dublin, Ireland 122527, European Association of Agricultural Economists.
    • Handle: RePEc:ags:eaa123:122527
    DOI: 10.22004/ag.econ.122527
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    1. Wei Xu & Guenther Filler & Martin Odening & Ostap Okhrin, 2010. "On the systemic nature of weather risk," Agricultural Finance Review, Emerald Group Publishing Limited, vol. 70(2), pages 267-284, August.
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    Cited by:

    1. Ceballos, Francisco, 2016. "Estimating spatial basis risk in rainfall index insurance: Methodology and application to excess rainfall insurance in Uruguay," IFPRI discussion papers 1595, International Food Policy Research Institute (IFPRI).
    2. Goodrich, Brittney K. & Davidson, Kelly A., 2024. "Enrollment in Pasture, Rangeland, and Forage Rainfall Index Insurance: Awareness Matters," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 0(Preprint), January.
    3. Xiaofeng Cao & Ostap Okhrin & Martin Odening & Matthias Ritter, 2015. "Modelling spatio-temporal variability of temperature," Computational Statistics, Springer, vol. 30(3), pages 745-766, September.
    4. Brenda López Cabrera & Martin Odening & Matthias Ritter, 2013. "Pricing Rainfall Derivatives at the CME," SFB 649 Discussion Papers SFB649DP2013-005, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    5. Doms, Juliane, 2017. "Put, call or strangle? About the challenges in designing weather index insurances to hedge performance risk in agriculture," 57th Annual Conference, Weihenstephan, Germany, September 13-15, 2017 261990, German Association of Agricultural Economists (GEWISOLA).
    6. López Cabrera, Brenda & Odening, Martin & Ritter, Matthias, 2013. "Pricing rainfall futures at the CME," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4286-4298.
    7. Schmidt, Lorenz & Odening, Martin & Schlanstein, Johann & Ritter, Matthias, 2022. "Exploring the weather-yield nexus with artificial neural networks," Agricultural Systems, Elsevier, vol. 196(C).

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    More about this item

    Keywords

    Risk and Uncertainty;

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • Q14 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Finance
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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