Computational Economics

, Volume 44, Issue 1, pp 67–86 | Cite as

Minimizing Geographical Basis Risk of Weather Derivatives Using A Multi-Site Rainfall Model

Article

Abstract

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.

Keywords

Risk management Weather risk Regional diversification  Portfolio weights 

Notes

Acknowledgments

The financial support from the German Research Foundation via the CRC 649 ‘Economic Risk’, Humboldt-University Berlin, is gratefully acknowledged. Moreover, the authors would like to thank the participants of the CRC 649 Conference 2011 for their helpful comments and discussions on this topic.

References

  1. Ang, A., Chen, J., & Xing, Y. (2006). Downside risk. Review of Financial Studies, 19(4), 1191–1239.CrossRefGoogle Scholar
  2. Bentler, P. M., & Chou, C.-P. (1987). Practical issues in structural modeling. Sociological Methods Research, 16, 78–117.CrossRefGoogle Scholar
  3. Berg, E., & Schmitz, B. (2008). Weather-based instruments in the context of whole-farm risk management. Agricultural Finance Review, 68(1), 119–133.CrossRefGoogle Scholar
  4. Buishand, T. A., & Brandsma, T. (2001). Multisite simulation of daily precipitation and temperature in the Rhine Basin by nearest-neighbor resampling. Water Resources Research, 37(11), 2761–2776.CrossRefGoogle Scholar
  5. Cao, M., Li, A., & Wei, J. (2004). Precipitation modeling and contract valuation: A frontier in weather derivatives. The Journal of Alternative Investments, 7(2), 93–99.CrossRefGoogle Scholar
  6. Cheng, G., & Roberts, M. C. (2004). Weather derivatives in the presence of index and geographical basis risk: Hedging dairy profit risk. In Proceedings of the NCR-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management, St. Louis, MO.Google Scholar
  7. Cowden, J. R., Watkins, D. W, Jr., & Mihelcic, J. R. (2008). Stochastic rainfall modeling in West Africa: Parsimonious approaches for domestic rainwater harvesting assessment. Journal of Hydrology, 361(1–2), 64–77.Google Scholar
  8. Diaz-Caneja, M. B., Conte, C. G., Pinilla, F. J. G., Stroblmair, J., Catenaro, R., & Dittmann, C. (2009). Risk management and agricultural insurance schemes in Europe. JRC Reference Report, EU-23943, EN-2009, European Commission.Google Scholar
  9. Diebold, F. X., & Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business & Economic Statistics, 13, 134–144.Google Scholar
  10. Dischel, R. S. (2002). Climate risk and the weather market. London: Risk Books.Google Scholar
  11. Härdle, W. K., & Osipenko, M. (2011). Pricing Chinese rain: A multi-site multi-period equilibrium pricing model for rainfall derivatives. SFB 649 Discussion Paper 2011–055.Google Scholar
  12. Hughes, J. P., Guttorp, P., & Charles, S. P. (1999). A non-homogeneous hidden Markov model for precipitation occurrence. Applied Statistics, 48(1), 15–30.Google Scholar
  13. Lazo, J. K., Lawson, M., Larsen, P. H., & Waldman, D. M. (2011). US economic sensitivity to weather variability. Bulletin of the American Meteorological Society, 92(6), 709–720.CrossRefGoogle Scholar
  14. Lee, T.-H. (2008). Loss functions in time series forecasting. In W. A. Darity Jr. (Ed.), International encyclopedia of the social sciences (2nd ed., Vol. 9). Detroit: Macmillan Thomson Gale Publishers.Google Scholar
  15. Li, Z., Brissette, F., & Chen, J. (2012). Finding the most appropriate precipitation probability distribution for stochastic weather generation and hydrological modelling in Nordic watersheds. Hydrological Processes. doi: 10.1002/hyp.9499.
  16. López Cabrera, B., Odening, M., & Ritter, M. (2013). Pricing rainfall futures at the CME. Journal of Banking and Finance, 37, 4286–4298. doi: 10.1016/j.jbankfin.2013.07.042.
  17. McNeil, A. J., Frey, R., & Embrechts, P. (2005). Quantitative risk management: Concepts, techniques, and tools. New Jersey: Princeton University Press.Google Scholar
  18. Mehrotra, R., Srikanthan, R., & Sharma, A. (2006). A comparison of three stochastic multi-site precipitation occurrence generators. Journal of Hydrology, 331(1–2), 280–292.CrossRefGoogle Scholar
  19. Miranda, M. J., & Gonzalez-Vega, C. (2011). Systemic risk, index insurance, and optimal management of agricultural loan portfolios in developing countries. American Journal of Agricultural Economics, 93(2), 399–406.Google Scholar
  20. Mußhoff, O., Odening, M., & Xu, W. (2011). Management of climate risks in agriculture—will weather derivatives permeate? Applied Economics, 43(9), 1067–1077.CrossRefGoogle Scholar
  21. Odening, M., Muhoff, O., & Xu, W. (2007). Analysis of rainfall derivatives using daily precipitation models: Opportunities and pitfalls. Agricultural Finance Review, 67(1), 135–156.CrossRefGoogle Scholar
  22. Roldán, J., & Woolhiser, D. A. (1982). Stochastic daily precipitation models: 1. A comparison of occurrence processes. Water Resources Research, 18(5), 1451–1459.CrossRefGoogle Scholar
  23. Salsón, S., & Garcia-Bartual, R. (2003). A space-time rainfall generator for highly convective Mediterranean rainstorms. Natural Hazards and Earth System Sciences, 3, 103–114.CrossRefGoogle Scholar
  24. Shepard, D. (1968). A two-dimensional interpolation function for irregularly-spaced data. In Proceedings of the 1968 ACM National Conference (pp. 517–524).Google Scholar
  25. Vedenov, D. V., & Barnett, B. J. (2004). Efficiency of weather derivatives as primary crop insurance instruments. Journal of Agricultural and Resource Economics, 29(3), 387–403.Google Scholar
  26. Wang, W., Bobojonov, I., Härdle, W. K., & Odening, M. (2013). Testing for increasing weather risk. Stochastic Environmental Research and Risk Assessment, 27, 1565–1574.CrossRefGoogle Scholar
  27. Wilks, D. S. (1998). Multisite generalization of a daily stochastic precipitation generation model. Journal of Hydrology, 210(1–4), 178–191.CrossRefGoogle Scholar
  28. Woodard, J. D., & Garcia, P. (2008). Basis risk and weather hedging effectiveness. Agricultural Finance Review, 68(1), 99–117.CrossRefGoogle Scholar
  29. Woolhiser, D. A., & Pegram, G. G. S. (1979). Maximum likelihood estimation of Fourier coefficients to describe seasonal variations of parameters in stochastic daily precipitation models. Journal of Applied Meteorology, 18(1), 34–42.CrossRefGoogle Scholar
  30. Woolhiser, D. A., & Roldán, J. (1982). Stochastic daily precipitation models: 2. A comparison of distributions of amounts. Water Resources Research, 18(5), 1461–1468.CrossRefGoogle Scholar
  31. Xu, W., Filler, G., Odening, M., & Okhrin, O. (2010). On the systemic nature of weather risk. Agricultural Finance Review, 70(2), 267–284.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Department of Agricultural Economics and Rural DevelopmentGeorg-August Universität GöttingenGöttingenGermany
  2. 2.Department of Agricultural EconomicsHumboldt-Universität zu BerlinBerlinGermany
  3. 3.Department of Agricultural Economics and Rural DevelopmentGeorg-August Universität Göttingen GöttingenGermany

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