Minimizing Geographical Basis Risk of Weather Derivatives Using A Multi-Site Rainfall Model
- 369 Downloads
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.
KeywordsRisk management Weather risk Regional diversification Portfolio weights
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.
- 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
- 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
- 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
- Diebold, F. X., & Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business & Economic Statistics, 13, 134–144.Google Scholar
- Dischel, R. S. (2002). Climate risk and the weather market. London: Risk Books.Google Scholar
- 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
- 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
- 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
- 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.
- 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.
- McNeil, A. J., Frey, R., & Embrechts, P. (2005). Quantitative risk management: Concepts, techniques, and tools. New Jersey: Princeton University Press.Google Scholar
- 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
- 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
- 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