Bokusheva, Raushan (2010): Measuring the dependence structure between yield and weather variables.
Download (320kB) | Preview
The design and pricing of weather-based crop insurance and weather derivatives is strongly based on an implicit assumption that the dependence structure between yields and weather variables remains unchanged over time. In this paper, we prove this assumption based on empirical time series of weather variables and farm wheat yields from Kazakhstan over the period from 1961 to 2003. By employing two different methods to measure dependence in multivariate distributions – the regression analysis and copula approach – we reveal statistically significant temporal changes in the joint distribution of relevant variables. These empirical results indicate that greater effort is required to capture potential temporal changes in the dependence between yield and weather variables, and subsequently to consider them in the design and rating of weather-based insurance instruments.
|Item Type:||MPRA Paper|
|Original Title:||Measuring the dependence structure between yield and weather variables|
|Keywords:||weather-based index insurance, dependence structure, copula estimation, Bayesian hierarchical model, Kazakhstan.|
|Subjects:||C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models
G - Financial Economics > G2 - Financial Institutions and Services > G22 - Insurance ; Insurance Companies ; Actuarial Studies
C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C11 - Bayesian Analysis: General
|Depositing User:||Raushan Bokusheva|
|Date Deposited:||19. May 2010 15:38|
|Last Modified:||13. Feb 2013 11:51|
Barnett BJ, Mahul O. Weather Index Insurance for Agriculture and Rural Area in Lower-Income Countries. American Journal of Agricultural Economics, 2007; 89:1241-1247.
Bokusheva R, Breustedt G. Ex ante evaluation of index-based crop insurance effectiveness, contributed paper for the XII EAAE Congress ‘People, Food and Environments: Global Trends and European Strategies’, Ghent (Belgium), August 26-30, 2008.
Breustedt G, Bokusheva R, Heidelbach O. The potential of index insurance schemes to reduce farmers’ yield risk in an arid region. Journal of Agricultural Economics, 2008; 59:312-328.
Carlin BP, Louis TA. Bayesian methods for data analysis, 3rd edition. CRC Press, Taylor & Francis Group: Boca Raton, 2009.
Embrechts P, McNeil AJ, Straumann D. Correlation and dependency in risk management: properties and pitfalls. Pages 176–223 in Dempster M (ed). Risk Management: Value at Risk and Beyond Cambridge University Press, 2002.
Gamerman D, Lopes HF. Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference, 2nd eds. Chapman & Hall, London, 2006.
IPCC. Climate Change 2007 – Mitigation of Climate Change. Contribution of Working Group III to the Fourth Assessment Report of the IPCC. IPCC, Geneva, Switzerland, 2007.
McNeil A, Frey R, Embrechts P. (2005). Quantitative Risk Management, Princeton University Press, Princeton, 2005.
Miranda MJ. Area-Yield Crop Insurance Reconsidered. American Journal of Agricultural Economics, 1991; 73:233-242.
Musshoff O, Odening M, Xu W. Management of climate risks in agriculture – will weather derivatives permeate? Applied Economics, (2009); 1-11, iFirst. DOI: 10.1080/00036840802600210.
Shamen AM. Ob Issledovanii Zasushlivih Yavlenij v Kazakhstane (Research on Drought in Kazakhstan). Hydrometeorology and Ecology, 1997; 2:39-56.
Skees JR, Gober S, Varangis P, Lester R, Kalavakonda V. Developing Rainfall-Based Index Insurance in Morocco. Policy Research Working Paper 2577. World Bank, 2001.
Skees JR, Black JR, Barnett BJ. Designing and Rating an Area Yield Crop Insurance Contract. American Journal of Agricultural Economics, 1997; 79:430-438.
Skees, JR, Hazell P, Miranda M. New Approaches to Crop Yield Insurance in Developing Countries. EPTD Discussion Paper No. 55, Environment and Production Technology Division, International Food Policy Research Institute, Washington, D.C.S, 1999.
Sklar A. Fonctions de répartition à n dimension et leurs marges, Publ. Inst. Stat. Univ. Paris, 1959; 8:299-231.
Spiegelhalter DJ, Best NG, Carlin BP, Linde A. Bayesian measures of model complexity and fit (with discussion). Journal of Royal Statistical Society, 2002; 64:583–639.
Spiegelhalter DJ, Thomas A, Best NG. WinBUGs 1.4. Computer Program. Imperial College and MRC Biostatistics, Unit: Cambridge, 2003.
Turvey CG. Weather Derivatives for Specific Event Risks in Agriculture, Review of Agricultural Economics, 2001; 23:333–351.
Trivedi PK, Zimmer DM. Copula Modeling: An Introduction for Practitioners. now Publishers Inc., Delft, The Netherlands, 2007.
UN Department of Economic and Social Affairs. Developing Index-Based Insurance for Agriculture in Developing Countries. Sustainable Development Innovation Briefs, 2007; 2:1-8.
Varangis P, Skees J, Barnett B. Weather Indexes for Developing Countries. In Dischel R (ed). Climate Risk and the Weather Market. London: Risk Books, 2002.
Vedenov DV. Application of copulas to estimation of joint crop yield distributions, Contributed paper at the Annual Meeting of the AAEA 2008, Orlando, USA, July 27-29, 2008.
Vedenov DV, Barnett BJ. Efficiency of weather derivatives as primary crop insurance instruments. Journal of Agricultural and Resource Economics, 2004, 29:387-403.
Xu W, Odening M, Musshoff O. Indifference pricing for weather derivatives. American Journal of Agricultural Economics, 2008; 90:979-993.
Xu W, Filler G, Odening M, Okhrin O. On the systemic nature of weather risk. Agricultural Finance Review, 2010; 70 (in print).
Zhu Y, Ghosh S, Goodwin B. Modeling dependence in the design of whole farm insurance contract a copula-based approach, Contributed paper at the Annual Meeting of the AAEA 2008, Orlando, USA, July 27-29, 2008.