Measuring the dependence structure between yield and weather variables
AbstractThe 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.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 22786.
Date of creation: Apr 2010
Date of revision:
weather-based index insurance; dependence structure; copula estimation; Bayesian hierarchical model; Kazakhstan.;
Find related papers by JEL classification:
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
This paper has been announced in the following NEP Reports:
- NEP-AGR-2010-05-29 (Agricultural Economics)
- NEP-ALL-2010-05-29 (All new papers)
- NEP-IAS-2010-05-29 (Insurance Economics)
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