Measuring dependence in joint distributions of yield and weather variables

Purpose – The design and pricing of weather‐based insurance instruments is strongly based on an implicit assumption that the dependence structure between crop yields and weather variables remains unchanged over time. The purpose of this paper is to verify this critical assumption by employing historical time series of weather and farm yields from a semi‐arid region. Design/methodology/approach – The analysis employs two different approaches to measure dependence in multivariate distributions – the regression analysis and copula approach. The estimations are done by employing Bayesian hierarchical model. Findings – The paper reveals statistically significant temporal changes in the joint distribution of weather variables and wheat yields for grain‐producing farms in Kazakhstan over the period from 1961 to 2003. Research limitations/implications – By questioning its basic assumption the paper draws attention to serious limitations in the current methodology of the weather‐based insurance design. Practical implications – The empirical results obtained indicate that the relationship between weather and crop yields is not fixed and can change over time. Accordingly, greater effort is required to capture potential temporal changes in the weather‐yield‐relationship and to consider them while developing and rating weather‐based insurance instruments. Originality/value – The estimation of selected copula and regression models has been done by employing Bayesian hierarchical models.