Modeling dependence between report lag and claim amounts using copula models

Relationships between two or more variables are considered a phenomenon of interest in a world where modeling risk is becoming more and more popular. Having a variable that can explain the behavior of another can prove an important aid in understanding the variable of interest. This relationship is described as dependence between variables.The most common measure used to quantify dependence between variables is the Pearson’s correlation coefficient. However, the Pearson’s correlation coefficient is only a single figure and therefore; there is only a limited amount of information we can derive from it concerning the dependence between. In addition to this, the Pearson’s correlation coefficient assumes a linear relationship exists between the variables. Copulas on the other hand are distributions used to join the marginal distributions of the variable to obtain multivariate distributions. This enables one to derive more information regarding the dependence between the variables. The following paper seeks to study the dependence between report lag and the claim amount variables in the insurance context using copulas.