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Monday, January 27, 2020

**Abstract:** Insurance companies keep track of each policyholder's claims over time, resulting in longitudinal data. Efficient modeling of time dependence in longitudinal claim data can improve the prediction of future claims needed, for example, for ratemaking. Insurance claim data have their special complexity. They usually follow a two-part mixed distribution: a probability mass at zero corresponding to no claim and an otherwise positive claim from a skewed and long-tailed distribution. We propose a two-part D-vine copula model to study longitudinal mixed claim data. We build two stationary D-vine copulas. One is used to model the time dependence in binary outcomes resulting from whether or not a claim has occurred, and the other studies the dependence in the claim size given occurrence over time. The proposed model can predict the probability of making claims and the quantiles of severity given occurrence straightforwardly. We use our approach to investigate a dataset from the Local Government Property Insurance Fund in the state of Wisconsin.