Combination of autoregressive graphical models and time series bootstrap methods for risk management in marine insurance

Abstract

In this paper a methodology to assess risk by forecasting the trend of marine losses at a global scale is presented. The proposed procedure, which can be used to continuously update an insurance company’s costing model, identifies the most relevant risk indicators through Probabilistic Graphical Models (PGMs). The use of PGMs makes the variable selection more understandable since they provide a clear interface to interpret the model and perform predictions. Furthermore, this procedure can be used to verify independence relationships, validate the dataset and identify unexpected links among the considered variables. The robustness of estimates, crucial for risk assessment in the insurance context, is dealt with bootstrap.