Validation of PARX Models for Default Count Prediction - Agosto, Arianna; Raffinetti, Emanuela (Frontiers in Artificial Intelligence, 2019)
The growing importance of financial technology platforms, based on interconnectedness, makes necessary the development of credit risk measurement models that properly take contagion into account. Evaluating the predictive accuracy of these models is achieving increasing importance to safeguard investors and maintain financial stability. The aim of this paper is twofold. On the one hand, we provide an application of Poisson autoregressive stochastic processes to default data with the aim of investigating credit contagion; on the other hand, focusing on the validation aspects, we assess the performance of these models in terms of predictive accuracy using both the standard metrics and a recently developed criterion, whose main advantage is being not dependent on the type of predicted variable. This new criterion, already validated on continuous and binary data, is extended also to the case of discrete data providing results which are coherent to those obtained with the classical predictive accuracy measures. To shed light on the usefulness of our approach, we apply Poisson autoregressive models with exogenous covariates (PARX) to the quarterly count of defaulted loans among Italian real estate and construction companies, comparing the performance of several specifications. We find that adding a contagion component leads to a decisive improvement in model accuracy with respect to the only autoregressive specification.