Forecasting short-term defaults of firms in a commercial network via Bayesian spatial and spatio-temporal methods

To protect financial institutions from unexpected credit losses, during the monitoring phase of granted loans it is of primary importance to foresee any evidence of a contagion of liquidity distress across a network of firms. This term indicates a situation of lack of solvency of a firm (e.g., a customer) that propagates to other firms (e.g, its suppliers), which could consequently face challenges in repaying their own granted loans. In this paper, we look for the evidence of contagion of liquidity distress on an Intesa Sanpaolo proprietary dataset by means of Bayesian spatial and spatio-temporal models. Our results indicate that such models can detect cases of distress not yet apparent from covariate information collected on the firms by instead borrowing information from the network, leading to improved forecasting performance on the prediction of short-term default with respect to state-of-the-art methods.