CMStatistics 2022: Start Registration
View Submission - CMStatistics
Title: Bayesian spatio-temporal methods: an application to forecasting short-term defaults of firms in a commercial network Authors:  Silvia Montagna - University of Turin (Italy) [presenting]
Raffaele Argiento - Università degli Studi di Bergamo (Italy)
Claudia Berloco - Intesa Sanpaolo SPA (Italy)
Abstract: To protect financial institutions from unexpected credit losses, it is of primary importance to foresee any evidence of 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). We look for evidence of contagion of liquidity distress on an Intesa Sanpaolo proprietary dataset by means of Bayesian spatial and spatio-temporal models. The 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.