Unobserved components models with stochastic volatility for extracting trends and cycles in credit

This paper develops a multivariate filter based on an unobserved component trend-cycle model. It incorporates stochastic volatility and relies on specific formulations for the cycle component. We test the performance of this algorithm within a Monte-Carlo experiment and apply this decomposition tool to study the evolution of the financial cycle (estimated as the cycle of the credit-to-GDP ratio) for the United States, the United Kingdom and Ireland. We compare our credit cycle measure to the Basel III credit-to- GDP gap, prominent for its role informing the setting of countercyclical capital buffers. The Basel-gap employs the Hodrick-Prescott filter for trend extraction. Filtering methods reliant on similar-duration assumptions suffer from endpoint-bias or spurious cycles. These shortcomings might bias the shape of the credit cycle and thereby limit the precision of the policy assessment reliant on its evolution to target financial distress. Allowing for a flexible law of motion of the variance covariance matrix and informing the estimation of the cycle via economic fundamentalsweare able to improve the statistical properties and to find a more economically meaningful measure of the build-up of cyclical systemic risks. Additionally, we find a large heterogeneity in the drivers of the credit cycles across time and countries. This result stresses the relevance in macro prudential policy of considering flexible approaches that can be tailored to country characteristics in contrast to standardized indicators.