The dynamic Dirichlet process mixture model: An application to the banking sector in Australia

This paper proposes a new dynamic Bayesian nonparametric model to capture time varying distributions. Our model is built on Gutierrez et al. (2016) and Mena and Ruggiero (2016). We improve their algorithm through incorporating a break indicator and a hierarchical prior structure governing the parameters of all components in the mixture. Using the Australian banking statistics from 1925 to 2019, we apply the model to estimate the time varying bank size/growth distributions. Our results suggest that the skewness of the weighted bank growth distribution is procyclical to the business/financial cycle. Different quantiles of the weighted bank growth distribution exhibit different correlations with financial cycles.

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