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Credit cycle identification: A Markov-switching application
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Orientador(es)
Resumo(s)
This project aims to study credit dynamics and to identify phases of credit cycles at the country level. We applied a Markov-switching (MS) autoregressive framework and a MS with regime-invariant macroeconomic variables to a broad concept of credit, domestic credit. We used a sample of 10 developed countries. MS identification power is assessed using smooth probabilities of low growth states, collected as a by-product of models estimation, against historical databases of crisis events. Conclusions support that MS is accurate in identifying credit cycle phases, and that domestic credit is a good variable for such identification. Additionally, Credit Gap, excess growth over GDP and Broad Money contribute positively to the MS predictions.
Descrição
A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics
Palavras-chave
Credit cycles Phase identification Markov-switching