Re-anchoring countercyclical capital buffers: Bayesian estimates and alternatives focusing on credit growth
Rodrigo Gonzalez,
Leonardo Marinho and
Joaquim Ignacio Alves de Vasconcellos e Lima
International Journal of Forecasting, 2017, vol. 33, issue 4, 1007-1024
Abstract:
We re-evaluate the Basel Committee on Banking Supervision’s (BCBS) proposed framework with the credit-to-GDP gap as an anchor indicator relative to the countercyclical capital buffer (CCB), and propose an alternative approach that focuses on credit-to-GDP growth. We estimate Bayesian structured time series models (STM) fully and recursively for 34 countries and evaluate whether these state components and related indicators can anticipate crises over the following three years. Using an early warning framework similar to the original BCBS one, we find leading indicators that outperform the credit-to-GDP gap in anticipating banking crises, with lower noise-to-signal (NS) ratios and similar sensitivities to threshold variation (assessed using receiver operational characteristics (ROC)). Moreover, the credit-to-GDP gap fails an exercise using limited information, suggesting that the 10% anchor on this indicator that was put forward by the BCBS can be misleading in countries with short credit series. Finally, we present an illustrative panel of CCB use with our leading indicators.
Keywords: Financial cycle; Bayesian STM; Countercyclical capital buffer (CCB); Banking crisis; Noise-to-signal (NS) (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0169207017300560
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:33:y:2017:i:4:p:1007-1024
DOI: 10.1016/j.ijforecast.2017.04.006
Access Statistics for this article
International Journal of Forecasting is currently edited by R. J. Hyndman
More articles in International Journal of Forecasting from Elsevier
Bibliographic data for series maintained by Catherine Liu ().