This paper applies a Qual VAR approach to generate a continuous banking crisis indicator from an underlying latent variable using a Markov Chain Monte Carlo algorithm. Four decades of banking crises are assessed by accounting for the evolutionary nature of precursors, as measured through periodic, regional, and developmental effects using a representative sample of countries. Aggregate results from forecast error variance decomposition show that banking sector variables explain nearly half of total variation, external sector a third and real sector a fifth. Findings suggest that recursive out-of-sample forecasts up to 12-months preceding a banking crisis render vital early warning signals, and as based on quarterly data, support expeditious response times. In out-of-sample forecasting, the Qual VAR outperforms a probit model. Improved forecasting performance may assist banking oversight departments and support remediation efforts of policymakers to adequately and timeously respond to banking crises.
This paper applies a Qual VAR approach to generate a continuous banking crisis indicator from an underlying latent variable using a Markov Chain Monte Carlo algorithm. Four decades of banking crises are assessed by accounting for the evolutionary nature of precursors, as measured through periodic, regional, and developmental effects using a representative sample of countries. Aggregate results from forecast error variance decomposition show that banking sector variables explain nearly half of total variation, external sector a third and real sector a fifth. Findings suggest that recursive out-of-sample forecasts up to 12-months preceding a banking crisis render vital early warning signals, and as based on quarterly data, support expeditious response times. In out-of-sample forecasting, the Qual VAR outperforms a probit model. Improved forecasting performance may assist banking oversight departments and support remediation efforts of policymakers to adequately and timeously respond to banking crises.