Early warning systems using dynamic factor models: An application to Asian economies

Chi Truong, Jeffrey Sheen*, Stefan Trück, James Villafuerte

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


This study develops an early warning system for financial crises with a focus on small open economies. We contribute to the literature by developing macro-financial dynamic factor models that extract useful information from a rich but unbalanced mixed frequency data set that includes a range of global and domestic economic and financial indicators. The framework is applied to several Asian countries—Thailand, South Korea, Singapore, Malaysia, the Philippines and Indonesia. Logit regression models that use the extracted factors and other leading indicators have significant power in predicting systemic events. In-sample and out-of-sample test results indicate that the extracted factors help to improve the predictive power over a model that uses only sufficiently long history indicators. Importantly, models that include the dynamic factors yield consistently better out-of-sample crisis prediction results for key performance measures such as a usefulness index, the noise to signal ratio, and AUROC.

Original languageEnglish
Article number100885
Pages (from-to)1-41
Number of pages41
JournalJournal of Financial Stability
Early online date19 May 2021
Publication statusPublished - Feb 2022


  • Asian countries
  • Early warning system
  • Factor models
  • Mixed frequency
  • Systemic financial risk


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