Early warning systems using dynamic factor models: An application to Asian economies
Stefan Trück and
Journal of Financial Stability, 2022, vol. 58, issue C
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.
Keywords: Systemic financial risk; Early warning system; Asian countries; Factor models; Mixed frequency (search for similar items in EconPapers)
JEL-codes: C33 E44 G01 G17 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finsta:v:58:y:2022:i:c:s1572308921000450
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