Evaluating early warning systems for currency crisis in select emerging ASEAN economies

• Main Authors: Chan, Mikhaela Kristine R., Co, Berndhart S., Juan, Mary Khristine P., Lacanlale, Erryl Ron M.
• Format: text
• Language: English
• Published: Animo Repository 2021
• Subjects: Currency crises > Southeast Asia; Finance and Financial Management
• Online Access: https://animorepository.dlsu.edu.ph/etdb_finman/12; https://animorepository.dlsu.edu.ph/context/etdb_finman/article/1017/viewcontent/juan2.pdf
• Institution: De La Salle University

This study examined the performance of early warning systems for currency crises in select emerging ASEAN economies, particularly the Philippines, Thailand, Indonesia, Malaysia, and Vietnam. The researchers compared the performance of the signal approach and logit model in predicting the likelihood of a currency crisis covering the period from 1995Q1 to 2020Q4 using quarterly data. The study also determined the most significant indicators of crises overall and per country. A total of 60 crises were identified using the meta-possibilistic fuzzy index function. Results suggest that both models were able to predict currency crises. However, the logit model is more accurate as it identified crises and significant indicators better, providing a more in-depth analysis to the data. The most significant indicators for the signal approach were real interest rates and M2, while for the logit model, these were interest rate, M1, inflation rate and consumer price index (CPI). The findings of this study may be used to further develop and improve the early warning systems for emerging economies, particularly in the ASEAN region. This research may also help policymakers, governments, financial institutions, and regulatory bodies in the assessment and determination of financial and or economic vulnerabilities. It would also afford policymakers, governments, and other stakeholders room to adjust their responses and prepare for potential situations or trouble. Future researchers are recommended to develop early warning systems per country to find the best performing model and get the most accurate result, in relation to the most significant indicators of that country.