Do Macroprudential Policies Reduce the Probability of Financial Crises?
Jae-Hyun Suh Jae-Hyun Suh
What the paper says
Purpose – Considering Korea’s repeated exposure to global financial shocks—such as the 1997 Asian financial crisis, the 2008 global financial crisis, and the recent COVID-19 pandemic—this paper investigates whether macroprudential policies (MPs) can reduce the likelihood of financial crises in Korea. While theoretical studies suggest that MPs can contain systemic risk, empirical research specifically assessing MPs’ crisis-prevention role in the Korean context has been lacking. Design/methodology – We examine the relationship between MPs and four categories of financial crisis—banking, currency, debt, and inflation—using a panel dataset of 27 countries, including Korea, from 1990 to 2017. We use two MP variables: the loan-to-value (LTV) ratio and a composite index aggregating 17 MP instruments. Employing probit and complementary log-log models, we estimate the nonlinear relationship between MPs and crisis probabilities. Findings – Results indicate that tighter MPs are associated with lower probabilities of financial crises, especially debt, inflation, and banking crises. Furthermore, a higher LTV ratio is strongly linked to debt and inflation crises, and in some cases, it can perfectly predict inflation crises. Aggregate MP tightening significantly reduces the likelihood of banking and debt crises. However, MPs appear ineffective in preventing currency crises, which are driven largely by global economic factors. Originality/value – This study helps narrow an existing empirical gap by providing evidence on the role of MPs in crisis prevention, focusing on Korea. The findings suggest that maintaining and refining MPs is crucial to mitigating external shocks and preserving macrofinancial stability.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.