2014年-IMF国际货币组织全球_Financial_Soundness_Indicators_and_the_Characteristics_of_Financial_Cycles_26页_912kb

Summary of "Financial Soundness Indicators and the Characteristics of Financial Cycles"

Core Content

This paper examines the relationship between financial soundness indicators (FSIs) and the characteristics of financial cycles, focusing on how changes in FSIs during different phases of the credit cycle affect credit growth and the severity of financial downturns.

Main Viewpoints

• Financial soundness indicators are used to assess the health of banks and the broader financial system.
• Improving FSIs during a downturn may exacerbate credit contraction and financial instability, whereas maintaining or improving FSIs during upturns can support more stable credit growth.
• The timing of regulatory changes is crucial—FSIs should be built up during good times to prepare for future shocks, not during downturns when they may worsen the contraction.
• FSIs are relevant for gauging financial system soundness, as better capitalized and more liquid banks are generally more resilient to shocks.
• The relationship between FSIs and financial cycles is not linear and depends on the phase of the cycle (upturn or downturn).

Key Information

Financial Soundness Indicators (FSIs)

Three SRF-FSIs are analyzed in the paper:

1. Capital-asset ratio: Measures how much a bank's assets are financed by its own capital. Higher ratios indicate better risk-bearing capacity.
2. Liquid-asset ratio: Reflects the liquidity available to meet cash demands. It tends to decline during good times and increase during bad times.
3. NOP-capital ratio: Measures the exposure of banks to foreign exchange risks relative to their capital. Lower NOP-capital ratios suggest less foreign exchange risk.

Financial Cycles

• The financial cycle is defined as the cycle of bank credit to the private sector.
• The Bry-Boschan Quarterly (BBQ) algorithm is used to identify the peaks, troughs, and phases of the financial cycle.
• Contraction phases (downturns) are typically shorter (5-7 quarters) and more volatile than expansion phases (upturns), which are longer and more stable.
• A typical credit cycle involves about a 4% change in credit.

Empirical Findings

• During downturns, increases in capital-asset and liquid-asset ratios are associated with larger credit shrinkage.
• During upturns, increases in liquid-asset ratios are associated with faster credit growth, while capital-asset ratios have a smaller effect.
• NOP-capital ratios have little effect on credit growth during both phases.
• Better initial FSIs (higher capital and liquidity) are associated with milder and shorter downturns.
• Upturns starting with higher NOP-capital ratios tend to be longer and less abrupt.

Methodology

• The paper uses panel VAR models to analyze the dynamic relationship between FSIs and credit growth.
• The Helmert procedure is used to account for country heterogeneity and avoid biased estimates.
• The variance decomposition of credit growth over a 10-month horizon shows that the liquid-asset ratio has the largest impact, explaining 10% of the variation during downturns and 8% during upturns.
• The capital-asset ratio has a more pronounced effect during downturns, explaining 2% of the variation.

Theoretical and Empirical Context

• The paper references existing literature on the cyclicality of capital requirements and the relationship between FSIs and economic cycles.
• Covas and Fujita (2009) argue that procyclical capital requirements increase output volatility and reduce household welfare.
• N'Diaye (2009) suggests that countercyclical prudential regulations can reduce output fluctuations and financial instability.
• Resende et al. (2011) show that countercyclical capital requirements have a significant stabilizing effect on macroeconomic variables.
• Cihak & Schaeck (2010) and Babihuga (2007) found that FSIs fluctuate with business cycles and interest rates, but may not be strong leading indicators of crises.

Data and Analysis

• The SRF-FSIs are calculated from monthly data of the IMF's Monetary and Financial Statistics (MFS) database.
• IMF-FSIs are less frequently reported and cover fewer countries, making them less suitable for regression analysis.
• The SRF-FSIs are used to assess the dynamic responses of credit growth to changes in FSIs across different phases of the financial cycle.
• The impulse response functions (Figure 2) show that FSI changes during downturns tend to reduce credit growth, while during upturns they support credit expansion.

Conclusion

The paper concludes that FSIs are important indicators of financial system soundness and that improving them during downturns can worsen credit contraction. Therefore, policy makers should focus on strengthening FSIs during good times to better prepare for future financial cycles. While FSIs may not be perfect early warning indicators, they can contribute to reducing the severity and volatility of financial cycles when managed appropriately.