Identifying Vulnerabilities in Systemically-Important Financial Institutions in a Macro-Financial Linkages Framework

During the subprime crisis, governments intervened to stabilize the financial condition of some troubled systemically important financial institutions. Two questions naturally arise: Why do some institutions receive intervention while others not? What are the macro-financial driving forces of the vulnerabilities in the systemically important SIFIs? This column summarizes the results of some recent studies about identifying vulnerabilities in SIFIs. Overall, the existing research suggests that leverage is the most reliable indicator, while several widely used indicators are not very useful in identifying the differences in SIFIs.

Systemically important financial institutions (SIFIs) are defined as those financial institutions that are systemically important in the context of size (the classic doctrine of too big to let fail) and the four C’s of systemic importance (contagion, concentration, correlation, and conditions) (Thomson, 2009);

During the subprime crisis, central banks and governments worldwide have taken unprecedented policy actions. A more detailed consideration of those questions involves a response to the following questions:

  • What are the common factors among the SIFIs that have required public intervention? Did balance sheet data, especially traditional financial soundness indicators (FSIs), provide meaningful warnings?
  • Can bank-specific indicators explain the development over time of the expected default frequencies (EDFs) for the systemically important SIFIs? What role does the macroeconomic and global situation play in this process? Can we find robust indicators that denote rising EDFs?

Any financial stability monitoring exercise would benefit from knowing the reasons behind the relative immunity of some SIFIs to government intervention during the subprime crisis. Thus, indicators that identify the key characteristics of the SIFIs are of considerable interest for analytical reasons as well as for understanding the implications of the differences between intervened and nonintervened SIFIs. In addition, these indicators could be helpful in identifying macro-financial linkages, promoting ongoing financial reforms, and designing crisis prevention initiatives.

A substantial amount of theoretical and empirical work has documented how financial soundness indicators are used to capture vulnerabilities in firms and economies. For instance, the financial crises of the late 1990s prompted the search for indicators of financial system soundness. Various studies have proposed early warning indicators of impending turmoil in banking systems (e.g., Hardy and Pazarbaşioğlu, 1999; Gonzalez-Hermasillo, 1998; Hutchinson and McDill, 1999; Hutchinson, 2002; European Central Bank, 2005). Despite these advances, there is increasing evidence that some FSIs might not fully capture the sources of risk. Besides the research on traditional balance sheet data, there is a growing body of literature that analyzes the macroeconomic determinants of banks’ credit risks. A more data-intensive approach is to examine the impact of macro factors on corporate and/or household sector default risk and map these developments into banks’ loan losses using various techniques. There are generally three approaches that can be used to link the EDFs with macro-financial indicators: (i) the Vector Autoregression (VAR) framework, (ii) probit and logit models, and (iii) panel models.

Empirical Evidence

In Sun (2011), we present a new approach to identifying vulnerabilities in 45 SIFIs. The 45 SIFIs have been selected on the basis of their systemic importance in terms of size, business scope, and possible regional/global impact, though proving this is beyond the reach of this paper. Intervened institutions are assumed to be those that have gone bankrupt, have received government capital injections or loans, have had assets purchased by government, have received official loans to facilitate a merger or acquisition. Central bank temporary liquidity injections are not considered to be a type of intervention.

Sun responds to these questions by: (i) investigating balance sheet data well beyond the widely-used FSIs, and trying to find more “good” indicators that capture the key features of SIFIs; and (ii) constructing a group of panel data models (pertaining to different scenarios), which link the measures of the EDFs to a set of domestic and global macroeconomic and financial variables. In particular, we use panel cointegration to test the long-run causal effect of some important indicators, such as leverage (e.g., debt to common equity), on the EDFs.

  • Mixed results were found regarding the balance sheet data to highlight those firms that proved to be vulnerable in the current financial crisis. Leverage ratios were the most reliable indicator, and ROA and business scope can also provide predictive power. However, capital-to-asset ratios (including risk-adjusted ratios), and nonperforming loan data proved to be of little predictive power. In the current crisis, key vulnerabilities were unanticipated due to off-balance-sheet exposures and lenders’ dependence on wholesale funding. Indeed, many “failed” institutions still met regulatory minimum capital requirements. In particular, caution should be taken to encourage banks to increase retained earnings when boosting capital.
  • Further econometric work using panel specifications and panel cointegration further strengthen the importance of some bank -specific indicators including leverage (e.g., debt to common equity ratio), ROA, and stock market performance indicators (equity prices) in driving the changes in EDFs.In addition, leverage has a long-run causal effect on the EDFs. This piece of evidence also suggests that measures to set up leverage constraints could pay significant dividends in restraining the rise in EDFs when designing a new regulatory framework. Once again, some indicators that are widely taken as important to strengthen SIFIs and push forward future financial reforms, such as capital ratios, do not provide a useful indication of the rising EDFs.
  • Price stability matters. As the panel specifications show, inflation can exert an influence on the EDFs. This further underscores the importance of maintaining price stability, which is vital not only for monetary stability but financial stability as well.
  • Global macroeconomic conditions also matter. There is evidence that global excess liquidity and the financial stress index are significantly associated with the EDFs. This appears to suggest that global SIFIs are highly vulnerable to changes in global conditions. This means that better macroeconomic and global policies help to achieve lower EDFs, and, as a consequence, less financial instability.

Policy implications

In sum, based on the sample of institutions examined, it would be useful to include on the regulatory radar screen indicators on leverage (e.g., debt to common equity ratio), liquidity, and business scope, since they could provide a starting point for a deeper analysis of vulnerable institutions. Also, the current center-stage focus on regulatory capital adequacy ratios may need to be redefined, especially if it can be shown that SIFIs were able to shift risks to off-balance sheet vehicles, which receive lower risk weights, and thus the risks on the balance sheet are under-representing those of the FI.

Although the analysis here has been partial and cursory, other studies have found similar issues with the application of FSIs, calling for further improvement in their collection and usage. On the other hand, for less sophisticated institutions and general financial sector analysis, the current FSIs are useful, since the ratios are the most readily available indicators to represent the SIFIs’ level of risk. Finally, it is not necessarily the case that even those variables that identify vulnerabilities can be used separately. We need to check their usefulness in a macro-financial framework by putting them together with other bank-specific variables, and macroeconomic and global conditions. This will be the task in Section IV.

Overall, the panel specification and cointegration approach appears to be a useful tool for analyzing plausible global macro-financial shock scenarios designed for financial sector stress-testing purposes. The empirical analysis highlights several factors that would account for the vulnerabilities in the systemically important SIFIs. The results discussed above and the policy challenges associated with them point to the need to enhance the bank-specific indicators of financial soundness and improve the regulatory framework with a view to reducing the vulnerabilities emanating from the macroeconomic and global environment.


European Central Bank, 2005, “Financial Stability Review June 2005” (Frankfurt: European Central Bank).

Hardy, D. and C. Pazarbaşioğlu, 1998, “Leading Indicators of Banking Crises: Was Asia Different?” IMF Working Paper 98/91 (Washington: International Monetary Fund).

Hermosillo-Gonzalez, Brenda, 1999, “Determinants of Ex-Ante Banking System Distress: A Macro-Micro Empirical Exploration of Some Recent Episodes”, IMF Working Paper 99/33, (Washington: International Monetary Fund).

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Hutchinson, M. M., 2002, “European Banking Distress and EMU: Institutional and

Macroeconomic Risks,” Scandinavian Journal of Economics, Vol. 104 (3),

pp. 365–389.

James B. Thomson, “On Systemically Important Financial Institutions and Progressive Systemic Mitigation” ,Policy Discussion Paper Number 27, August 2009,Federal Reserve Bank of Cleveland

Sun Tao, “Identifying Vulnerabilities in Systemically-Important Financial Institutions in a Macro-financial Linkages Framework”, IMF Working Paper, May 2011, WP/11/111.