Analysis of FSIs for Banking7

In most countries, banks form the core of the financial system and, thus, warrant close monitoring for indications of potential vulnerabilities. A range of quantitative indicators can be used to analyze the health and stability of the banking system, including financial soundness indicators (aggregated microprudential indicators), market-based indicators of financial conditions, structural indicators describing ownership and concentration pat­terns, and macroeconomic indicators. A range of qualitative information is also needed to assess the banking system, including the strength of the regulatory framework (which is based on assessments of the Basel Core Principles, or BCP), the functioning of the payment system, accounting and auditing standards, the legal infrastructure, the liquidity support arrangements, and the financial sector safety nets.

Banking sector FSIs discussed in chapter 2 cover capital adequacy, asset quality, man­agement soundness, earnings and profitability, liquidity, and sensitivity to market risk. An analysis of inter-linkages among those FSIs and their macroeconomic and institutional determinants, together with an assessment of their sensitivity to various shocks through stress tests, provide the basic building blocks of financial stability analysis.8

The linkages not only among the various groups of FSIs but also to other variables are derived from accounting and lending relationships within the financial sector and with other non-financial sectors. They also reflect institutional determinants, such as the key

parameters of the prudential framework. Topics studied in this area include, for example, determinants of asset quality, links between asset quality changes and capital, and deter­minants of profitability, all of which are discussed below.

One important topic of study involves determinants of asset quality. Asset quality is affected by the state of the business cycle, the corporate financial structure, and the level of real interest rates, which, together, influence the capacity for debt servicing. Therefore, in empirical work, FSIs of asset quality are typically regressed on various explanatory variables, such as corporate leverage, macroeconomic conditions, and interest rates. In some assessments, those types of regression estimates were based on panel data for banks in a country; in other cases, time series of aggregate data were used. As an example of cross-country time series regression, the IMF (2003c) estimated the relationship between corporate sector FSIs and banking sector asset quality FSIs on panel data compiled from large private databases for 47 countries over 10 years. It found that a 10 percentage point increase in corporate leverage was generally associated with a 1.8 percentage point rise in NPLs relative to total loans after one year. Also, a 1 percentage point rise in GDP growth resulted in a 2.6 percentage point decline in the NPLs-to-loans ratio, reflecting the fact that fewer corporations are likely to experience problems repaying loans during rapid growth.

Links between asset quality changes and capital are also studied. A deterioration in asset quality affects capital (and risk-weighted assets) through additional reserves that banks need to hold against the additional bad assets. The additional reserves reflect the rules in the country involving loan loss provisioning and the application of those rules in banking practice. Therefore, to model this link, one needs to understand well the pruden­tial and supervisory framework in the country in question, which is where the findings of the BCP assessments can be of great help. The link between asset quality (and other risk factors) and capital is typically studied in the context of stress tests (see appendix D on stress testing for references on this issue).

Another important topic of study involves the determinants of profitability. There is a large theoretical and empirical literature on the bank-level and country-level factors determining bank efficiency. This issue is further discussed in chapter 4.

Quantitative analysis of FSIs can be complemented with information from assessments of the effectiveness of financial sector supervision. BCP assessments9 provide a vast array of contextual information that can be useful in interpreting FSIs. First, they can clarify the definition of data being used to compile FSIs by, for example, indicating the quality of capital. Second, they can help establish the underlying cause of observed movements in FSIs when there are competing explanations, such as whether a fall in the capital ratio might be supervisory action rather than rapid balance-sheet expansion. Third, they provide information on risks, such as operational and legal risk that cannot be captured adequately using FSIs. Fourth, they provide information on how effective the banks’ risk management is and, thus, how effectively the banking system is likely to respond to the risk associated with particular values for FSIs. Finally, they indicate the responsiveness of the supervisory system to emerging financial sector problems, which reveals how quickly vulnerabilities identified by FSIs are likely to be corrected. A lack of compliance with many of the BCP would suggest that the banking sector vulnerabilities detected using FSIs may be more serious than in a financial system with good compliance. Assessments

of financial infrastructure—corporate governance, accounting and auditing, insolvency and creditor rights regimes, and systemic liquidity arrangements—can also help interpret the liquidity and solvency indicators.

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