IMF Working Paper No. 13/232
November 11, 2013
Summary: Rules of thumb can be useful in undertaking quick, robust, and readily interpretable bank stress tests. Such rules of thumb are proposed for the behavior of banks’ capital ratios and key drivers thereof—primarily credit losses, income, credit growth, and risk weights—in advanced and emerging economies, under more or less severe stress conditions. The proposed rules imply disproportionate responses to large shocks, and can be used to quantify the cyclical behaviour of capital ratios under various regulatory approaches.
Motivated by the usefulness of rules of thumb,
this paper concentrates on the formulation of rules of thumb for key factors affecting bank solvency, namely credit losses, pre-impairment income and credit growth during crises, and illustrates their use in the simulation of the evolution of capital ratios under stress. We thereby seek to provide answers to the following common questions in stress testing:
- How much do credit losses usually increase in case of a moderate, medium and severe macroeconomic downturn and/or financial stress event, e.g., if cumulative real GDP growth turns out to be, say, 4 or 8 percentage points below potential (or average or previous years') growth?
- How typically do other major factors that affect capital ratios, such as profitability, credit growth, and risk-weighted assets (RWA), react under these circumstances?
- Taking these considerations together, how does moderate, medium, or severe macro-financial stress translate into (a decrease in) bank capital, and thus, how much capital do banks need to cope with different levels of stress?
A variety of evidence is presented on the “average” pattern of behavior of financial aggregates relevant to solvency stress testing banks based in EMs [emerging market economies] and ACs [advanced economies], and, with some limitations, also for larger LIC banks. Table 10 provides an overview of some main results.
Typical levels of credit loss rates, pre-impairment income, and credit growth were estimated under moderate stress (a one-in-10/15-years shock), medium stress (worst-in-20-year), severe stress (a 1-in-40-years shock), and extreme stress (1-in-100 years). All three variables react in non-linear fashion to the severity of stress, which means that effects under severe conditions is manifold the effects under moderate conditions. Also, a substantial “tail” of poorly performing banks is likely to be much more affected than the median bank.
Comparing ACs on the one hand and EMs/LICs on the other, loss levels are found to be substantially higher in the latter, compensated for by higher returns. It was found that 1-in-20 year stress loss levels usually lead banks to report some net losses, especially in ECs, and thereby lose some capitalization (1 to 3 percentage points if they are under Basel I or the Basel II standardized approach), but only a macroeconomic crisis approaching severe intensity would normally bring down typical well-capitalized banks (unless there are other issues related to confidence and financial sector-generated sources of strain).
Further evidence is presented on macro-financial linkages, and specifically on defining rules of thumb of how a change in GDP growth triggers credit losses, income, and credit growth effects under different levels of stress. While such rough satellite models are more complex than the descriptive solvency rules, they allow the development of scenarios based on an explicit story. As such, the rules make allowance for national circumstances, such as the expected severity of shocks.
While the study has found general patterns, country-specific and/or bank-specific circumstances may differ widely from the average. Hence, the rules of thumb elaborated in this study serve as broad guidelines, particularly to understand benchmarks for worst-case scenarios, but do not fully substitute for detailed analysis when that is possible. The rules of thumb with explicit focus on macro-financial linkages cover only some of the main macroeconomic risk factors that may affect a banking system, namely those captured by GDP. It would be worthwhile to investigate whether analogous simple rules can be formulated that link specific elements of banks’ balance sheets and profitability to such other sources of vulnerability. Relevant macroeconomic variables could include (i) interest movements, including an overall shift in rates and a steepening or flattening of the yield curve. Effects are likely to depend crucially on how frequently rates on various assets and liabilities adjust; (ii) inflation and especially unexpected movements in the inflation rate. A rapid deceleration could strain borrowers’ ability to repay; (iii) exchange rate movement, especially where a large proportion of loans are denominated in foreign currency; and (iv) shocks affecting sectoral concentration of exposures or certain business lines.
The rules of thumb can be used to compute minimum levels of capitalization needed to withstand shocks of different severities—even those far from a country’s historical experience. Also, the regulatory approach used by banks matters: whether a bank adopts an IRB approach to estimating risk-weighted assets or relies on a standardized approach is shown to make a substantial difference to the magnitude and also the timing of when the effects of shocks are recognized, provided that banks’ risk models reflect changes in risk on a timely basis. Thus, the results are relevant to recent policy discussions centered on the robustness of regulatory capital ratios, especially on the computation of RWAs (e.g., BIS 2013, BCBS 2013, Haldane 2012, 2013) and the design of (countercyclical) capital buffers (e.g., Drehmann and other 2009). The results echo the call for (much) longer samples to be used in the calibration of models used for RWA computation and the “right” choice of the regulatory capital ratio (e.g., BCBS 2013, BIS 2013).