Practical Session

 The main objectives of this section is to:  Understand the mechanics of conducting a stress test of the financial sector, including the assumptions and simplifications necessary to make a stress test feasible, and  Conduct a reasonable stress test of banks, based on the result of analysis in Parts I and II of the case study. 2

 This framework is implemented in a separate Excel spreadsheet called Bank Stress Tester 1.0.  The framework employs a top-down approach to stress testing.  Balance sheet and income statement data are provided for the 10 largest banks, which account for more than 90 percent of total banking assets.  The case study focuses on four main financial risk factors – interest rates, exchange rates, credit quality, and liquidity.  Once participants enter a set of macro shocks, these shocks are translated automatically into changes to bank balance sheets.  Changes in interest rates and exchange rates have a direct effect on banks’ balance sheets, and the sign and size of these changes depend on the characteristics of banks’ portfolios. 3

 In addition, changes in interest rates, exchange rates, GDP growth, and the unemployment rate have indirect effects on bank balance sheets as a result of a change in credit quality (e.g., a change in loan loss reserves).  Finally, the results of the stress test are summarized with graphs that: i. compare the post shock capital adequacy ratios (CAR) of various categories of banks with their CARs under the baseline (pre-shock) scenario; ii. present the amount of capital (as a percent of GDP) that will be necessary to recapitalize banks; and iii. assess the systemic importance of the test by illustrating the concentration of “bad assets” in the banking system, by bank category. 4

 The case study focuses on four indirect sources of changes in credit quality as a result of changes in macro variables – real GDP growth (%Δy), the unemployment rate (UR), the long-term interest rate (RL), and the growth rate of the exchange rate (%Δe).  One way to model credit risk is to simply regress an aggregate bank balance sheet ratio – usually NPL-to-total loans or LLR-to-total loans – on a set of macro variables.  A linear regression with the ratio of the dependent variable would result in non-normal errors. Instead, using a logit transformation of the ratio: logit (x) = x / (100 - x) 5

 IMF (2007b) reports such regressions for modelling loan loss reserves in a number of countries in Eastern and Central Europe. The regressions have the following logit specification: logit(LLR/TL) it = α 0 + ρ logit(LLR/TL) it-1 + α 1 % Δy it + α 2 UR it + α 3 R it + α 4 % Δe it + η it  where LLR is loan-loss reserves, TL is total loans, and η it is a white-noise error. 6

VariableCoefficient Loan-loss reserves/loans t ** (24.24) Real GDP growth t ** (-2.986) Unemployment rate t ** (3.655) Exchange rate change t (-1.432) Long-run interest rate t * (2.416) R-squared z-statistics in parentheses **p<0.01, *p<0.05, +p< ESTIMATES OF COEFFICIENTS  Note that some of the coefficients are insignificant, and others have the “wrong” sign. This could be the result of several factors.  First, this set of countries have had very little volatility in interest rates, exchange rates, and unemployment rates in recent years, making it difficult to capture the relationship between loan loss reserves and the macro environment.  In addition, banks and bank supervisors in different countries may have different responses to changes in the macro environment, while the panel regression is explicitly assuming that their responses are relatively the same. 7

1. The Excel workbook Bank Stress Tester 1.0 contains all information necessary to carry out various stress tests, and it is organized as follows:  “A. Data”: Contains balance sheet and income statement data for the 10 largest banks, as well as a number of financial soundness indicators. Data is provided for individual banks and for subtotals based on bank size  “B. Assumptions”: Assumptions for the stress test are entered in the sheet.  “C. GDP Shock”, “D. UR Shock”, “E. IR Shock”, and “F. ER Shock”: These worksheets calculate the direct and indirect effects of various shocks based on the structure of the banking system and shows the effects of individual shocks on bank capital and CARs (details of the calculations are presented in section F.)  “G. Scenario”: Summarizes the aggregate results of the full scenario. 2. Examine the sheets associated with calculating the effects of the various macro shocks.  What assumptions are being used?  What simplifications are being made to make the calculations? Are they reasonable?  What type of information would you need to improve the accuracy of the calculations? 8

3. Familiarize yourself with the “G. Scenario” worksheet. Note how the effects of the shocks are being aggregated. Why might this approach underestimate the aggregate effects of all shocks? 4. Once you are comfortable with the stress test framework, enter the changes in the macro variables for your stress test scenario derived earlier in the case study. (Make sure you keep a clean copy of the stress testing worksheet as your proceed!) 5. What are the effects of individual shocks on bank capital, risk-weighted assets, and CARs?  How many banks fall below the minimum CAR as a result of the shocks?  Are these banks small, medium, or large banks?  Do these results seem reasonable? Why or why not? 9

7. What are the aggregate effects of the stress test scenario on bank capital, risk-weighted assets, and CARs?  Do these results seem reasonable? Why or why not?  How would you assess the overall impact of your scenario?  Are you concerned about the stability of the banking system? If so, where is the focus of your concern? 8. Conduct a sensitivity analysis: How sensitive are your conclusions to your assumptions about the magnitude of various shocks?  How sensitive are your conclusions to other assumptions in the stress test framework? 10