RUSSIAN FEDERATION FINANCIAL SECTOR ASSESSMENT PROGRAM September 2016 TECHNICAL NOTE STRESS TESTING Prepared By This Technical Note was prepared by IMF staff in the Monetary and Capital Markets context of the Financial Sector Assessment Program Department mission in the Russian Federation, led by Karl Habermeier. It contains technical analysis and detailed information underpinning the FSAP’s findings and recommendations. Further information on the FSAP can be found at http://www.imf.org/external/np/fsap/fssa.aspx RUSSIAN FEDERATION CONTENTS Glossary __________________________________________________________________________________________ 4 EXECUTIVE SUMMARY __________________________________________________________________________ 5 PERFORMANCE OF THE BANKING SECTOR ____________________________________________________ 6 STRESS TEST DESIGN____________________________________________________________________________ 8 TOP-DOWN SOLVENCY STRESS TESTS BY STAFF ______________________________________________ 9 A. Stress-Testing Methodology __________________________________________________________________ 10 B. Macroeconomic Scenarios ____________________________________________________________________ 10 C. Balance Sheet and Income Statement Growth Projections ____________________________________ 11 D. Risk-Weighted Assets and Asset Quality ______________________________________________________ 12 E. Results ________________________________________________________________________________________ 13 COMPARISON OF CBR AND STAFF TOP-DOWN RESULTS ___________________________________ 14 BOTTOM-UP STRESS TESTS____________________________________________________________________ 14 SINGLE-FACTOR STRESS TESTS ________________________________________________________________ 15 LIQUIDITY STRESS TESTS ______________________________________________________________________ 15 STRESS TESTING RECOMMENDATIONS_______________________________________________________ 16 FIGURES 1. Bank Capital and Liquidity, 2008–15 __________________________________________________________ 17 2. Bank Asset Quality, 2008–15 __________________________________________________________________ 18 3. Staff Stress Testing Framework ________________________________________________________________ 19 4. Macroeconomic Scenarios: Main Variables ____________________________________________________ 20 5. Oil, GDP Growth, FSX, Inflation, and Interest Rates ____________________________________________ 21 6. Stress Test Scenarios and Restructured Loans, 2013–16 _______________________________________ 22 7. Asset Quality Adjustment _____________________________________________________________________ 23 8. Stress Test Results (V-Shape Scenario) ________________________________________________________ 24 TABLES 1. Financial Soundness Indicators, 2013–16 ______________________________________________________ 25 2. Macroeconomic Scenarios: Projections 2016–20 ______________________________________________ 26 2 INTERNATIONAL MONETARY FUND RUSSIAN FEDERATION 3. Regression Results ____________________________________________________________________________ 27 4. Detailed Stress Test Results ___________________________________________________________________ 28 5. Banks’ Bottom-Up Results for 12 Banks in 2016 _______________________________________________ 29 6. CBR’s Top-Down Results for the Same 12 Banks in 2016 ______________________________________ 30 7. Results of Single-Factor Tests (at the end of 2015) ____________________________________________ 31 8. Assumptions of Liquidity Tests ________________________________________________________________ 32 APPENDIX I. Banking Sector Stress Testing Matrix (STeM) __________________________________________________ 33 INTERNATIONAL MONETARY FUND 3 RUSSIAN FEDERATION Glossary AQR Asset Quality Review BU Bottom-up CAR Capital adequacy ratio CBR Central Bank of the Russian Federation FSAP Financial Sector Assessment Program FSSA Financial System Stability Assessment FX Foreign exchange GDP Gross domestic product NCOs Net charge offs NII Net interest income NPLs Nonperforming Loans RF Reserve Fund RWAs Risk-weighted assets SIB Systemically important bank SOB State-owned commercial bank (bank with state shares) TD Top-down WEO World Economic Outlook 4 INTERNATIONAL MONETARY FUND RUSSIAN FEDERATION EXECUTIVE SUMMARY This note presents the methodology and results of stress tests of the financial sector carried out as part of the 2016 Financial Sector Assessment Program (FSAP) for the Russian Federation. 1 To provide a comprehensive assessment of the banking system, the stress testing exercise comprised several different tests: a top-down stress test performed by the Central Bank of Russia (CBR), a bottom-up stress test performed by banks, a separate top-down exercise that included asset quality adjustments performed by staff, single-factor tests performed by CBR, and liquidity tests performed by CBR. The stress tests focused on banks, reflecting the structure of the Russian financial sector, which is relatively small and bank-dominated. Bank assets amounted to 103 percent of the gross domestic product (GDP) at end-2015. Pension funds, insurance, and mutual funds have assets of 3.6, 2.0, and 1.7 percent of GDP, respectively. The stress tests showed that the banking system is likely to need additional capital. Even in the baseline scenario, certain banks will need new capital owing to low profitability and increasing credit losses. The required resources are higher in the stress scenarios, but remain manageable. If public funds are needed for recapitalization, there is sufficient fiscal space, provided that fiscal policy remains prudent. Stress tests should be supplemented by a granular and comprehensive review of banks’ asset portfolios to gauge capitalization needs. 1 This note was prepared as part of the 2016 FSAP (Financial Sector Assessment Program). Staff would like to thank CBR‘s supervisory stress testing team for the open exchange of methodologies and excellent discussions of tests and results. All stress tests were discussed and agreed by CBR and staff. The principal authors of this note are (in alphabetical order) Adrian Alter, Nazim Belhocine, Ricardo Cervantes, and Fabian Lipinsky under the guidance of Dale Gray. INTERNATIONAL MONETARY FUND 5 RUSSIAN FEDERATION PERFORMANCE OF THE BANKING SECTOR 1. The banking sector is heavily concentrated, and state ownership continues to be important. The largest 20 banks account for three quarters of system assets, while the top 10 banks extend about 70 percent of total lending. State-owned commercial banks, dominated by Sberbank and VTB Group, accounted for 60 percent of system assets at end-2015. Bank concentration measured by the Herfindahl-Hirschman Index is close to the European median. Banks’ business model relies mainly on credit intermediation. The majority of assets are loans (70 percent of banking assets), followed by securities (mostly in domestic government and corporate bonds), and interbank lending. Banks are mainly funded by deposits of non-financial corporations and individuals.2 2. Despite recent stress, reported capital remains adequate on average, and liquidity has improved (Figure 1 and Table 1). The capital adequacy ratio (CAR) of banks was broadly stable in 2015 at about 13 percent, thanks to the capital injection program and regulatory forbearance. After forbearance was reduced in early 2016, the CAR fell to 12 percent. Liquidity has improved, with the loan-to-deposit ratio decreasing to 115 percent by end-2015 from its recent peak of 125 percent, reflecting increased retail deposits, falling credit growth, and government spending out of the Reserve Fund (RF). However, there is considerable dispersion around these averages, as reflected in the closures of many, mainly small, banks. 3. Loan portfolio quality and profitability have deteriorated (Figure 2). Nonperforming loans (NPLs) have increased, with household overdue loans reaching 8.4 percent of total loans by February 2016, compared to 6.5 percent for the corporate sector. Overall NPLs were stable in May– December 2015, reflecting loan rescheduling and regulatory forbearance. Bank profitability has dropped markedly—with the return on assets reaching 0.3 percent at end-2015—to levels similar to those observed during the global financial crisis. Several factors explain these developments. On the revenue side, net interest margins have contracted, reflecting slower asset growth and higher policy rates. In addition, net fees and commissions fell in line with net interest income (NII). On the expenditure side, non-interest expenses declined at a lower rate than NII, while provisions have risen sharply owing to the deterioration in loan portfolios. 4. Connected lending and loan concentration continue to be of concern, with possible implications for asset quality. Large exposures stood at 261 percent of capital in February 2016. The reported numbers may, however, understate the extent of related party lending: a narrow definition of related parties and connected relationships, coupled with weak implementation of the concept of beneficial owner, prevents CBR from linking all exposures. Stricter rules were published in 2014 but implementation has been postponed twice until January 2017.3 2For further information on banks’ assets and liabilities, please see “Russian Federation: Financial System Stability Assessment,” IMF Country Report No. 16/231, July 2016. 3 Related party lending from banks to parties owned by the lending banks’ shareholders is of more concern than lending from state-owned banks to state-owned enterprises, which have typically a higher credit rating. 6 INTERNATIONAL MONETARY FUND RUSSIAN FEDERATION 5. Performance across the system is uneven and medium-sized banks appear particularly vulnerable. Banks in the 21–50 segment, by asset size, show the weakest performance, with a negative return on equity of about 25 percent in 2015Q4. These banks did not benefit from the capital injection program and were particularly exposed to underperforming unsecured consumer lending. Two banks in this segment are currently undergoing resolution. Profits of the whole system remained slightly positive owing to the performance of a few of the largest banks. 6. Banks’ net foreign exchange (FX) exposures appear to be within prudent limits. Banks are adhering to regulatory limits on net open FX positions, with a total overall limit of 20 percent of capital. The net foreign asset position of banks has continued to improve and reached US$100 billion at end-2015, reflecting foreign deposit withdrawals and deleveraging following the imposition of sanctions. 7. Cross-border bank exposures are declining. Based on Bank for International Settlements (BIS) data, the exposure of foreign banks to Russia has halved since the onset of the geopolitical tensions in 2014 to about US$110 billion by end-2015. Forty percent of foreign bank claims are concentrated in France and Italy (US$26 and US$19 billion). Russian international investment position data suggests a cross-border exposure of Russian banks of about US$245 billion at end-2015 (below 20 percent of GDP), of which two thirds are loans and deposits. Russian banks’ subsidiaries operate primarily in Austria, Turkey, and Cyprus.4 8. The banking sector—while currently stable—is exposed to significant risks, asset quality being the largest. Even in the absence of a further macroeconomic deterioration, credit losses could be significant. Unsecured consumer lending in 2011–14 has already led to an increase in NPLs in the retail sector. The increase in NPLs in the corporate sector has been smaller thus far, but overdue loans have been rising in various industries, in particular in construction and real estate, where contracts were denominated in FX and were not rolled over when the exchange rate depreciated. Other sectors experiencing increased credit risks include mining, trade, and agriculture, which suffered from low domestic demand and a slowdown in government spending, and also pre- existing weaknesses in the case of mining. 9. There is considerable uncertainty about the strength of loan portfolios. CBR inspections of asset quality have revealed violations, including lending to shell companies, overvaluation of collateral, misreporting, and unreliable financial statements. Nonperforming assets may thus be higher than reported, reflecting: (i) lower quality of restructured loans; (ii) potentially under- provisioned and under-collateralized portfolios; and (iii) transfer of distressed assets to affiliated off- balance sheet entities that are not subject to consolidated supervision. 10. At a system-wide level, liquidity risk is contained, but individual vulnerabilities remain. The short-term focus and segmented nature of the money market increases the vulnerability of the 4The exposure of Russian banks to Ukraine, including interbank loans and loans to individuals and firms, was estimated at 1.1 percent of Russia’s GDP in January 2016. INTERNATIONAL MONETARY FUND 7 RUSSIAN FEDERATION banking system to liquidity shocks going forward.5 Large banks hold sizeable stocks of high-quality assets and have access to unsecured funding. Lower-rated small and medium-sized banks do not have access to the same low-cost sources of funding, and instead rely on higher yielding collateral, short-term secured markets, and CBR. These banks are vulnerable to liquidity dry-ups. 11. Market risk, including sovereign risk, appears contained, but data limitations make the assessment difficult. Securities portfolios account for 15 percent of total assets at end-2015, and consist mainly of debt securities, but also include small shares of equities and minority interest holdings. While direct sovereign risk is small (sovereign securities constitute only a quarter of debt security portfolios), indirect sovereign risk arising from the state-owned financial and non-financial corporate sectors is likely greater, given that a large share of the banking sector and many large corporations are state-owned.6 STRESS TEST DESIGN 12. Several solvency stress tests were performed—top-down by CBR, bottom-up by banks, and top-down by staff (Appendix I). The top down (TD) tests had a horizon of five years, while the bottom up (BU) test had a horizon of one year. The resilience of the system was assessed relative to the regulatory capital adequacy minimums: 4 ½ percent for Common Equity Tier 1 and 8 percent for the total CAR.7 The stress tests covered a wide range of risks, including credit, market, interest rate, and liquidity:  Asset quality, income, and capital of individual banks were modeled under three macroeconomic scenarios: baseline, V-shaped, and L-shaped. This allows for a bank-by-bank assessment of possible needs for additional capital.  The TD solvency stress test performed by CBR covered essentially all of the banks in the system (681 in total) and were based on Russian accounting standards, on a stand-alone basis.8  The BU stress test covered 12 banks, including the 10 SIBs, accounting for two thirds of system assets. 5During late 2014, the significant fall in oil prices and the flight from ruble denominated assets led to large margin calls on the secured interbank market and the emergence of collateral constraints. As a result, market rates were pushed through CBR’s interest rate corridor ceiling, and the yield curve became inverted for two–three days. 6 A comprehensive analysis of sovereign risks was not undertaken given that supervisory data were not available. 7Russian banks will be required to report capital ratios according to Basel III, which includes a phased-in capital conservation buffer and a systemic surcharge for D-SIBs. These buffers are, however, are intended to be released during periods of stress and were not activated in 2015, when GDP declined by 3.7 percent. In view of the more severe adverse scenarios and the slow recovery in the baseline scenario, CBR and the FSAP team agreed to use hurdle rates based on current requirements. 8 The cut-off date for the data used by CBR and staff was December 31, 2015. 8 INTERNATIONAL MONETARY FUND RUSSIAN FEDERATION  The TD stress test performed by staff used publicly available data for 37 banks accounting for about 82 percent of system assets, and covered credit and interest rate risk. The data captures consolidated banking groups and is based on International Financial Reporting Standards (IFRS), which somewhat limits the impact of regulatory forbearance.9  To allow the quantification of a full shock-to-recovery cycle, the top-down tests were calibrated over a horizon of five years. The horizon of the bottom-up tests was one year.  Staff’s analysis focused on credit and interest rate risks affecting net interest income, which is consistent with the fact that the main function of Russian banks is to intermediate savings and extend loans. Credit risk also covered deterioration in the loan portfolio due to depreciation (notably FX risk). In addition, the TD by CBR and BU stress tests also covered market and liquidity risks. 13. Adjustments were made to provisioning levels in the staff’s TD stress test. Staff estimated the effect on capital adequacy of weak restructured loans and under-provisioning using aggregated data from CBR and information from market participants. First, staff estimated the impact of the migration of a certain part of restructured loans into lower loan categories associated with higher provisioning rates. While restructured loans stand at about 30 percent of large loans, one third of the restructured loans were estimated to be of weaker quality at end-2015. The migration of such loans resulted in an increase of loan impairment charges by 1 percentage point of total loans. Second, staff accounted for under-provisioning in each loan category, with the loan impairment charges increasing by an additional 2.2 percentage points. The overall effect of restructuring and under-provisioning was thus assessed to be about 3.3 percentage points. 14. In addition, CBR carried out single-factor tests and system-wide liquidity stress tests. The liquidity stress test included three scenarios (mild, severe, very severe) with a test horizon of one month. The scenarios entail increasingly large outflows from most liability categories and increasingly high discounts on non-liquid asset categories. The scenarios assume no CBR or interbank financing. The liquidity stress tests assess the extent to which liquidity outflows exceed the available liquid assets. TOP-DOWN SOLVENCY STRESS TESTS BY STAFF 15. This section explains staff’s TD solvency stress tests. It covers: (i) the stress-testing methodology; (ii) the macroeconomic scenarios; (iii) the models used to map the macroeconomic scenarios into credit losses, income statement projections, and balance sheet items; (iv) other assumptions governing regulatory capital during stress test scenarios, including changes in risk- weighted assets (RWAs) and changes in provisions resulting from asset quality adjustments; and (v) the results of the stress tests. 9 IFRS provide less discretion in applying forbearance. Consequently, using IFRS entails a stricter loan classification. INTERNATIONAL MONETARY FUND 9 RUSSIAN FEDERATION A. Stress-Testing Methodology 16. Staff’s top-down stress testing methodology applied panel regression techniques similar to those used in advanced economy FSAPs (Figure 3).10 In the case of Russia, consideration needed to be given to higher volatility in the exchange rate, inflation, and interest rates. Consequently, the model was designed to accurately capture the effects of substantial exchange rate depreciation and increases in inflation transmitted through adverse changes in net interest income and loan impairment charges (due to increasing interest rates). 17. The model focused on loan impairment charges to net income. This has the advantage that differences in loan categorization and in accounting for losses do not influence the stress tests. Loans are classified into five categories, with loans in categories IV and V defined as NPLs. However, provisions and charges to the income statement are also necessary for loans in categories I to III. Focusing on NPLs could thus understate provisions and charges to the income statement. Moreover, loan categorization and write-offs may be handled differently in different countries, and hence distort stress testing results. Focusing on loan impairment charges incorporates the combined effect of changes in probabilities of default and loss given default. B. Macroeconomic Scenarios 18. The macroeconomic stress scenarios quantified the impact of negative oil price shocks calibrated to tail events in the oil price probability distribution (Table 2, Figures 4 and 5). These shocks, in turn, were calibrated to tail events of the oil price probability distribution. The authorities and the FSAP team agreed on three macroeconomic scenarios.11 In the V-shaped scenario, oil prices fall to US$19 per barrel, while in the L-shaped scenario, they fall to US$25 per barrel but recover more slowly. These tail events have strong adverse effects on GDP and unemployment. They also entail further large exchange rate depreciation, in view of the relationship between oil prices and RUB/US$ rate. Inflation and short-term interest rates respond strongly to the exchange rate depreciation. In the medium term, the scenarios are conservative. The Brent oil price recovers only moderately to US$40 and US$37 in 2020 in the V-shaped and L-shaped stress scenarios, and annual real GDP growth converges to one percent in both scenarios, half a percentage point below 2020 baseline growth. 19. Under the stress scenarios, banks’ profitability suffers, reflecting deterioration in asset quality and lower interest margins. Lower corporate and household incomes result in worsening of the quality of domestic loan portfolios, with ruble-denominated consumer and corporate loans hit the hardest. Exchange rate depreciation leads to weaker performance of portfolios in foreign currency with very low provisioning.12 In addition, exchange rate depreciation triggers higher domestic inflation, which in turn is reflected in higher interest rates. As a result of higher funding 10 Such as Switzerland and the United States. 11 The baseline scenario followed closely the projections from IMF’s WEO as of end of 2015. 12 Generally, FX loans are mostly extended to the largest and strongest corporations in Russia. Consequently, provisioning rates are lower and NPLs tend to increase less than for ruble denominated loans. 10 INTERNATIONAL MONETARY FUND RUSSIAN FEDERATION costs and the inability to fully adjust the interest rate on the asset side owing to maturity mismatch, banks’ net interest margin shrinks in the short term. By the end of the five-year horizon, net interest margins recover, while loan loss provisions moderate. C. Balance Sheet and Income Statement Growth Projections 20. Bank finances were projected conditional on the macroeconomic scenarios (Figure 6). Over the five-year stress testing horizon, balance sheet and net income items were projected based on the statistical relationships with macroeconomic indicators, bank specific characteristics, and other assumptions. In each scenario, these projections helped to calculate bank-specific capital dynamics and capital ratios. 21. Statistical relationships were estimated for each balance sheet and income statement item necessary to project bank capital ratios (Table 3).13 The determinants in each relationship were selected from a pool of macrofinancial indicators combined with bank-level characteristics. Several specifications were considered for each dependent variable, drawing on economic theory and the literature.14 Balance sheet growth projections 22. The growth rate of gross loans is estimated using a panel data regression with bank fixed effects. It is assumed that higher economic activity and the interaction between the GDP growth rate and the type of bank would affect these dynamics.15 The growth rate of assets is assumed to be affected by the growth rate in gross loans, which is the most important asset category for the Russian banks. 23. Net charge offs (NCOs) are estimated as a share of gross loans. Intuitively, the dynamics of NCOs are modeled based on the negative relationship with real output, house prices, and loan growth. In contrast, higher interest rates and the term premium are found to be positively associated with NCOs. Income statement growth projections 24. Net income is disaggregated into six components: net interest income, trading income, non-interest expenses, net fees and commissions, other net income and provisions. Net interest income depends on interest rates and the growth in loans and deposits.16 By contrast, loan provision growth is negatively associated with real output growth and the term premium, while 13 The statistical relationships were estimated over the sample period 2005Q1 to 2015Q3. 14 For robustness purposes, different definitions of the dependent variables were considered. For example, the results of quarterly and annual (yoy) growth rates or ratios to other items (such as percent of gross loans) were compared. The best regression models were chosen using several criteria: economic interpretation and significance, relatively higher R2 in comparison to other models, and parsimony. Moreover, deviations from the mean point estimates were used to check robustness. 15 To accommodate the persistence of gross loans, the lagged dependent variable is included in the specification. 16 Loans and deposits are assumed to grow at the same rate. INTERNATIONAL MONETARY FUND 11 RUSSIAN FEDERATION an increase in the unemployment rate amplifies provisions growth. Risks stemming from lending in foreign currency and the impact of depreciation are captured by the term premium. When the exchange rate depreciates, inflation and short-term interest rates tend to increase, resulting in a decline in the term premium and an increase in loan impairment charges. Alternative specifications with the exchange rate in the regression resulted in a lower model fit overall. 25. Trading income increases during crises, but the fitted relationship is not very strong. Trading income growth is associated with a higher term premium and real estate prices. Other income items such as non-interest expense (y-o-y growth), net fees and commissions (y-o-y growth) are modeled in a simplified way in relation to total asset growth. Lastly, other net income (percent of total assets) is negatively driven by the term premium and real estate prices (y-o-y growth).17 D. Risk-Weighted Assets and Asset Quality 26. As Russian banks determine risk weights according to the standardized approach, risk weights were assumed to be constant, at two different levels: 90 percent and 100 percent. The value of 90 percent is in line with the average risk weight observed at the end of 2015. However, both theory and the empirical literature indicate that risk weights increase during crises.18 Thus, the stress tests were also run with an increased risk weight of 100 percent. In line with the observed data, dividend payouts (in case profits are positive) and taxes were fixed at 20 percent over the stress testing horizon. 27. The potential overestimation of capital ratios was addressed by adjusting for the estimated effects of restructured bad loans and under-provisioning (Figure 7). Staff estimated the extent to which restructured bad loans and under-provisioning affect capital adequacy, using aggregated data from CBR and information obtained during meetings with market participants and CBR.19 First, staff estimated the impact of migration of a certain part of restructured loans into lower loan categories associated with higher provisioning rates. While restructured loans stand at about 31 percent of large loans, one-third of the restructured loans were estimated to be of weaker loan quality at the end of 2015.20 The migration of such loans resulted in an increase of provisions by about 1 percentage point in total. Second, staff also accounted for under-provisioning in each loan category. As a result, provisions increase by an additional 2.2 percentage points. The total effect of restructuring and under-provisioning on provisions was assessed to be about 3.3 percentage points. 17 Owing to the very poor fit of the regression, trading income is assumed to be constant over the stress test horizon. Similarly, the residual component (other net income) is assumed to be constant. 18 For details, see for example Hardy, D., and C. Schmieder. “Rules of Thumb for Bank Solvency Stress Testing.” WP 13−232, International Monetary Fund, 2013. 19 Staff assessed asset quality conservatively, taking the higher end of reported weak restructured loans, and conservative provisioning rates and collateral values based on discussions with market participants and CBR. 20 The evolution of restructured loans by bank type is depicted in Figure 6 (bottom panel). 12 INTERNATIONAL MONETARY FUND RUSSIAN FEDERATION E. Results 28. Two sets of results are presented. The first takes as its point of departure reported bank capital ratios at the end of Q3 2015, with no adjustments for regulatory forbearance, and risk weights averaging 90 percent. In the second set, by contrast, an adjustment is made to initial bank capital ratios for under-provisioning and restructuring of bad loans. In addition, risk weights average 100 instead of 90 percent to account for deteriorating asset quality under stress. 29. The first set of results shows an estimated capital deficit of about ½ and 2 ½ percent of GDP in the baseline and stress scenarios respectively (Table 4). CBR and staff estimated an aggregate capital deficit—defined as the peak deficit during the five-year stress test horizon—of 0.3 and 0.4 percent of GDP, respectively, in the baseline scenario. The deficit increases in the stress scenarios compared to the baseline by about 2 percentage points. CBR estimated a deficit of 2.5 and 2.1 percent of GDP in the V-shape and L-shape scenarios, respectively. Staff estimated deficits of 2.4 and 2.7 percent of GDP in the V-shape and L-shape scenarios. 30. The more conservative second set of staff results shows higher capital deficits of about 1 percent and 4 ½ percent of GDP in the baseline and in the stress scenarios, respectively. Including the effects of restructuring and under-provisioning increases the capital deficit by 0.6 percentage points in the baseline scenario and by about 2 percentage points in the stress scenarios. Staff estimated capital deficits of 4.6 and 4.4 percent of GDP in the V-shape and L-shape scenarios respectively. While the magnitude of deficits is similar in both scenarios, peak level deficits occur in different years—2017 in the V-shape scenario, and 2019 in the L-shape scenario. 31. The detailed results for the V-shape scenario show that the system could face serious challenges (Figure 8). In the results including asset quality adjustment, banks with capital ratio breaches account for 87 percent of banking sector assets in 2017. The median CET1 capital and total capital ratios fall to 0.8 percentage point and 3.5 percentage points, respectively, in 2017. The results without the asset quality adjustment are less severe. 32. The detailed results for the L-shape scenario show a similar picture. With the asset quality adjustment, cumulative capital deficit accounts for 4.4 percent of GDP in the L-shape scenario, and the median total capital ratio bottoms out at 7.8 percent of RWA in 2018. 33. There are capital shortfalls even in the baseline scenario, when asset quality adjustments are taken into account. Minimum regulatory capital requirements are breached by 15 out of 37 banks in 2017 accounting for about 37 percent of banking sector assets. This said, the median CET1 capital and total capital ratios are 5.5 and 9.9 percent, respectively, in 2017, above regulatory minima. 34. The stress test results indicate that some large banks as well as medium- and small- sized banks could face difficulties under the stress scenarios. According to staff’s stress tests, some large state-owned, private, and foreign banks breach capital ratios under the stress scenarios (with and without the asset quality adjustment), pointing to relatively weaker banks in each INTERNATIONAL MONETARY FUND 13 RUSSIAN FEDERATION category.21 In the medium term, staff estimates imply that some weak banks, accounting for about 40 percent of banking sector assets, have difficulties returning to profitability, owing to low capital levels and profitability at the end of 2015, as well as moderate medium-term GDP growth projections. According to CBR, weak large banks recover in the medium term.22 However, some medium- and small-sized banks (accounting for about 5 percent of banking system asset and not included in staff’s sample) accumulate large capital deficits by 2020. 35. These results are mitigated by the small size of the banking system and sound macroeconomic policies. Although the cumulative capital deficits are not negligible, the estimated economic costs of recapitalization are manageable, given the relatively small size of the Russian banking sector relative to GDP, low government debt, high FX reserves, and sound monetary policy. COMPARISON OF CBR AND STAFF TOP-DOWN RESULTS 36. The corresponding CBR and staff top-down stress tests show similar capital deficits (Figure 8). The panel data regression techniques used are broadly similar. While staff projects banks’ asset and equity position (liabilities are a residual), CBR has developed a suite of models that projects liabilities and equity (with asset projections being the residual). Credit losses peak in the first year in the staff’s stress tests, while the CBR shows them peaking in the second year (owing to a lagged effect). The dynamics of capital ratios and capital deficits are very similar. Capital ratios bottom in 2017 and recover. Estimated capital deficits are also similar. BOTTOM-UP STRESS TESTS 37. CBR’s top-down analysis is more conservative than banks’ own assessment (Tables 5 and 6). In the baseline scenario, banks’ bottom-up results show that one bank would have a capital deficit of RUB 3 billion, whereas the top-down results show one bank with a capital deficit of RUB 65 billion. In the V-shaped scenario, bottom-up results show six banks with capital deficits of RUB 199 billion, while the top-down results show seven banks with capital deficits of RUB 522 billion—2.6 times higher. In the L-shape scenario, bottom-up results show two banks with capital deficits of RUB 70 billion, while the top-down results show six banks with capital deficits totaling RUB 426 billion—6 times higher. Along with credit risk, market risk in particular seems to be underestimated by banks. 21 The bulk of the reported capital deficits arise at state-owned banks, which is not surprising given that they are by far the largest banks. 22 The fast pace of recovery in CBR’s methodology is due to different sensitivities of net interest income and expenses with respect to macroeconomic variables in CBR’s and staff’s satellite models. 14 INTERNATIONAL MONETARY FUND RUSSIAN FEDERATION SINGLE-FACTOR STRESS TESTS 38. The CBR’s single factor stress tests consisted of one credit risk scenario and four market risk scenarios (Table 7).  The credit risk scenario assumes default of each bank’s top five (non-bank) borrowers. Results show that 338 banks with 82.5 percent of banking system assets experience an aggregate capital deficit of RUB 1,519 billion.  Market risk scenarios. An exchange rate depreciation of 20 percent shows that five banks comprising 0.1 percent of system assets would have a capital deficit of RUB 0.2 billion. A 30 percent decline in the value of equity holdings shows that seven banks comprising 0.5 percent of system assets would have a capital deficit of RUB 0.9 billion. Two interest rate shocks to banks’ trading portfolio were also considered. The first assumes an increase of 1,000 basis points in corporate sector spreads and an increase of 400 basis point in government bond spreads. In this case, 13 banks comprising 6.7 percent of system assets would have an aggregate capital deficit of RUB 20 billion. If only corporate sector spreads were to increase by 1,000 basis points (with no increase in government bond spreads), four banks comprising 1 percent of system assets would have an aggregate capital deficit of RUB 6.7 billion. 39. Single factor tests related to credit risk point to a significant concentration risk in lending. Other single factor tests of market risk showed relatively minor effects, owing to the small size of securities portfolios. LIQUIDITY STRESS TESTS 40. CBR carried out liquidity stress tests for the banking system as a whole (Table 8). A key assumption was that banks would not have access to CBR financing or interbank inflows. Three liquidity scenarios were considered: mild, severe, and very severe. The mild and severe scenarios were calibrated based on historical outflow rates during periods of stress in Russia. The very severe scenario covers additional stress, mainly in the interbank market, as seen in some advanced and emerging economies. 41. The results of CBR’s liquidity stress tests show minor liquidity deficits for the mild scenario and substantial but manageable liquidity deficits for the strict and very strict scenarios. Under the mild scenario, only 36 banks (1.7 percent of system assets) show liquidity deficits, with a total shortage of less than 0.1 percent of GDP. These indicators increase to 111 banks (11 percent of system assets) with a shortage of 0.4 percent of GDP in the strict scenario and 181 banks (41.8 percent of system assets) with a shortage of 2.9 percent of GDP in the very strict scenario. 42. The three largest state-owned banks have a special position regarding liquidity. Specifically, they are significant players in the corporate (state-related) and retail deposit markets, and along with other large banks, hold large stocks of high quality assets and have access to INTERNATIONAL MONETARY FUND 15 RUSSIAN FEDERATION unsecured funding. (This said, sanctions continue to restrict the access of large banks to term external funding.) Lower-rated small and medium sized banks do not have access to the same low- cost sources of funding, and instead rely on higher yielding collateral, short-term secured markets, and CBR. STRESS TESTING RECOMMENDATIONS 43. CBR has significantly upgraded its stress testing practices in recent years, but some further improvements are possible. Key priorities are: (i) implementation of modern, comprehensive, and integrated TD and BU methodologies covering not just banks but also affiliated entities; (ii) development of an “expected loss approach” for credit risk using probability of default, loss given default, and exposure at default; (iii) development of a credit registry, also to provide additional data; (iv) expanded use of the results of TD bank-by-bank stress tests in the bank supervision process, including for capital planning and dividend policy; (v) possible publication of aggregate TD stress test results in the Financial Stability Review; and (vi) enhanced system-wide stress tests for all banks, beginning with the large and medium-sized banks, using macro scenarios and key bank stress test parameter assumptions over a three-year horizon. 44. It would be advisable to also conduct stress tests on a consolidated basis, and liquidity stress test by currency. The current stress tests on a solo basis may miss downstreaming of problematic assets by D-SIBs (which are subject to stricter regulatory requirements) to unconsolidated subsidiaries. Similarly, liquidity stress tests based on aggregate asset and liability categories may overlook liquidity shortages in particular currencies. 45. An asset quality review is recommended to assess risks stemming from uncertainty about asset quality, concentrated lending, and related party lending. Stress tests should be supplemented by a granular and comprehensive review of banks’ asset portfolios to gauge capitalization needs.23 23 The Bank of Russia has already begun to implement the recommendations of FSAP mission: as part of banking supervision improvement the Risk Analysis Service has been organized. The Service is expected to assess assets and transactions of credit institutions on ongoing basis. 16 INTERNATIONAL MONETARY FUND RUSSIAN FEDERATION Figure 1. Bank Capital and Liquidity, 2008–15 … and the use of regulatory forbearance to shield risk weighted Capital ratios remained stable due to capital injection … assets from the impact of the depreciation. Capital Adequacy Equity to Total Assets of Russian Banks (In percent) (In percent) 15 25 Capital to risk-weighted assets 14 20 Core capital to risk-weighted assets 13 12 15 11 10 10 CAR Min. Req. 9 5 Core Capital Min. Req. 8 Apr-08 Jul-09 Oct-10 Jan-12 Apr-13 Jul-14 Oct-15 0 Jan-11 Apr-12 Jul-13 Oct-14 Feb-16 Liquidity dried up following a strong outflow of retail deposits in …which was compensated by CBR stepping in to support liquidity December 2014… of the banking system. Retail Deposits Main Funding Sources (Percent change, M-o-m, SA) (In percent of total liabilities) 6 68 16 Saving accounts 66 14 Funds received from CBR, rhs 4 64 12 62 10 2 60 8 58 0 6 56 54 4 -2 52 2 50 0 -4 Apr-08 Jul-09 Oct-10 Jan-12 Apr-13 Jul-14 Oct-15 -6 Jan-08 Aug-09 Mar-11 Oct-12 May-14 Jan-16 Since then, confidence returned and the liquidity situation has … amid declining loan-to-deposit ratios. improved …… Liquidity Assets to Total Assets Loan-to-Deposit Ratio (In percent) (In percent) 30 200 180 Total Ruble 25 160 FX 140 20 120 100 80 15 Feb-07 Nov-08 Sep-10 Jul-12 Apr-14 Feb-16 Jan-11 Apr-12 Jul-13 Oct-14 Feb-16 Sources: Central Bank of Russia and IMF staff calculations. INTERNATIONAL MONETARY FUND 17 RUSSIAN FEDERATION Figure 2. Bank Asset Quality, 2008–15 … although provisions are higher once FX loans, loans to NPLs remain lower than their 2008 peak … FIs and governmental institutions are excluded. Overdue Loans and NPLs Provisions (In percent of total loans) (In Russian ruble denominated loans; in percent of total loans) 10 20 9 18 Loans to corporates Mortgage loans 8 16 Consumer loans 7 14 6 12 5 10 4 8 3 6 Households 2 Corporate 4 1 NPLs 2 0 0 Jan-08 Aug-09 Mar-11 Nov-12 Jun-14 Feb-16 Apr-09 Apr-10 Apr-11 Apr-12 Apr-13 Apr-14 Apr-15 Overdue loans in rubles have increased primarily in the … while overdue loans in FX have risen primarily in the construction and retail trade sectors… real estate, agriculture, and mining sectors. Ruble Overdue Loans to Total Loans by Sector Foreign Exchange Overdue Loans to Total Loans (In percent, by sector) 10 6 Other Real estate Other 9 Transport and communications Agriculture, hunting and forestry Wholesale and retail trade Electricity, gas and water Manufacturing 5 8 Transport and communications Mining and quarrying Wholesale and retail trade Construction Construction 7 Electricity, gas and water 4 Manufacturing 6 Agriculture, hunting and forestry 5 3 Mining and quarrying Real estate 4 2 3 2 1 1 0 0 Dec-09 Dec-12 Dec-15 Jun-11 Jun-14 Mar-09 Mar-12 Mar-15 Sep-10 Sep-13 Dec-09 Dec-12 Dec-15 Jun-11 Jun-14 Mar-09 Mar-12 Mar-15 Sep-10 Sep-13 … while loans in FX show weak performance in agriculture, Nearly 25 percent of the construction portfolio is overdue… mining, and machinery and equipment. Ruble Overdue Loans by Portfolio Foreign Exchange Overdue Loans by Portfolio (In percent) (In percent) 30 30 Wood products Mineral products Paper products Machinery and equipment 25 Construction 25 Agriculture, hunting and forestry Average Average 20 20 15 15 10 10 5 5 0 0 Dec-09 Dec-12 Dec-15 Jun-11 Jun-14 Mar-09 Mar-12 Mar-15 Sep-10 Sep-13 Dec-09 Dec-12 Dec-15 Jun-11 Jun-14 Mar-09 Mar-12 Mar-15 Sep-10 Sep-13 Sources: Central Bank of Russia and IMF staff calculations. 18 INTERNATIONAL MONETARY FUND RUSSIAN FEDERATION Figure 3. Staff Stress Testing Framework Stress Testing Methodology PROJECTIONS DATA Other assumptions: Risk weights, Asset Quality Adjustment, Historical data on macroeconomic, Dividend payout. financial variables, individual bank data on Balance Sheet and Income statement ESTIMATION Items. Estimate relashionships between Income Statement and Balance sheet Project Balance items (dependent variables) and Sheet (or use Common Equity macroeconomic, financial variables and assumptions of Capital ratio. MODELS/ASSUMPTIONS bank specific variables. balance sheet dynamics). Panel regression models Constant shares Project Income Statements. Macroeconomic Scenarios: Baseline, V-shape adverse and L-shape adverse Income Statement Mapping IMF Bankscope Net interest income Net interest income = Interest Income – Interest Expense - Loan impairment charge Loan impairment charge Securities Income Securities Income (Return on Securities ) = Sum of 1. Net Gains (Losses) on Trading and Derivatives 2. Net Gains (Losses) on Other Securities 3. Net Gains (Losses) on Assets at FV through Income Statement 4. Change in Value of AFS Investments (part of Fitch Comprehensive Income) Return on Securities = Securities Income/Securities (Balance Sheet) - Net Expenses Net Expenses = Expenses – Fee Income Expenses = Sum of 1. Personnel Expenses 2. Other operating Expenses Fee Income = Sum of 1. Net Fees and Commission 2. Other Operating Income Other Income All other income items except ‘Tax expense’ and ‘Currency Translation Differences’ Currency Translation Differences Currency Translation Differences - Tax Expense Tax expense Comprehensive Net Income Fitch Comprehensive Income Source: IMF staff estimates. INTERNATIONAL MONETARY FUND 19 RUSSIAN FEDERATION Figure 4. Macroeconomic Scenarios: Main Variables Staff designed three scenarios… …based on option implied oil price scenarios. Brent Brent (Level) (Quarter-on-quarter percentage change) 140 50 40 120 Brent (level) Baseline 30 100 V-Adverse 20 L-Adverse 10 80 0 -10 60 -20 40 -30 Brent Baseline -40 20 V-Adverse -50 L-Adverse 0 -60 1995Q2 1998Q2 1999Q4 2001Q2 2004Q2 2005Q4 2007Q2 2008Q4 2011Q4 2013Q2 2014Q4 2016Q2 2019Q2 2020Q4 1996Q4 2002Q4 2010Q2 2017Q4 1995Q2 1996Q4 1998Q2 1999Q4 2004Q2 2005Q4 2007Q2 2008Q4 2010Q2 2011Q4 2016Q2 2017Q4 2019Q2 2020Q4 2001Q2 2002Q4 2013Q2 2014Q4 Output responds according to oil price changes. Decline in GDP would be similar to previous recessions. Output Output (Year-on-year percentage change) (Quarter-on-quarter percentage change) 15 6 4 10 2 5 0 0 -2 Real GDP Real GDP -5 Baseline Baseline -4 V-Adverse V-Adverse -10 L-Adverse -6 L-Adverse -15 -8 2011Q4 2013Q2 2014Q4 2016Q2 2017Q4 2019Q2 2020Q4 1995Q2 1996Q4 1998Q2 1999Q4 2001Q2 2002Q4 2004Q2 2005Q4 2007Q2 2008Q4 2010Q2 1995Q2 1996Q4 1998Q2 2001Q2 2004Q2 2007Q2 2008Q4 2010Q2 2011Q4 2014Q4 2017Q4 2020Q4 1999Q4 2002Q4 2005Q4 2013Q2 2016Q2 2019Q2 The ruble depreciates … …and unemployment rises. Exchange Rate Unemployment Rate (In Russian rubles per U.S. dollar) (Level) 120 16 FX (level) Unemployment 14 100 Baseline Baseline V-Adverse 12 V-Adverse 80 L-Adverse L-Adverse 10 60 8 6 40 4 20 2 0 0 1996Q4 1999Q4 2001Q2 2002Q4 2004Q2 2007Q2 2010Q2 2011Q4 2013Q2 2014Q4 2016Q2 2017Q4 1995Q2 1998Q2 2005Q4 2008Q4 2019Q2 2004Q2 2005Q4 2007Q2 2008Q4 2010Q2 2011Q4 2013Q2 2014Q4 2016Q2 2017Q4 2020Q4 2019Q2 Source: IMF staff estimates. 20 INTERNATIONAL MONETARY FUND RUSSIAN FEDERATION Figure 5. Oil, GDP Growth, FX, Inflation, and Interest Rates GDP growth is highly correlated with changes in oil Ruble seems to depreciate strongly when oil prices fall, prices…. especially after 2008…. Output and Oil Price Foreign Exchange and Oil Price (Year-on-year percentage change) (Quarter-on-quarter percentage change) 15 150 40 60 Y Foreign exchange 30 10 Brent Brent 40 100 20 5 20 10 50 0 0 0 0 -10 -5 -20 -20 -50 -10 -40 -30 -15 -100 -40 -60 2005Q4 2006Q3 2007Q2 2008Q4 2009Q3 2010Q2 2011Q1 2012Q3 2013Q2 2014Q1 2004Q2 2005Q1 2008Q1 2011Q4 2014Q4 2015Q3 1996Q1 1997Q1 1999Q1 2000Q1 2001Q1 2003Q1 2004Q1 2005Q1 2007Q1 2008Q1 2009Q1 2011Q1 2012Q1 2013Q1 2015Q1 1998Q1 2002Q1 2006Q1 2010Q1 2014Q1 Higher inflation is accommodated by higher interest rates, The inflation pass-through is high… in line with CBR’s objective…. Foreign Exchange and Inflation (Quarter-on-quarter percentage change) Inflation and Interest Rates (Levels, percentage points) 8 60 20 7 Inflation 50 18 Inflation Foreign exchange Interest Rates 16 6 40 14 5 30 12 4 20 10 3 10 8 2 0 6 4 1 -10 2 0 -20 0 2004Q2 2005Q1 2005Q4 2006Q3 2007Q2 2008Q1 2008Q4 2009Q3 2010Q2 2011Q1 2011Q4 2012Q3 2013Q2 2014Q1 2014Q4 2015Q3 2003Q1 2003Q4 2004Q3 2005Q2 2006Q1 2006Q4 2007Q3 2008Q2 2009Q1 2009Q4 2010Q3 2011Q2 2012Q1 2012Q4 2013Q3 2014Q2 Sources: IMF staff calculations. 2015Q1 INTERNATIONAL MONETARY FUND 21 RUSSIAN FEDERATION Figure 6. Stress Test Scenarios and Restructured Loans, 2013–16 Loan impairment charge increases as loan quality … and interest income decreases due to shrinking margins. deteriorates… Loan Impairment Charge Net Interest Income (In percent of gross loans, median) (yoy percentage change, median) 3 70 Vshape 60 2.5 Lshape Baseline 50 2 40 30 1.5 20 10 1 0 Vshape 0.5 -10 Lshape -20 Baseline 0 -30 2007Q1 2008Q3 2010Q1 2011Q3 2016Q1 2017Q3 2019Q1 2020Q3 2013Q1 2014Q3 2008Q3 2010Q1 2013Q1 2014Q3 2016Q1 2017Q3 2019Q1 2020Q3 2007Q1 2011Q3 Output declines sharply, long-run growth is modest… …reflecting the outlook for oil prices. Output Projections Brent Oil Price Prospects 1/ (Constant prices) (In U.S. dollars a barrel) 11500 160 140 11000 120 10500 100 80 95% confidence interval 10000 86% confidence interval 60 68% confidence interval 40 9500 Vshape Lshape Futures 20 Baseline 9000 0 2007Q1 2009Q2 2011Q3 2013Q4 2020Q3 2016Q1 2018Q2 Jan-07 Jan-08 Jan-09 Jan-10 Jan-11 Jan-12 Jan-13 Jan-14 Jan-15 Jan-16 Jan-17 Jul-07 Jul-08 Jul-09 Jul-10 Jul-12 Jul-13 Jul-15 Jul-16 Jul-11 Jul-14 1/ Derived from prices of futures options on March 10, 2015. \ The share of restructured loans has been increasing during the last two years. Share of Restructured Loans in Large Loans (In percent) 45 40 35 30 25 20 15 State-owned Large private 10 Foreign-owned Other 5 0 2009 2010 2011 2012 2013 2014 2015 Sources: Authorities, Bloomberg, and IMF staff estimates. 22 INTERNATIONAL MONETARY FUND RUSSIAN FEDERATION Figure 7. Asset Quality Adjustment Discussions with banking analysts, rating agencies, banks Asset quality adjustments were based on aggregate loan and CBR resulted in an estimate of weak restructured portfolio. loans of 30 percent of restructured loans. Loan Quality of Russian Banking System (01/01/2016) Migration of Bad Restructured Loans Current LP Prov. Ratio Current Prov. Downgraded Loans Adjusted LP Prov. Ratio Adjusted Prov. Standard 26,254 23,836 Substandard 22,237 2% 400 2,418 22,607 2% 407 2,048 6,378 18% 1,148 Doubtful 4,769 18% 858 439 1,718 41% 706 Problem 1,409 41% 579 130 3,572 77% 2,754 Loss 3,442 77% 2,654 58,111 5,015 58,111 4,492 Restructured Loans: 30.7% Loan Imp. Charge Bad Loans: 30.0% 0.9% Higher provisioning, and lower collateralization rates … … result in higher provisions. CBR Regulation (Min/Max) Mid Point Provisioning Adjustment 1% 20% 11% Current LP Adj. Prov. Ratio Adjusted Prov. Standard 26,254 21% 50% 36% Substandard 22,237 3% 758 51% 100% 76% Doubtful 4,769 21% 1,022 100% 100% Problem 1,409 49% 696 Current LP Relative Shares Collateralized Loss 3,442 95% 3,279 Standard 26,254 (excl. standard) 58,111 5,755 Substandard 22,237 70% 69% Loan Imp. Charge Doubtful 4,769 15% 40% 2.2% Problem 1,409 4% 35% Loss 3,442 11% 5% 58,111 100% 56% The combined effect of loan migration and higher provisioning results in loan impairment charge of 3.3 percentage points. Migration of Restructured Loans and Provisioning Adjustment Adjusted LP Adjusted Prov. 23,836 22,607 3% 770 6,378 21% 1,367 1,718 49% 849 3,572 95% 3,403 58,111 6,389 Loan Imp. Charge 3.3% Source: IMF staff calculations. INTERNATIONAL MONETARY FUND 23 RUSSIAN FEDERATION Figure 8. Stress Test Results (V-Shape Scenario) Capital deficits increases are similar in CBR’s and staff’s Credit losses peak in the first two years. stress tests. Credit Losses Capital Deficit Increase (In billions of Russian rubles) (In billions of Russian rubles) 4,500 1,000 4,000 900 Staff estimates Staff estimates 3,500 800 CBR CBR 3,000 700 600 2,500 500 2,000 400 1,500 300 1,000 200 500 100 0 0 2016 2017 2018 2019 2020 2016 2017 2018 2019 2020 Median capital ratios (staff) breach total capital ratio Average capital ratios (CBR) bottom out in 2017 and threshold (CAR Threshold) in 2017, and recover slightly recover by 2020. above by 2020. Capital Ratios Capital Ratios—Without Asset Quality Adjustments (CBR, weigthed average, percent) (Staff estimates, median, percent) 16 16 CET1 CET1 14 CAR 14 CAR CET1 Threshold 12 12 CET1 Threshold CAR Threshold CAR Threshold 10 10 8 8 6 6 4 4 2 2 0 0 2015 2016 2017 2018 2019 2020 2016 2017 2018 2019 2020 Total capital deficit is about 2½ percent of GDP (CBR and When adjusting for forbearance, median capital ratios staff), with additional capital charge of about 2 percent of (Staff) are more affected GDP (staff) due to asset quality adjustments. Total Capital Deficit Capital Ratios—With Asset Quality Adjustments (In percent of GDP) (Staff estimates, median, percent) 3.0 14 2.5 CET1 12 CAR 2.0 0.7 RW CET1 Threshold 10 1.5 CAR Threshold 2.5 2.4 8 1.0 1.1 P 1.5 0.5 6 0.4 0.4 R 0.0 4 R=0.9% P=2.2% R&P=3.3% RW=90% RW=100% RW=100% 2 CBR* Staff estimates* Note: R=Restructuring; P=Provisioning; RW=Risk Weight. 0 *Includes credit, market , interest, and liquidity risk. 2015 2016 2017 2018 2019 2020 **Covers about 80% of total banking assets. Sources: Authorities and IMF staff estimates. 24 INTERNATIONAL MONETARY FUND RUSSIAN FEDERATION Table 1. Financial Soundness Indicators, 2013–16 (In percent) 2013 2014 2015 2016 March Financial Soundness Indicators Capital adequacy Capital to risk-weighted assets 13.5 12.5 12.7 12.4 Core capital to risk-weighted assets 9.1 9.0 8.5 8.4 Credit risk NPLs to total loans 6.0 6.7 8.3 9.2 Loan loss provisions to total loans 5.9 6.5 7.8 8.4 Large credit risks to capital 204.3 245.5 254.4 248.1 Distribution of loans provided by credit institutions Agriculture, hunting and forestry 4.3 3.5 3.5 3.6 Mining 3.1 4.2 4.9 5.5 Manufacturing 13.6 15.5 17.1 16.9 Production and distribution of energy, gas and water 2.5 2.5 2.5 2.5 Construction 5.6 5.3 4.8 4.8 Wholesale and retail trade 13.7 13.3 11.3 11.0 Transport and communication 4.2 4.4 4.2 4.3 Other economic activities 21.1 21.2 24.1 23.9 Individuals 32.0 30.1 27.5 27.6 Of which: mortgage loans 8.5 9.4 10.1 12.5 Geographical distribution of interbank loans and deposits Russian Federation 39.7 53.6 54.0 58.9 United Kingdom 23.8 13.9 12.3 11.1 United States 6.8 4.9 4.5 4.7 Germany 0.6 0.4 0.8 1.0 Austria 7.3 7.3 4.9 4.1 France 1.9 1.8 1.8 2.4 Italy 0.1 0.0 0.0 0.1 Cyprus 4.7 4.9 9.2 6.2 Netherlands 1.5 1.3 0.8 0.4 Other 13.6 11.8 11.8 11.0 Liquidity Highly liquid assets to total assets 9.9 10.4 10.6 11.6 Liquid assets to total assets 20.5 22.0 24.6 22.8 Liquid assets to short-term liabilities 78.7 80.4 139.3 121.8 Ratio of client's funds to total loans 98.7 92.8 59.0 59.4 Return on assets 1.9 0.9 0.3 0.4 Return on equity 15.2 7.9 2.3 3.4 Balance Sheet Structure, in percent of assets Total asset growth rate 16.0 35.2 6.9 9.0 Asset side Accounts with CBR and other central banks 3.9 4.2 3.0 3.6 Interbank lending 8.9 8.9 10.4 10.7 Securities holdings 13.6 12.5 14.2 14.6 Liability side Funds from CBR 7.7 12.0 6.5 5.0 Interbank liabilities 8.4 8.5 8.5 9.5 Individual deposits 29.5 23.9 28.0 27.8 Sources: Authorities and IMF staff calculations. INTERNATIONAL MONETARY FUND 25 RUSSIAN FEDERATION Table 2. Macroeconomic Scenarios: Projections 2015–20 Actual Projections A. Baseline Scenario 2015 2016 2017 2018 2019 2020 Real GDP (%) -3.7 -1.0 1.0 1.5 1.5 1.5 Brent price (USD/bbl, avg.) 53 43 50 54 57 60 Exchange Rate (RUB/USD, avg.) 61 73 68 64 62 61 Inflation (%, avg.) 15.5 8.3 7.0 5.0 4.0 4.0 Short-term Interest Rates (%, avg.) 11.0 8.5 5.5 5.5 5.5 5.5 Unemployment (%, avg.) 5.6 6.4 6.1 6.0 6.0 6.0 B. V-Shape Scenario 2015 2016 2017 2018 2019 2020 Real GDP (%) -3.7 -7.8 2.6 2.9 1.1 1.0 Brent price (USD/bbl, avg.) 53 19 24 32 36 40 Exchange Rate (RUB/USD, avg.) 61 100 88 75 71 67 Inflation (%, avg.) 15.5 15.9 6.1 4.0 4.2 4.2 Short-term Interest Rates (%, avg.) 11.0 17.7 7.6 5.4 5.7 5.7 Unemployment (%, avg.) 5.6 9.5 9.2 8.0 7.4 6.9 C. L-Shape Scenario 2015 2016 2017 2018 2019 2020 Real GDP (%) -3.7 -5.7 -0.3 1.7 1.1 1.0 Brent price (USD/bbl, avg.) 53 25 25 29 33 37 Exchange Rate (RUB/USD, avg.) 61 88 91 83 78 74 Inflation (%, avg.) 15.5 13.3 5.4 4.1 4.2 4.2 Short-term Interest Rates (%, avg.) 11.0 15.2 6.9 5.5 5.7 5.7 Unemployment (%, avg.) 5.6 8.9 9.5 8.6 8.0 7.4 Source: IMF staff calculations. 26 INTERNATIONAL MONETARY FUND RUSSIAN FEDERATION Table 3. Regression Results A: Balance Sheet Items (1) (2) (3) Variables Gross Loans (yoy %) Total Assets (yoy %) Net Charge Offs (% Gross Loans) Gross loans (yoy %) = L, 0.382*** (0.0310) Gross loans (yoy %) 0.853*** -0.0158*** (0.0113) (0.00215) Real GDP (yoy %) -0.125*** (0.0306) Real GDP (yoy %) if g>0 3.820*** (0.410) Real GDP (yoy %) if RETAIL or OTHER 0.643* (0.357) Lending Rate 0.00434 (0.0603) Term Premium 0.412*** (0.131) Real Housing Index (yoy %) -0.0207*** (0.00679) Rouble/USD 0.0478*** (0.0178) Constant 9.938*** 4.236*** 3.715*** (1.461) (0.460) (0.968) Observations 1,487 1,504 1,502 R-squared 0.361 0.884 0.512 Firm FE YES YES YES YEAR>2007 Robust standard errors in parentheses B: Income Statement Items (1) (2) (3) (4) (5) (6) Net interest Trading Inc. Non-interest Net Fees and Other Net Inc. Loan Variables income (yoy (%TA) Expense (yoy Commissions (%TA) Impairment Loans * Lending Rate (yoy %) = L, 0.445*** (0.0342) Deposits * Deposit Rate (yoy %) = L, -0.168*** (0.0264) Total Assets (yoy %) 0.387*** 0.272*** (0.0277) (0.0511) Real GDP (yoy %) 4.149*** -0.0290*** -0.0508*** (0.212) (0.00314) (0.00407) RIX -0.00466 (0.0132) InflationRate_yoy 0.611* (0.355) Unemployment 0.101*** (0.0207) TermPremium 0.0446*** -0.0659*** -0.115*** (0.0133) (0.0136) (0.0186) RHI_yoy_g 0.00589*** -0.00190* (0.000722) (0.00105) Constant 3.323 0.156*** 15.22*** 18.44*** 0.275*** 0.619*** (3.100) (0.0422) (1.211) (2.007) (0.0280) (0.123) Observations 1,481 1,554 1,497 1,136 1,554 1,554 R-squared 0.354 0.152 0.232 0.132 0.412 0.559 Firm FE YES YES YES YES YES YES YEAR>2007 YES Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Source: IMF staff calculations. INTERNATIONAL MONETARY FUND 27 RUSSIAN FEDERATION Table 4. Detailed Stress Test Results 2016 2017 2018 2019 2020 2016 2017 2018 2019 2020 2016 2017 2018 2019 2020 Baseline Stress Scenario (V-shaped) Stress Scenario (L-shaped) Top-down, CBR 1/ without asset quality adjustment CET1 capital ratio 2/ 7.9 8.1 8.1 8.3 8.3 5.9 4.6 5.8 6.3 7.2 6.3 5.2 5.6 6.1 6.7 Total capital ratio 2/ 12.3 12.5 12.6 12.7 12.8 10.6 9.0 10.1 11.0 11.9 11.0 10.2 10.7 11.3 11.8 Top-down, Staff 3/ without asset quality adjustment CET1 capital ratio 4/ 9.0 8.4 10.1 12.4 14.8 5.4 4.6 5.5 6.2 7.4 6.5 4.8 5.2 5.9 6.5 Total capital ratio 4/ 12.5 12.8 13.3 14.2 16.2 8.7 7.5 8.0 8.4 9.0 9.7 8.1 7.8 8.4 8.8 Top-down, Staff 3/ with asset quality adjustment CET1 capital ratio 4/ 6.0 5.5 7.2 9.5 11.8 1.6 0.8 1.7 2.3 3.3 2.5 1.0 1.4 2.0 2.6 Total capital ratio 4/ 9.5 9.9 10.3 11.2 13.2 4.5 3.5 3.9 4.2 4.8 5.5 3.9 3.8 4.2 4.7 Top-down, CBR 1/ Sum Sum Sum without asset quality adjustment Credit losses 5/ 1.2 1.7 0.8 0.6 0.5 4.8 3.4 4.7 1.4 0.9 0.7 11.1 2.8 3.7 1.5 0.9 0.7 9.6 Total capital deficit 6/ 7/ 0.1 0.2 0.2 0.2 0.3 0.5 1.5 1.9 2.2 2.5 0.4 1.2 1.6 1.9 2.1 Top-down, Staff 3/ without asset quality adjustment Credit losses 5/ 2.9 1.9 1.7 1.8 1.8 10.2 6.8 2.1 1.8 2.5 2.5 15.8 5.8 3.5 2.4 2.6 2.5 16.7 Total capital deficit 6/ 0.1 0.2 0.3 0.4 0.4 0.7 1.4 1.7 2.1 2.4 0.4 1.3 1.8 2.3 2.7 Top-down, Staff 3/ with asset quality adjustment Credit losses 5/ 6.2 1.9 1.7 1.8 1.8 13.5 10.1 2.1 1.8 2.5 2.5 19.1 9.1 3.5 2.4 2.6 2.5 20.0 Total capital deficit 6/ 0.5 0.8 0.9 1.0 1.0 3.8 4.6 4.4 4.3 4.2 3.0 4.0 4.3 4.4 4.4 Top-down, CBR 1/ without asset quality adjustment No. of banks with capital deficit 1/ 34 36 27 23 19 72 140 76 34 29 62 108 67 35 19 Share of banks with capital deficit /8 6.8 7.0 6.2 6.1 5.7 19.1 29.7 18.7 5.8 5.4 17.2 28.9 15.5 6.4 4.9 Top-down, Staff 3/ without asset quality adjustment No. of banks with capital deficit 3/ 7 7 9 9 9 16 20 19 18 16 13 18 19 18 17 Share of banks with capital deficit /8 8.4 11.8 15.0 15.0 15.0 37.0 40.0 40.0 39.4 38.1 34.8 38.4 39.4 39.4 39.0 Top-down, Staff 3/ with asset quality adjustment No. of banks with capital deficit 3/ 15 15 15 14 12 27 28 26 25 23 27 25 26 25 24 Share of banks with capital deficit /8 37.0 37.0 37.7 37.5 18.7 86.5 86.8 83.5 83.3 44.3 86.5 83.2 83.4 83.3 44.3 Source: IMF staff calculations. 1/ Based on a sample of 681 banks. 5/ In percent of total loans. 2/ Asset weighted mean. 6/ In percent of GDP. 3/ Based on a sample of largest 37 banks. 7/ Includes also market and liquidity risk. 4/ Median 8/ In percent of sample total assets. 28 INTERNATIONAL MONETARY FUND RUSSIAN FEDERATION Table 5. Banks’ Bottom-Up Results for 12 Banks in 2016 Baseline V-Shape L-Shape Main Results Scenario Scenario Scenario Total losses, bn RUB 947 2, 882 2,122 Losses on credit risk, bn RUB 786 1,978 1,536 Including losses on: Corporate portfolio, bn RUB 561 1,572 1,215 Retail portfolio, bn RUB 225 407 322 Losses on market risk, bn RUB 1 146 90 Including losses on: Interest rate risk on trade portfolio, bn RUB 0 17 10 Equity risk, bn RUB 0 76 49 FX risk, bn RUB 0 54 31 Losses on Interest rate risk on banking book, bn RUB 25 404 224 Losses on liquidity risk, bn RUB 135 254 225 Financial result, bn RUB 730 -1,159 -466 Aggregate capital deficit, bn RUB 3 199 70 Number of banks with capital deficit 1 6 2 Source: CBR estimates. INTERNATIONAL MONETARY FUND 29 RUSSIAN FEDERATION Table 6. CBR’s Top-Down Results for the Same 12 Banks in 2016 Baseline V-Shape L-Shape Main Results Scenario Scenario Scenario Total losses, bn RUB 1,687 3,746 3,425 Losses on credit risk, bn RUB 1,277 2,221 2,031 Including losses on: Corporate portfolio, bn RUB 1,031 1,807 1,659 Retail portfolio, bn RUB 247 414 372 Losses on market risk, bn RUB 197 749 678 Including losses on: Interest rate risk on trade portfolio, bn RUB 108 412 376 Equity risk, bn RUB 59 225 204 FX risk, bn RUB 30 112 98 Losses on Interest rate risk on banking book, bn RUB 189 720 671 Losses on liquidity risk, bn RUB 24 56 45 Financial result, bn RUB 334 -680 -505 Aggregate capital deficit, bn RUB 65 522 426 Number of banks with capital deficit 1 7 6 Source: CBR estimates. 30 INTERNATIONAL MONETARY FUND RUSSIAN FEDERATION Table 7. Results of Single-Factor Tests (at the end of 2015 Credit Market Risk Risk Interest Rate Risk in the Trading Book Corporate Corporate sector sector Default of +1000bps +1000bps top five Exchange rate Equity government government bank depreciation of decline of bonds bonds borrowers 20 percent 30 percent +400 bps +0 bps Number of 338 5 7 13 4 banks with capital deficit Share of 82.5 0.1 0.5 6.7 1.0 total banking assets (in percent) Aggregated 1,519 0.2 0.9 20 6.7 capital deficit, bn RUB Source: CBR estimates. INTERNATIONAL MONETARY FUND 31 RUSSIAN FEDERATION Table 8. Assumptions of Liquidity Tests Very Severe Parameters Mild Scenario Severe Scenario Scenario Outflows, in percent Individual deposits 20 (10 for 30 (10 for 30 (10 for largest bank) largest bank) largest bank) Deposits of legal entities 10 10 30 Funds of legal entities in settlement 20 30 40 (20 for and other accounts largest bank) Interbank loans from non-residents 30 50 50 Interbank loans from residents 0 0 40 (20 for largest bank) Fire Sales with Discounts, in percent Cash and correspondent accounts 0 0 0 with the CRB Highly liquid assets 5 5 5 Liquid assets 20 20 20 Securities not included in liquid 30 40 65 assets Source: CBR estimates. 32 INTERNATIONAL MONETARY FUND Assumptions Appendix I. Banking Sector Stress Testing Matrix (STeM) Domain Top-Down by FSAP Team Top-Down by Authorities Bottom-Up by Authorities Banking Sector: Solvency Risk 1. Institutional Institutions included  Top 37  681  12 large banks Perimeter (including 10 systemically important banks) Market share  81.5 percent  99.7 percent  66 percent Data and baseline date  Institutions’ public data, up to  Public data and other data, up  Institutions’ own data; 3rd Q 2015 to 4th Q 2015 up to 4th Q 2015  Consolidated banking group,  Stand-alone basis  Stand-alone basis and stand-alone basis 2. Channels of Methodology  Balance-sheet model (loan  Balance-sheet model  Banks’ internal models Risk impairment charge/ (NPLs/provisions) provisions/NPLs) Propagation  Supplemental balance sheet analysis of impact of restructuring and under- provisioning Satellite Models for  Models for credit losses, NPLs,  Models for credit losses, NPLs,  Own models Macrofinancial linkages loan impairment charge, pre- provisions, pre-impairment impairment income income INTERNATIONAL MONETARY FUND  Satellite model bank by bank  CBR macro model has second using IFRS data round effects related to the dependency between credit growth and growth of fixed capital investments Stress test horizon  Five years, 2016–20 Five years, 2016–20  One year RUSSIAN FEDERATION 33 RUSSIAN FEDERATION Assumptions 34 Domain Top-Down by FSAP Team Top-Down by Authorities Bottom-Up by Authorities INTERNATIONAL MONETARY FUND 3. Tail shocks Scenario analysis  Macro scenarios are shocks  Macro scenarios are shocks  Macro scenarios are shocks conditioned upon oil price, conditioned upon oil price, conditioned upon oil price, GDP, exchange rate, inflation, GDP, exchange rate, inflation, GDP, exchange rate, interest rates, unemployment interest rates, unemployment inflation, interest rates,  Baseline, adverse V-shaped  Baseline, adverse V-shaped unemployment. scenario, adverse L-shaped scenario, adverse L-shaped  Baseline, adverse V-shaped scenario (magnitude of scenario scenario, adverse L-shaped shocks derived from adverse scenario oil price scenario plus macro model) Sensitivity analysis  Not performed by staff  Single-factor shocks are  Not performed by banks applied; concentration risk: default of top five borrowers; market risks: FX 20 percent depreciation, equity 30 percent decline, interest rate +1000 bps corporate bonds and+400 bps, government bonds 4. Risks and Risks/factors assessed  Credit losses, profitability,  Credit losses, profitability,  Same as authorities’ TD Buffers (how each element is income after tax, funding income after tax, funding stress tests costs, exchange rate costs, market risk, fixed derived, assumptions) income holdings of banks/sovereigns, exchange rate Behavioral adjustments  Dividend payout can be  Dividend payout can be  Banks’ internal models restricted by CBR. restricted by CBR. Assumptions Domain Top-Down by FSAP Team Top-Down by Authorities Bottom-Up by Authorities 5. Regulatory Calibration of risk  NPLs/provisions  NPLs/provisions  Own methodologies and Market- parameters  RWA according to  RWA according to  RWA similar to authorities’ standardized approach standardized approach stress tests Based  Risk weights increase from Standards and 90 percent to 100 percent in Parameters the prudent approach Regulatory/Accounting  Hurdle rate: Basel III schedule,  Hurdle rate: Basel III schedule,  Hurdle rate: Basel III and Market-Based local regulatory requirements local regulatory requirements schedule, local regulatory requirements Standards  CET1 of 4.5 percent, CAR of  CET1 of 4.5 percent, CAR of  CET1 of 4.5 percent, T1 of 8 percent 8 percent 6 percent, CAR of 8 percent 6. Reporting Output presentation  Dispersion of capital ratios:  Distributions by capital ratio:  Capital shortfall, system Format for median weighted average wide.  Capital deficit, system wide  Capital deficit, system wide  Pass or fail; percentage of Results  Pass or fail; percentage of  Pass or fail; percentage of assets that fail assets that fail assets that fail  Number of banks  Number of banks  Number of undercapitalized undercapitalized undercapitalized banks INTERNATIONAL MONETARY FUND 7. Asset Stress testing  Adjustment for weak  Not performed by authorities  Not performed by banks quality methodology restructured loans and under- provisioning lead to one-off adjustment increase in provisions at beginning of stress testing period RUSSIAN FEDERATION 34 RUSSIAN FEDERATION 34 INTERNATIONAL MONETARY FUND Assumptions Domain Top-Down by FSAP Team Top-Down by Authorities Bottom-Up by Authorities BANKING SECTOR: LIQUIDITY RISK 1. Institutional Institutions included  Carried out by authorities with  681  Banks included in Perimeter agreed scenarios authorities’ liquidity scenarios Market share  N/A  99.7 percent  N/A Data and baseline date  N/A  Institutions’ public data and  N/A other data up to 4th Q 2015  Stand-alone basis 2. Channels of Methodology  N/A  Cash outflow asset discounts  N/A Risk and sensitivity analysis for liquidity risk Propagation 3. Risks and Risks  N/A  Outflows from liabilities  N/A Buffers liquidity shock  Discounts on assets  Market liquidity shock Buffers  N/A  Counterbalancing capacity  N/A stand-alone 4. Tail shocks Size of the shock  N/A  Bank run and dry up of  N/A (one period, i.e., one-off wholesale funding markets, taking into account haircuts shock to most recent to liquid assets balance sheet). Assumptions Domain Top-Down by FSAP Team Top-Down by Authorities Bottom-Up by Authorities  Outflows: household deposits-30 percent (10 percent largest bank); legal entities 30 percent; corporate settlement and other accounts-40 percent (20 for largest bank); interbank loans non-resident 50 percent; interbank loans resident 40 percent (20 for largest bank)  Haircut: highly liquid asset, 5 percent; liquid asset 20 percent; low-liquid asset 65 percent 5. Regulatory Regulatory standards N/A Amount of liquidity deficit and N/A and Market- number of banks with liquidity deficit Based Standards and Parameters INTERNATIONAL MONETARY FUND 6. Reporting Output presentation N/A Amount of liquidity deficit and N/A Format for number of banks with liquidity deficit Results RUSSIAN FEDERTION 34