Leading Indicators of Russian Banking Sector Risks : Methodology and Examples

1. Leading Indicators of Russian Banking Sector Risks: Methodology and Examples

2. The Objectives of Research • to estimate probability ofbanking turmoiltill 2012 • to identify risks in different scenarios

3. Methodology and Tools Medium-term econometric model of national economy • Macroeconomic indicators (GDP, inflation, investment, retail trade and etc.) • Income distribution • Consolidated budget • Balance of payments • Households balances • Exchange and interest rates • Monetary aggregates • Central bank balance • Banking system balance Composite medium-term forecast System of leading indicators of banking crises • Liquidity risks indicators • Credit risks indicators • Currency risks indicators Composite leading indicator (CLI)

4. Framework: • only macroeconomic factors of systemic risks; political factors are ignored • Analysis of systemic crisis probability; probability of local crises, which are related to small groups of banks (e.g. 2004 crisis) isnot estimated

5. Medium of exchange Store of value Universal money The function of money Money issuance, ensuring its circulation Transformation of savings into investments Securing international accounts, capital import and etc. Banking sector function Liquididty risk Credit risk Currency risk Risk type Liquidity crisis Bad loans crisis Currency debt crisis Crisis type Liquidassets provision Equity capital provision Foreign and currency assets provision Stability criterion System of Leading Indicators The Model of Banking Crises, which is the theoretical basis of leading indicators model

6. Estimated Models for Panel Data Discrete Choice models Binary Choice Logit Model with Fixed Effects Multinomial Logit Model

7. Econometric Estimation of Multinomial Logit Model The probability of systemic banking crisis was estimated by means of the following general equation: Where • dependent variable, which takes value j. In our case j takes values 0,1,2. j=0 in the case of banking crisis absence, j=1 in the year priorto banking crisis and j=2 in the crisis year; • leading indicators; • coefficients; • countries from 1 to n.

8. Econometric Estimation of Multinomial Logit Model where , if dependent variable takes value j for country i & in opposite case.

9. Leading Indicators, Included in Multinomial Logit ModelsM7 and M10: Liquidity risk indicators: • RLS_1_1 (-) • RLS_1_2(-) Credit risk indicators: • DKRS_2_1 (-) • ALT_S (+) Currency risk indicators: • VRS_3_1 (-) • VRS_3_2 (-) Institutional indicator: • GDPperc

10. Multinomial Logit Model М7

11. Multinomial Logit Model М10

12. Estimated Probability of Systemic Banking Crisis in Russiafor the Period 1994-2003 Here and further: Pr1M7 – probability of systemic banking crisis, estimated with model M7. Pr1M10 – probability of systemic banking crisis, estimated with model M10. Lcrisis_3 – dependent variable , which takes value 0 in the year without crisis, 1 in the year prior to banking crisis and 2 in the crisis year. Value 2 is not represented on the graphs in order to simplify them.

13. Estimated Probabilities of Systemic Banking Crisisfor Sample Countries in the Period 1989-2002

14. Estimated Probabilities of Systemic Banking Crisis for Sample Countries in the Period 1989-2002

15. Estimated Probabilities of Systemic Banking Crisis for Sample Countries in the Period 1989-2002

16. Oil Prices in Three Scenarios

17. Euro Exchange Rate in Three Scenarios

18. Ruble Exchange Rate in Three Scenarios

19. Net Capital Inflow in Three Scenarios

20. The Dynamics of Composite Leading Indicator in the Baseline Scenario The graph shows that CLI (composite leading indicator) rises fast at the end of 2007, becomes close to the threshold level 0.19 at the beginning of 2009 and exceeds it at the end of 2009. When the CLI exceeds the threshold value, it signals that current risks are so high that they may realize into crisis next year. According to the dynamics of CLI in 2009 banking sector risks sharply rise and it means that in 2010 Russian banking sector may suffer difficulties and high risks that may entail the systemic banking crisis.

21. The Dynamics of Composite Leading Indicator in the Soft Landing Scenario In the soft landing scenario the situation is almost the same. CLI sharply rises from the second half of 2007, exceeds the threshold value at the end of 2009 and continue increasing till the end of 2011. In 2009 CLI signals that risks are too high and soon systemic problems may appear in banking sector. The increase in credit risks makes the major contribution to the CLI growth. Credit risks rise due to fast consumption growth, which is faster than households’ and enterprises’ income growth.

22. The Dynamics of Composite Leading Indicator in the Hard Landing Scenario The behavior of CLI in hard landing differs from the baseline and soft landing scenarios. The CLI slows, because the ruble depreciation leads to the consumption slowing down and decrease in credit risks. The same effect upon the credit risks produces slowing down in external debt growth. Besides, ruble depreciation improves balance of payments and hence banking system liquidity.

23. The Dynamics of Particular Leading Indicators in the Baseline Scenario This graph shows the dynamics of three components of CLI: credit, liquidity and currency risks. Credit risks sharply rise. The explanation lies in expected increase in loan payment defaults of households and enterprises. Defaults can happen, because currently households’ and enterprises’ spending grows faster than their incomes. Liquidity risks rise due to lack of liquid assets in banking system. Currency risks stay stable.

24. The Dynamics of Particular Leading Indicators in the Soft Landing Scenario In the soft landing scenario credit risks and liquidity risks rise more sharper. The reasons are the same as in the baseline scenario. But expected difficulties in banking system are stronger.

25. The Dynamics of Particular Leading Indicators in the Hard Landing Scenario On the contrary to baseline and soft landing scenarios in the hard landing scenario risks at first sharply rise and then stabilize. The growth of credit risks slows down due to decrease in consumption growth. The reasons for liquidity risks stabilization may be: slowing down in external debt growth and ruble devaluation.

26. The Ratio of Enterprises and Households Investments and Consumption to their Incomes (%, data for last four quarters) Investments and consumption of enterprises and households increase faster than their receipts. Fast expansion of spending comparatively to income is concerned with attraction of borrowing costs. This process may lead to Ponzi schemes, which mean that companies and households repay previous loans by taking new ones. As a result in the case of temporary difficulties with getting new loans major part of borrowers may become insolvent. In the extreme case it may lead to realization of such scheme: increase in defaults in payments →slowing downof credit growth → decrease in consumer and investment demand → slowing down of economic growth → slowing down of income → increase in defaults in payments.

27. The Dynamics of Real Disposable Income and Households’ Consumption (quarter year on year growth, %) The growth rate of population consumption is higher than the growth rate of its disposable income. It may lead to difficulties with loans payments, because household, which suffer lack of income, may service their debt by taking new loans.

28. The Dynamics of Gross Profit and Investment in Fixed Capital (quarter year on year growth, %) The corporate sector investment increases faster than their profits. Such situation may cause problems with enterprises’ debt payments, if they suffer difficulties with taking new loans to service previous ones.

29. The Growth of Money Supply (broad definition) and Money Demand (monetary aggregateM2, %) The lag of money supply (broad definition) comparatively to expansion of money demand (monetary aggregate M2) is observed from 2004. The reason for that is intensive sterilization of money supply in Russian sovereign investment funds (till 2008 in Stabilization fund, after 2008 in Reserve Fund and National Welfare Fund ). In the middle-run if the monetary policy stays the same, the gap will be increasing. Increase in import may lead to the slowing down in foreign currency reserves growth. This slowing down may cause decrease in main source of money supply expansion.

30. Banking System Liquidity1(%) The steady money supply lagging from its demand leads to fall in banking system liquidity. This fall means decrease in ratio of liquid assets, which service the turnover of clients’ accounts, to balances on this accounts.It leads to difficulties in paymentsin banking system, which in complex with another appeared problems may destabilize many banks. 1the ratio of liquid bank assets in national currency to their liabilities in rubles. Liquid assets are: cash, balances on correspondent accounts in the Central Bank, bonds and other time liabilities of the Central Bank.

31. External Debt of Private Sector(in percent of export of goods and services) The graph confirms the tendency of increase in external debt of enterprises and banks. The net debt of banking system is rising. Besides the net debt in foreign currency of enterprises and households to banking system is increasing. The net debt in foreign currency means the difference between volume of loans in foreign currency and volume of deposits in foreign currency of companies and population. It means that the risk of possible loses in case of unexpected ruble depreciation will lead to redistribution from banks to enterprises and population. In addition the net external debt of enterprises is expected to increase and that may intensify their risks.