Leading Indicators of Russian Banking Sector Risks: Methodology and Examples

1
Leading Indicators of Russian Banking Sector
Risks Methodology and Examples
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The Objectives of Research

• to estimate probability of banking turmoil till
  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)
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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) is not estimated

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System of Leading Indicators
The Model of Banking Crises, which is the
theoretical basis of leading indicators model
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Estimated Models for Panel Data
Discrete Choice models
Binary Choice Logit Model with Fixed Effects
Multinomial Logit Model
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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. j0 in the case of
  banking crisis absence, j1 in the year prior to
  banking crisis and j2 in the crisis year

• leading indicators

• coefficients

• countries from 1 to n.

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Econometric Estimation of Multinomial Logit Model
where , if dependent variable
takes value j for country i
in opposite case.
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Leading Indicators, Included in Multinomial
Logit Models M7 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

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Multinomial Logit Model ?7
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Multinomial Logit Model ?10
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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.
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Estimated Probabilities of Systemic Banking
Crisisfor Sample Countries in the Period
1989-2002
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Estimated Probabilities of Systemic Banking
Crisis for Sample Countries in the Period
1989-2002
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Estimated Probabilities of Systemic Banking
Crisis for Sample Countries in the Period
1989-2002
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Oil Prices in Three Scenarios
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Euro Exchange Rate in Three Scenarios
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Ruble Exchange Rate in Three Scenarios
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Net Capital Inflow in Three Scenarios
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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.
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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.
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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.
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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.
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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.
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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.
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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 down of credit growth ?
decrease in consumer and investment demand ?
slowing down of economic growth ? slowing down of
income ? increase in defaults in payments.
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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.
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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.
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The Growth of Money Supply (broad definition)
and Money Demand (monetary aggregate M2, )
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.
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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 payments in 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.

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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.