leading indicators of russian banking sector risks : methodology and examples

May 2008

CENTER FOR MACROECONOMIC ANALYSIS AND SHORT-TERM FORECASTING

Leading Indicators of Russian Banking Sector RisksLeading Indicators of Russian Banking Sector Risks: : Methodology and ExamplesMethodology and Examples

CMASF

May 20082

2

The Objectives of ResearchThe Objectives of Research

• to estimate probability ofto estimate probability of banking turmoilbanking turmoil till 2012till 2012

• to identify risks in different scenariosto identify risks in different scenarios

CMASF

May 20083

3

Medium-term econometric model of national economy 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

Composite medium-term forecast

System of leading indicators of banking crisesSystem of leading indicators of banking crises• Liquidity risks indicators• Credit risks indicators• Currency risks indicators

Composite leading indicator (CLI)

Composite leading indicator (CLI)

Methodology and ToolsMethodology and Tools

CMASF

May 20084

4

Framework:Framework:

• only only macroeconomic factorsmacroeconomic factors of systemic risks; of systemic risks; political factors are ignoredpolitical factors are ignored

• Analysis of Analysis of systemic crisis systemic crisis probabilityprobability;; probability of local crises, which are related to small groups of banks probability of local crises, which are related to small groups of banks

(e.g. 2004 crisis) is(e.g. 2004 crisis) is not estimatednot estimated

CMASF

May 20085

5

System of Leading IndicatorsSystem of Leading IndicatorsThe Model of Banking Crises,

which is the theoretical basis of leading indicators model

The function of money

Medium of exchange Store of value Universal money

Banking sector function

Risk type

Crisis type

Stability criterion

Currency debt crisisBad loans crisis

Liquididty risk Credit risk Currency risk

Liquidity crisis

Liquid assets provision Equity capital provision

Foreign and currency assets provision

Money issuance, ensuring

its circulation

Transformation of savings into investments

Securing international accounts, capital import and

etc.

CMASF

May 20086

6

Estimated Models for Panel DataEstimated Models for Panel Data

Binary Choice Logit Model

with Fixed Effects

Binary Choice Logit Model

with Fixed Effects

Multinomial Logit Model

Multinomial Logit Model

Discrete Choice modelsDiscrete Choice models

CMASF

May 20087

7

Econometric Estimation of Multinomial Logit ModelEconometric Estimation of Multinomial Logit ModelThe probability of systemic banking crisis was estimated by means of the The probability of systemic banking crisis was estimated by means of the

following general equation:following general equation:

iY

J

k

x

x

iiik

ij

e

exjY

1

1)|Pr(

Jj ,...,2,1,0 00

WhereWhere

ix - leading indicators; leading indicators;

j

- dependent variable, which takes value dependent variable, which takes value jj. In our case . In our case j j takes valuestakes values 0,1,2. j=0 0,1,2. j=0 in the case of banking crisis absence,in the case of banking crisis absence, j=1 j=1 in the year priorin the year prior to banking crisis andto banking crisis and j=2 j=2 in the crisis year; in the crisis year;

- coefficients;coefficients;

i - countries from 1 to n.countries from 1 to n.

CMASF

May 20088

8

Econometric Estimation of Multinomial Logit ModelEconometric Estimation of Multinomial Logit Model

max)Pr(lnln1 0

n

i

J

jiij jYdL

1ijdwherewhere , , if dependent variable takes value j for country i if dependent variable takes value j for country i

& & in opposite case in opposite case. .

0ijd

CMASF

May 20089

9

Leading Indicators, Included in Leading Indicators, Included in Multinomial Logit ModelsMultinomial Logit Models M7 and M10M7 and M10::

Liquidity risk indicatorsLiquidity risk indicators::

• RLS_1_1 (-) RLS_1_1 (-)

• RLS_1_2RLS_1_2 (-)(-)

Credit risk indicatorsCredit risk indicators::

• DDKRS_2_1 (-) KRS_2_1 (-)

• ALT_S (+)ALT_S (+)

Currency risk indicatorsCurrency risk indicators::

• VRS_3_1 (-)VRS_3_1 (-)

• VRS_3_2 (-)VRS_3_2 (-)

Institutional indicatorInstitutional indicator::

• GDPpercGDPperc

CMASF

May 200810

10

Multinomial Logit ModelMultinomial Logit Model М7 М7

(Lcrisis_3==0 is the base outcome) ALT_S 2.148999 2.348564 0.92 0.360 -2.454102 6.752101 GDPperc -.0000528 .0000152 -3.46 0.001 -.0000827 -.0000229 VRS_3_2 -.5594877 .2913567 -1.92 0.055 -1.130536 .011561 VRS_3_1 .0004628 .0006512 0.71 0.477 -.0008135 .0017392 RLS_1_1 -5.328523 1.269692 -4.20 0.000 -7.817074 -2.839972 DKRS_2_1 -6.796459 4.556388 -1.49 0.136 -15.72682 2.1338972 ALT_S 5.604374 2.935972 1.91 0.056 -.1500249 11.35877 GDPperc -.0000561 .0000187 -3.00 0.003 -.0000927 -.0000195 VRS_3_2 -.5222923 .316156 -1.65 0.099 -1.141947 .0973622 VRS_3_1 -.2891215 .1612014 -1.79 0.073 -.6050704 .0268274 RLS_1_1 -6.428908 1.956398 -3.29 0.001 -10.26338 -2.594439 DKRS_2_1 -13.13579 4.860444 -2.70 0.007 -22.66209 -3.6094991 Lcrisis_3 Coef. Std. Err. z P>|z| [95% Conf. Interval]

Log likelihood = -201.37485 Pseudo R2 = 0.3477 Prob > chi2 = 0.0000 LR chi2( 12) = 214.67Multinomial logistic regression Number of obs = 281

CMASF

May 200811

11

Multinomial Logit ModelMultinomial Logit Model М10 М10

(Lcrisis_3==0 is the base outcome) ALT_S 5.139343 2.533769 2.03 0.043 .1732479 10.10544 VRS_3_1 .0006373 .0006477 0.98 0.325 -.0006323 .0019068 RLS_1_2 -3.174903 .76808 -4.13 0.000 -4.680312 -1.669494 RLS_1_1 -6.190026 1.489104 -4.16 0.000 -9.108616 -3.271435 DKRS_2_1 -11.50631 5.03061 -2.29 0.022 -21.36613 -1.64652 ALT_S 7.497954 3.059234 2.45 0.014 1.501965 13.49394 VRS_3_1 -.2805628 .1586527 -1.77 0.077 -.5915163 .0303906 RLS_1_2 -3.523644 .9022425 -3.91 0.000 -5.292006 -1.755281 RLS_1_1 -5.936016 2.049218 -2.90 0.004 -9.95241 -1.919622 DKRS_2_1 -15.23308 5.192586 -2.93 0.003 -25.41036 -5.0557981 Lcrisis_3 Coef. Std. Err. z P>|z| [95% Conf. Interval]

Log likelihood = -176.95814 Pseudo R2 = 0.3922 Prob > chi2 = 0.0000 LR chi2( 10) = 228.35Multinomial logistic regression Number of obs = 265

CMASF

May 200812

12

Estimated Probability of Systemic Banking Crisis in RussiaEstimated Probability of Systemic Banking Crisis in Russiafor the Period for the Period 1994-20031994-2003

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.

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.

iY

Russian Federation

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

0

0.2

0.4

0.6

0.8

1

1.2

Pr1M7 Pr1M10 Lcrisis_3

0.19 - threshold level of crisis probability

CMASF

May 200813

13

Estimated Probabilities of Systemic Banking CrisisEstimated Probabilities of Systemic Banking Crisisfor Sample Countries in the Period for Sample Countries in the Period 1989-20021989-2002

Venezuela

0

0.01

0.02

0.03

0.04

0.05

0.06

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

0

0.2

0.4

0.6

0.8

1

1.2


Haiti

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

1987

1989

1991

1993

1995

1997

1999

2001

0

0.2

0.4

0.6

0.8

1

1.2


Brazil

0

0.05

0.1

0.15

0.2

0.25

0.3

1990

1991

1992

1993

1994

1995

1996

1997

1998

0

0.2

0.4

0.6

0.8

1

1.2


Russian Federation

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

0

0.2

0.4

0.6

0.8

1

1.2


Argentina

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

0

0.2

0.4

0.6

0.8

1

1.2


Turkey

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

0

0.2

0.4

0.6

0.8

1

1.2


CMASF

May 200814

14

Norway

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

1987

1989

1991

1993

1995

1997

1999

2001

0

0.2

0.4

0.6

0.8

1

1.2


Sweden

00.050.1

0.150.2

0.25

0.30.350.4

0.450.5

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

0

0.2

0.4

0.6

0.8

1

1.2


Denmark

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

0

0.2

0.4

0.6

0.8

1

1.2


Greece

0

0.05

0.1

0.15

0.2

0.25

0.3

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

0

0.2

0.4

0.6

0.8

1

1.2


Estimated Probabilities of Systemic Banking CrisisEstimated Probabilities of Systemic Banking Crisis for Sample Countries in the Period for Sample Countries in the Period 1989-20021989-2002

CMASF

May 200815

15

Estimated Probabilities of Systemic Banking CrisisEstimated Probabilities of Systemic Banking Crisis for Sample Countries in the Period for Sample Countries in the Period 1989-20021989-2002

Korea

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

0

0.2

0.4

0.6

0.8

1

1.2


Philippines

00.02

0.040.06

0.080.1

0.120.14

0.160.18

0.2

1987

1989

1991

1993

1995

1997

1999

2001

0

0.2

0.4

0.6

0.8

1

1.2


Salvador

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

0

0.2

0.4

0.6

0.8

1

1.2

Pr1M7 Lcrisis_3

Thailand

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

1987

1989

1991

1993

1995

1997

1999

2001

0

0.2

0.4

0.6

0.8

1

1.2


Tunisia

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

0

0.2

0.4

0.6

0.8

1

1.2


Bolivia

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

1987

1989

1991

1993

1995

1997

1999

2001

0

0.2

0.4

0.6

0.8

1

1.2


CMASF

May 200816

16

Oil Prices in Three ScenariosOil Prices in Three Scenarios

Oil Price (Urals, $ per barrel)

0

50

100

150

200

2005

2006

2007

2008

2009

2010

2011

Hard landing Baseline Soft landing

CMASF

May 200817

17

Euro Exchange Rate in Three ScenariosEuro Exchange Rate in Three Scenarios

Euro Exchange Rate, average ($ per euro)

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

2005

2006

2007

2008

2009

2010

2011


CMASF

May 200818

18

Ruble Exchange Rate in Three ScenariosRuble Exchange Rate in Three Scenarios

Rouble Exchange Rate, average (RUR per $)

21

22

23

24

25

26

27

28

2920

05

2006

2007

2008

2009

2010

2011


CMASF

May 200819

19

Net Capital Inflow in Three ScenariosNet Capital Inflow in Three Scenarios

Net Capital Inflow (bln $)

-60

-40

-20

0

20

40

60

80

10020

05

2006

2007

2008

2009

2010

2011


CMASF

May 200820

20

The Dynamics of Composite Leading IndicatorThe Dynamics of Composite Leading Indicator in the Baseline Scenario 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.

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.

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50M

ar-0

0

Jun-

00

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-00

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02

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05

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06

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07

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11

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threshold level

CMASF

May 200821

21

The Dynamics of Composite Leading IndicatorThe Dynamics of Composite Leading Indicator in the Soft Landing Scenario 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.

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.

0.00

0.05

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0.25

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0.35

0.40

0.45

0.50

Mar

-00

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00S

ep-0

0

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01

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02

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03

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04

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05S

ep-0

5

Dec

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Mar

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06S

ep-0

6

Dec

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Mar

-07

Jun-

07

Sep

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Dec

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Mar

-08

Jun-

08

Sep

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Dec

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Mar

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Jun-

09

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Mar

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Jun-

10

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Jun-

11S

ep-1

1

Dec

-11

threshold level

CMASF

May 200822

22

The Dynamics of Composite Leading IndicatorThe Dynamics of Composite Leading Indicator in the Hard Landing Scenario 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.

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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ar-0

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00S

ep-0

0

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01

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02

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03

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04

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05S

ep-0

5

Dec

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Mar

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Jun-

06S

ep-0

6

Dec

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Jun-

07

Sep

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Dec

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Mar

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Jun-

08

Sep

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09

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10

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11S

ep-1

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threshold level

CMASF

May 200823

23

This graph shows the dynamics of three components of CLI: credit, liquidity and currency risks. Credit risks sharply rise. Theexplanation 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.

This graph shows the dynamics of three components of CLI: credit, liquidity and currency risks. Credit risks sharply rise. Theexplanation 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.

The Dynamics of Particular Leading Indicators in the Baseline The Dynamics of Particular Leading Indicators in the Baseline Scenario Scenario

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Credit risks Liquidity risks Currency risks

CMASF

May 200824

24

The Dynamics of Particular Leading Indicators in the Soft Landing The Dynamics of Particular Leading Indicators in the Soft Landing ScenarioScenario

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. 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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CMASF

May 200825

25

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.

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.

The Dynamics of Particular Leading Indicators in the Hard Landing The Dynamics of Particular Leading Indicators in the Hard Landing Scenario Scenario

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05S

ep-0

5D

ec-0

5M

ar-0

6Ju

n-06

Sep

-06

Dec

-06

Mar

-07

Jun-

07S

ep-0

7D

ec-0

7M

ar-0

8Ju

n-08

Sep

-08

Dec

-08

Mar

-09

Jun-

09S

ep-0

9D

ec-0

9M

ar-1

0Ju

n-10

Sep

-10

Dec

-10

Mar

-11

Jun-

11S

ep-1

1D

ec-1

1


CMASF

May 200826

26

The Ratio of Enterprises and Households Investments and The Ratio of Enterprises and Households Investments and

Consumption to their Incomes (Consumption to their Incomes (%, data for last four quarters%, data for last four quarters))

83.5%

84.5%

85.5%

86.5%

87.5%

88.5%

89.5%

90.5%

1.20

02

2.20

02

3.20

02

4.20

02

1.20

03

2.20

03

3.20

03

4.20

03

1.20

04

2.20

04

3.20

04

4.20

04

1.20

05

2.20

05

3.20

05

4.20

05

1.20

06

2.20

06

3.20

06

4.20

06

1.20

07

2.20

07

3.20

07

4.20

07

c

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.

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.

CMASF

May 200827

27

The Dynamics of Real Disposable Income and Households’ The Dynamics of Real Disposable Income and Households’

Consumption Consumption ((quarter year on year growth, 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.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.

CMASF

May 200828

28

The Dynamics of Gross Profit and Investment in Fixed Capital The Dynamics of Gross Profit and Investment in Fixed Capital

((quarter year on year growth, 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.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.

CMASF

May 200829

29

The Growth of Money Supply (The Growth of Money Supply (broad definitionbroad definition) ) and and

Money DemandMoney Demand ( (monetary aggregatemonetary aggregate MM2, %2, %))

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.

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.

CMASF

May 200830

30

Banking System LiquidityBanking System Liquidity11 (%)(%)

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.

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.

CMASF

May 200831

31

External Debt of Private SectorExternal Debt of Private Sector((in percent of export of goods and servicesin 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.

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.

leading indicators of russian banking sector risks : methodology and examples

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