Credit Risk Transfer and Systemic Risk. Study Case on Romania Coordonator: PhD. Professor Moisa ALTAR Student: Irina LUPA

Credit Risk Transfer and Systemic Risk. Study Case on Romania

Coordonator: PhD. Professor Moisa ALTARStudent: Irina LUPA

Introduction

Remarks by Chairman Alan Greenspan (1998) “In the context of bank capital adequacy, supervisors increasingly must be

able to assess sophisticated internal credit risk measurement systems, as well as gauge the impact of the continued development in securitization and credit derivative markets. It is critical that supervisors incorporate, where practical, the risk analysis tools being developed and used on a daily basis within the banking industry itself. If we do not use the best analytical tools available, and place these tools in the hands of highly trained and motivated supervisory personnel, then we cannot hope to supervise under our basic principle -- supervision as if there were no safety net.”

(1999) “Heavier supervision and regulation designed to reduce systemic risk would likely lead to the virtual abdication of risk evaluation by creditors of such entities, who could--in such an environment--rely almost totally on the authorities to discipline and protect the bank. The resultant reduction in market discipline would, in turn, increase the risks in the banking system, quite the opposite of what is intended. Such a heavier hand would also blunt the ability of U.S. banks to respond to crisis events. Increased government regulation is inconsistent with a banking system that can respond to the kinds of changes that have characterized recent years, changes that are expected to accelerate in the years ahead.”

Literature Review

• D. Baur and E. Joossens (2005)

• M. Davis and V. Lo (2001)

• Upper and Worms (2004)

• C. H. Furfine (2003)

• O. De Bandt and P. Hartmann (2000)

• F. Allen and D. Gale (2000)

SPV

Exposures portfolio

Defaults

Investor holdings scenarios

LOSS

Investors state of default

Inter-bank linkages matrix

Default of credit institutions by

contagion effect

The measurement

of the aggregated

impact

The conceptual model of the impact a securitization might have on financial stability

Data

Logit Model:

• Credit Register – the status of default of companies in debt (Dec. 2004 and

Dec. 2005)

• Ministry of Finance – financial statements of companies (Dec. 2004)

Securitization:

• Credit Register – value of loan exposures to companies (Dec. 2004)

Contagion:

• the matrix of inter-bank exposures (Dec. 2005)

• the level of own funds for each bank (Dec. 2005)

• the value of assets for each bank (Dec. 2005)

• the value of the risk-weighted assets for each bank (Dec. 2005)

Portofoliul institutiei de credit initiatoare

0

100

200

300

400

500

600

700

800

900

1000

1 601 1201 1801 2401 3001 3601 4201 4801 5401 6001 6601 7201 7801 8401 9001 9601 10201 10801 11401 12001 12601

Numar debitori

va

loa

re e

xp

un

eri

in

mil

ioa

ne

le

i

Individual value of exposures (mil

lei)

Number of exposures

% in the total

number

Cumulated value of the exposures

% in the total value of the loan portfolio

<1 11856 0.914674 1719.8538 0.124591

<2 482 0.037186 681.70502 0.049385

<3 180 0.013887 435.45774 0.031546

<5 185 0.014272 719.12073 0.052095

>=5 259 0.019981 10247.82 0.742383

Total 12962 1 13803.957 1

Loan portfolio structurestructure of the originator

Originator’s Loan Portfolio

Loan portfolio of the originator

The securitization will only include a fraction of the institution’s company loans portfolio. The securitized exposure amounts will affect the institution’s total risk-weighted assets (RWA). In order to determine the appropriate weighted exposure amount we develop a logit model that evaluates debtor’s probability of default (PD). The RWA will be calculated according to the provisions of Basel II.

Originator’s Loan Portfolio

Correlation (R) = 0.12 × (1 –EXP(-50 ×PD)) /(1 –EXP(-50)) + 0.24 ×[1 –(1 –EXP(-50 × PD))/(1 –EXP(-50))]

Maturity adjustment (b) = (0.11852 – 0.05478 × ln(PD))^2

Capital requirement (K) =[LGD×N[(1 – R)^-0.5×G(PD)+(R/(1 – R))^0.5×G(0.999)]–PDxLGD]x(1–1.5 x b)^-1×(1+(M– 2.5)×b)

Risk-weighted assets (RWA) = K x 12.5 x EAD

• LGD – loss given value has been considered to be 45%• M – the maturity is considered 2.5 yrs.

The variables, that have the highest discriminatory power, and are included in

the model are:

a) the term of recovery of receivables (trc);

b) the rate of repaying short-termed debt (tmps);

c) net cash-flow rate (tn12at).

Logit Model

iii

iii

xc

xc

iij

e

ecxyP

1

),,|1(

Dependent Variable: DEF

Method: ML - Binary Logit (Quadratic hill climbing)

Sample: 1 35512

Included observations: 35512

Convergence achieved after 9 iterations

Covariance matrix computed using second derivatives

Variable Coefficient Std. Error z-Statistic Prob.

TMPS -0.258722 0.029835 -8.671706 0.0000

TN12AT -2.360039 0.209011 -11.29144 0.0000

TRC 0.003279 0.000454 7.227984 0.0000

C -4.290552 0.124839 -34.36864 0.0000

Mean dependent var 0.013038 S.D. dependent var 0.113438

S.E. of regression 0.112832 Akaike info criterion 0.127369

Sum squared resid 452.0579 Schwarz criterion 0.128324

Log likelihood -2257.559 Hannan-Quinn criter. 0.127673

Restr. log likelihood -2469.342 Avg. log likelihood -0.063572

LR statistic (3 df) 423.5659 McFadden R-squared 0.085765

Probability(LR stat) 0.000000

Obs with Dep=0 35049 Total obs 35512

Obs with Dep=1 463

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Aroc=76.54%

0.72 0.73 0.74 0.75 0.76 0.77 0.78 0.79 0.8 0.810

20

40

60

80

100

120

140indice roc

AUROC value after 1000 bootstrap iterations

Roc curve of the model

Logit Model Statistics

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10

500

1000

1500

2000

2500PD estimat

Estimated Default Probabilities

5.31%

17.52%

29.21%

18.01%

14.55% 14.28%

1.12%

0%

5%

10%

15%

20%

25%

30%

35%

c1 c2 c3 c4 c5 c6 c7

Rating Scale

Distribution among debtors

Securitized exposures according to rating classes

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

0.900

1.000

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08

PD

Lei M

il.

The Holding of Securitizations

•junior securitizations

0 500 1000 1500 2000 2500 3000 3500 4000 45000

1

2

3

4

5

6

7

8

9x 10

9

a1

a2

a3

0 500 1000 1500 2000 2500 3000 3500 4000 45000

1

2

3

4

5

6

7

8x 10

10

x1

x2

x3

•senior securitizations

(1) sxxx nnn 321 , where sx ni ,0 , 3,1i , Nn and s represents a number of

percents of the entire issue. 100,0s

(2) jaaa nnn 321 , where ja ni ,...,2,1,0 , 3,1i , j represents the number of

percents of the entire issue. 100,0j , and 100 js

The initial shock in the system is generated by a random number of defaults in the securitized portfolio. The value of the absolute loss is generated by adding up exposures, in a descending default probability manner, until the total defaulted number is equal to that of the random number. The effect of the absolute loss on the value of the securitizations is:

0,100

max3

1

3

1

' La

aV

j

a

aa

ii

i

ii

ii

100,)

100(

100

100,

3

1

3

1

' jVL

x

xjVLV

s

x

x

jVLx

x

ii

i

ii

i

i

i

Where a’i and x’

i represent the value of the junior and senior securitizations after the loss, V is the portfolio value, L is the level of loss, s and j are those discussed above.

The Value of Securitizations after Inflicted Loss

0

10

20

30

0

10

20

30

0

0.2

0.4

0.6

0.8

Banca

X: 30Y: 23Z: 0.1717

X: 15Y: 6Z: 0.1728

X: 8Y: 6Z: 0.5183

X: 9Y: 23Z: 0.4121

X: 5Y: 23Z: 0.6868

Banca

The matrix of inter-bank exposures highlights the inter-bank assets of each credit institution to the other (n-1) banks in the system (left picture). The weight of inter-bank exposures compared to own funds show there are only a few banks that could default due to the loss of all their inter-bank exposures (right picture).

Inter-bank Linkages

0

10

20

30

0

10

20

30

0

1

2

3

4

5

6

x 104

•Inter-bank linkages determine an incomplete structure (according to Allen&Gale (2000))

•only 4 banks are exposed to insolvency if they lost all of their inter-bank exposures

If the inter-bank matrix is:

Then, the propagation of loss takes place the following way:

nnnjn

iniji

nj

IB

xxx

xxx

xxx

M

1

1

1111

, where xij is the gross exposure of bank “i” to bank „j”.

pn

x

p

x

p

x

p

x

p

x

p

x

p

x

p

x

p

x

MRP

nn

j

njn

n

in

j

iji

n

n

j

j

IB

1

1

1

1

11

1

11 , where xij has the same meaning as above and pj represents inter-bank liabilities of bank “j”.

Insolvency occurs whenever the insolvency indicator (IS) drops under 2%. When a bank defaults, it is removed from the matrix and analyses are continued the next round. Then IS is calculated for the remaining non-defaulted banks in the system.

Inter-bank Linkages

Results

0 500 1000 1500 2000 2500 3000 3500 4000 45000.5

1

1.5

2

2.5

3

s1

s1 minims1 maxim

s1 mediu

0 500 1000 1500 2000 2500 3000 3500 4000 45000.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

sever1

sever1 minimsever1 maxim

sever1 mediu

Average number of investor-banks that default in the first round (s1)

Average share of the system assets that default in the first round (atd)

Average number of banks that default by contagion in the following rounds (m1)

0 500 1000 1500 2000 2500 3000 3500 4000 45000

2

4

6

8

10

12

14x 10

-3

sever11


sever11 mediu

Average share of the system assets that default by contagion in the following rounds (sever1)

0 500 1000 1500 2000 2500 3000 3500 4000 45000

0.01

0.02

0.03

0.04

m11

m11 minimm11 maxim

m11 mediu

Indicator Average Median Standard Deviation

s1 2,623 2,906 0,467

atd 6,599% 6,773% 0,013

m1 0,005 0,001 0,011

sever1 0,026% 0,000% 0,001

0

10

20

30

0

10

20

30

0

0.5

1

1.5

2

2.5

X: 24Y: 6

Z: 0.6911

Banca

X: 30Y: 32Z: 0.4365

X: 8Y: 6Z: 1.037

X: 5Y: 17Z: 2.353

X: 5Y: 23Z: 1.538

Banca

Results

Inter-bank exposures are multiplied by 2

0 500 1000 1500 2000 2500 3000 3500 4000 45000

2

4

6

8

10

12x 10

-3

sever11


sever11 mediu



Only in some cases does the amount of inter-bank exposures exceed the credit institution’s level of own funds. The labeled values mark these institutions at risk.

0 500 1000 1500 2000 2500 3000 3500 4000 45000

0.02

0.04

0.06

0.08

0.1

0.12

m11

m11 minimm11 maxim

m11 mediu


s1 2,623 2,906 0,4671

atd 6,60% 6,77% 0,0131

m1 0,018 0,005 0,024

sever1 0,048% 0,006% 0,0011




There are more situations where the amount of interbank exposures exceed the credit institution’s level of own funds in this case. The labeled values mark these institutions at risk.

0 500 1000 1500 2000 2500 3000 3500 4000 45000

1

2

3

4

5

6

7

8

9x 10

-3

sever11


sever11 mediu

0

10

20

30

0

10

20

30

0

1

2

3

4

X: 24Y: 6Z: 1.037

Banca

X: 30Y: 32Z: 0.6547

X: 8Y: 6Z: 1.555

X: 8Y: 10Z: 1.086

X: 19Y: 32Z: 0.3273

X: 5Y: 17Z: 3.53

X: 5Y: 23Z: 2.308

Banca

0 500 1000 1500 2000 2500 3000 3500 4000 45000.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

m11

m11 minimm11 maxim

m11 mediu


s1 2,623 2,906 0,4671

atd 6,60% 6,77% 0,0131

m1 0,075 0,058 0,0348

sever1 0,082% 0,038% 0,0011

Results

Results

We assume that three credit institutions default simultaneously. This would mean that automatically all their loans on the inter-bank market will be considered losses to other banks. This is the maximum effect that can take place on the inter-bank market.

Inter-bank exposures are not multiplied



Indicator Maxim Value Average Standard Deviation

No. of defaulted institutions 2.000 0.117 0.3844% Defaulted Assets 0.007 0.000 0.001143





Conclusions

• credit risk transfer does not have major implications on the financial

stability, given that the credit institutions are capitalized.

• the inter-bank market is characterized by both a low degree of

connectivity (10.43%) and a low level of exposure amounts, issues that

will significantly reduce contagion risk, given that investors will default as

the result of very poor quality of the purchased securitizations. Generally,

the banks that are affected by contagion are the small ones and even so

the average number of banks that default is 0.005. The average

defaulted assets due to contagion is around 0.026% and the maximum

number of contagion rounds is 2.

Bibliography

• Baur, D., E. Joossens (2005), "The effect of credit risk transfer on financial stability", European

Commission, Joint Research Centre, Ispra (VA), Italy

• Davis, M.H.A, Lo, V. (2001) "Infectious defaults", Quantitative Finance, 1 (4). pp. 382-387

• De Bandt, O., P. Hartmann (2000), "Systemic Risk: A Survey", European Central Bank Working

Paper Series, Working Paper No. 35

• Furfine, C H (2003): "Interbank Exposures: Quantifying the Risk of Contagion", Journal of

Money,Credit and Banking, vol 35, pp 111-28.

• Hartmann, P., S. Straetmans, C. de Vries (2007), "Banking System Stability a Cross-Atlantic

Perspective", in "Risk Measurement and Systemic Risk"

• Upper, C., A. Worms (2002), "Estimating Bilateral Exposures in the German Interbank Market:

Is there a Danger of Contagion?", Discussion Paper no. 9, Economic Research Center of the

Deutsche Bundesbank

Thank you for your attention!

Documents

Credit Risk Transfer and Systemic Risk. Study Case on Romania Coordonator: PhD. Professor Moisa ALTAR Student: Irina LUPA