Consequences of Basel II for the individual SME company H.A. Rijken Vrije Universiteit, Amsterdam...

Consequences of Basel II for the individual SME company

H.A. Rijken

Vrije Universiteit, Amsterdam

International Conference

Small business banking and financing: a global perspective

University Cagliari, NYU Stern School of Business, Leeds University Business School University of Trieste, European Commission

Cagliari, 25-26 May 2007

Content

• Spreads from a Basel II banking perspective versus spreads in the bond market

• Creditworthiness of SME vs. larger companies

• Consequences for the spread of SMEs vs larger companies

• Consequences for the individual SME company

For unexpected losses banks must hold capital: capital requirements which are calculated within the VAR concept

PDLGD

0PDLGD

0

KProb < 1 - C

Assumption: no fat tails in the distribution (Mandelbrot)

Capital requirements are determined by the following formulas

EADLGD - 1

)()(N N loan per capital K

12/1-1

CNPD

• PD is the default probability

• C is the confidence level in the VAR calculations is the default correlation among companies. This correlation is assumed to be

lower for SME companies leading to a 20% reduction in capital requirements

• Parameters PD, LGD, M have to be determined by historical data

45

)5(1%4

)50exp(1

)50exp(11%24

)50exp(1

)50exp(1%12)(

OmzetPDPDPD

0%

5%

10%

15%

20%

25%

30%

35%

aaa

17

aa+ 1

6

aa- 1

5aa

14a+

13

a 12

a- 1

1

bbb+

10

bbb

9

bbb-

8

bb+ 7

bb 6

bb-

5b+

4 b 3

b- 2

ccc

1cc

0

equivalent S&P rating

capi

tal

requ

irem

ents

Basle II IRB approach

Basle II IRB approach with SME adjustment

Basle II standard approach

Basle I

Relationship between PD and capital requirements differs by approach

Spread calculation from a banking perspective

Spread = Interest income – financing costs

= Expected loss + Costs related to unexpected loss + operational costs

= Expected loss + costs of equity + operational costs

= LGD PD + Krequired equity rate of return + operational costs

= LGD PD + K15% + 30bp

0

100

200

300

400

500

B2

B1

Ba3

Ba2

Ba1

Baa

3B

aa2

Baa

1 A3

A2

A1

Aa3

Aa2

Aa1

/Aaa

credit rating (Moody's)

basi

spoi

nts

Expected losses are negligible for ratings above BBB/Baa1

Expected annual loss: PD×LGD

0

100

200

300

400

500

B2

B1

Ba3

Ba2

Ba1

Baa

3B

aa2

Baa

1 A3

A2

A1

Aa3

Aa2

Aa1

/Aaa


basi

spoi

nts

Expected Loss: PD×LGD

Basel I banking spread (in practice K ≈ 10–12 %)

Basel I bank spread = PD×LGD + KBasel I×15% + 30bp

0

100

200

300

400

500

B2

B1

Ba3

Ba2

Ba1

Baa

3B

aa2

Baa

1

A3

A2

A1

Aa3

Aa2

Aa1

/Aaa


basi

spoi

nts

Spread in bond markets: ‘82 – ‘04

FRICTION


Basel I banking spread

A gap exists between Basel I spread and market spread

Basel II bank spread = PD×LGD + KBasel II×15% + 30 bp

0

100

200

300

400

500

B2

B1

Ba3

Ba2

Ba1

Baa

3B

aa2

Baa

1

A3

A2

A1

Aa3

Aa2

Aa1

/Aaa


basi

spoi

nts

Spread in bond markets ‘82 – ‘04


Basel I banking spread

Basel II banking spread

Average market spread 82 – ’04 fully overlaps the Basel II banking spread

Possible conclusions

1. The Basle II model (KBasel II×15%) is a good proxy for the risk premium investors and bankers demand for unexpected losses.

2. The Basle II model is a good proxy how bankers price their debt. Bankers are dominant in setting the price in debt markets.

3. Basel II model parameters are chosen in such a way that a perfect match shows up between average market spreads and Basel II model spreads.

Perhaps the Basel II banking spread will become a standard in financial markets

Basel II model is

- relative easy to calculate

- it is a standard set by BIS

- the relative simple VAR approach fits with the investor’s intuition how to quantify (and price) credit risk

Alternative explanations for the relative high spreads for A – AAA bonds

- a liquidity premium of 45 bp (De Jong and Driessen, 2005)

- a high asset volatility, however structural models fail to explain the gap (for discussion see Longstaff, 2005)

How will banks set interests rates in a (new) Basel II environment, with a special focus on SME versus larger companies?

Consequences of Basel II are simulated with a “banking portfolio” consisting all firms available in the Compustat database

1. The Compustat data is used to estimate a bankruptcy prediction model→ sensitivity credit scoring models to Size

2. Based on ranked credit scores equivalent S&P ratings are for all firms in the Compustat database→ distribution of S&P ratings for SME vs. larger companies

3. Basel II capital requirements and Basel II banking spreads are calculated for 7 banking subportfolio’s→ Basel II banking spreads for SME vs. larger companies taking into account the companies’ life cycle

To make the Compustat banking portfolio of more interest to other countries, 7 subportfolio’s by type and Size are formed

type stable/mature

companies

innovative

companies

Loss-making

companies

criteria RE/TA > 0 en EBIT > 0

RE/TA < -0.5 and E/D > 2

RE/TA < -0.5 and E/D < 2

segment LE1 LE2 SME LE SME LE SME

turnover in mln Euro > 500 50 .. 500 < 50 > 50 < 50 > 50 < 50

Number of observations

26252 27059 8948 1267 5584 3402 4817

Advantages of the COMPUSTAT database (compared to databases at banks)1. complete: it contains all defaults in a specific market2. accurate: it includes all information accurately3. it covers a long period 1970 - 2001

ti

ti

ti scorecreditp

p,

,

, )1

log(

Credit scoring models: a lot a freedom to specify these models

modeltype: logit regression methodology

)ln(7654

321

TAEBIT

INT

TA

Turnover

D

ETA

EBIT

TA

RE

TA

WKscorecredit

0

0.2

0.4

0.6

0.8

1

0.0 0.2 0.4 0.6 0.8 1.0

ranking observations by credit score

Sha

re o

f def

aults

ACR = shaded surface / 0.5

Low creditquality

High creditquality

Accuracy of credit scoring models is measured by the ACR value

ACR

Model /

Credit scoring variables LE and

SME LE SME

Public model: only accounting and market value information

1 WK/TA, CA/CP, RE/TA, EBIT/TA, E/D, Turnover/TA, INT/EBIT, ln(TA)

75.0% 81.4% 60.7%

2 EBIT/TA, E/D, ln(TA) 74.1% 80.5% 58.5%

3 E/D, ln(TA) 69.2% 77.8% 47.4%

4 E/D 54.7% 70.8% 46.4%

Private model: only accounting information

5 WK/TA, CA/CP, RE/TA, EBIT/TA, BE/TA, Turnover/TA, INT/EBIT, ln(TA)

70.5% 78.2% 55.5%

6 EBIT/TA, BE/TA, ln(TA) 70.3% 78.2% 53.9%

7 BE/TA, ln(TA) 64.9% 72.7% 46.8%

8 BE/TA 54.5% 65.9% 47.5%

Three variables: profitability, solvability and Size are dominant in credit scoring models

16 S&P rating classes

Credit score ranking

NA

-------------------------------------------

NA

Step 1: Observations with a known S&P rating are ranked by credit score.

Step 2: 16 equivalent S&P ratings are defined with the same distribution as the actual S&P rating distribution

Step 3: For each equivalent S&P rating class the maximum and minimum credit score is determined: [Cmin(R), Cmax(R)].

Step 4: On the basis of these intervals [Cmin, Cmax] the equivalent S&P ratings of all other observations are determined

Cmax

Cmin

Equivalent S&P ratings are defined based on ranked credit scores

For stable companies LE companies are slightly more creditworthy than SME companies

0%

4%

8%

12%

16%aa

a 17

aa+

16aa

- 15

aa 1

4a+

13

a 12

a- 1

1bb

b+ 1

0bb

b 9

bbb-

8bb

+ 7

bb 6

bb-

5b+

4 b 3

b- 2

ccc

1cc

0


freq

uenc

y

rating distribution SME segment

rating distribution LE segment

For innovative companies differences in credit risk between LE and SME companies are larger

0%

4%

8%

12%

16%

20%

aaa

17

aa+

16

aa- 1

5

aa 1

4

a+ 1

3a

12a-

11

bbb+

10

bbb

9

bbb-

8

bb+

7bb

6

bb-

5b+

4 b 3

b- 2

ccc

1cc

0


freq

uenc

y



As expected loss-making companies are centered at low ratings

0%

4%

8%

12%

16%

20%

24%

28%

32%

aaa

17

aa+

16

aa- 1

5

aa 1

4

a+ 1

3a

12a-

11

bbb+

10

bbb

9

bbb-

8

bb+

7bb

6

bb-

5b+

4 b 3

b- 2

ccc

1cc

0


freq

uenc

y



6,000

7,000

8,000

9,000

10,000

1980 1985 1990 1995 2000

year

equi

vale

nt S

&P

ratin

g

LE1 segment sales > 500 mln Euro

LE2 segment sales 50 .. 500 mln Euro

SME segment sales < 50 mln EuroBBB+

BBB-

BB+

BBB

BB

De

fau

l t r a

t e

0.12%

0.41%

0.66%

1.20%

2.23%

Differences between LE1 and LE2/SME become smaller

5

6

7

8

9

10

sales in mln Euro (indexed to 2001)

Equ

ival

ent S

&P

rat

ing

1980 - 1990

1991 - 2001

BBB+

BBB

BBB-

BB+

BB

BB-

Stable companies: Size does matter in terms of creditworthiness

The lower profitability level and higher volatility level is not fully compensated by more conservative financing

stable/mature

companies

innovative

companies

Loss-making

companies

LE1 > 500

LE2 50 ..500

SME < 50

LE > 50

SME < 50

LE > 50

SME < 50

E/D 1.28 1.60 1.73 3.03 3.38 0.39 0.73

D/TA 0.57 0.48 0.40 0.43 0.39 0.89 0.79

WK/TA 0.20 0.31 0.34 0.35 0.35 0.07 0.05

RE/TA 0.27 0.22 0.17 -0.86 -1.94 -0.94 -1.81

EBIT/TA 0.104 0.085 0.045 -0.02 -0.30 -0.04 -0.22

stand. deviation EBIT/TA individual company

0.045 0.072 0.106 0.118 0.245 0.097 0.188

For all credit scoring models the average equivalent S&P ratings are lower in the SME segment

stable/mature

companies

innovative

companies

Loss-making

companies

model

LE1 > 500

LE2 50 ..500

SME < 50

LE > 50

SME < 50

LE > 50

SME < 50

Credit scoring model 1 9.22 8.26 7.69 8.14 5.42 3.06 2.20






average 9.29 7.92 7.21 8.60 6.09 3.40 2.61

9 = BBB, 8 = BBB-, 7 = BB+, 6 = BB, 5 = BB-, 4 = B+, 3 = B

Debt financing costs are (will be?) 120 bp higher for SME companies compared to large companies

stable/mature

companies

innovative

companies

Loss-making

companies

LE1 > 500

LE2 50 ..500

SME < 50

LE > 50

SME < 50

LE > 50

SME < 50

Probabilities bankruptcy / defaults

bankruptcy (one year) 0.17% 0.39% 0.54% 0.32% 0.61% 3.23% 2.55% default 1 (one year) 1.2% 2.4% 3.3% 1.7% 4.8% 10.1% 14.6% LGD (one year) 3 31.8% 32.5% 33.4% 34.7% 38.4% 41.0% 41.3%

Capital requirements costs (credit scoring model 1)

Standard approach 6.6% 7.4% 7.8% 7.5% 9.7% 11.3% 11.7% IRB with SME corr. 5.8% 7.5% 7.5% 6.9% 9.8% 17.7% 19.0% IRB without SME corr. 5.8% 7.5% 8.7% 6.9% 11.6% 17.7% 22.0%

Debt financing costs

IRB with SME corr. 0.9% 1.1% 1.1% 1.0% 1.5% 2.7% 2.8%

PD LGD (one year) 0.4% 0.8% 1.1% 0.6% 1.8% 4.1% 6.0%

Bond market 1.7% 2.4% 2.9% 2.1% 4.0% 6.6% 8.7%

**

** Computed on he basis of equivalent S&P rating distribution and Moody’s statistics

Standard deviation

rating(model x) – rating(model y)

stable/mature

companies

innovative

companies

Loss-making

companies

Credit scoring model x

Credit scoring model y

LE1 > 500

LE2 50 ..500

SME < 50

LE > 50

SME < 50

LE > 50

SME < 50

model 1 model 2 1.33 1.46 1.79 1.59 1.45 1.16 0.78

model 5 model 6 1.68 1.70 1.77 1.58 1.49 1.32 1.22

model 1 model 5 2.22 2.36 2.73 3.04 2.62 1.97 2.06

model 2 model 6 2.32 2.78 3.17 3.15 2.85 2.46 2.23

average 1.89 2.08 2.37 2.34 2.10 1.73 1.57

For an individual perspective the credit scoring model is relevant, not that much from a portfolio perspective

standard deviations van 2 notch steps can make the difference between a B and a BB+ rating, a difference of 300bp in credit spread

Conclusions for the credit risk of SME company

• Companies’ credit risk depends on Size, below an annual sales of 100 mln.

• Accuracy of credit risk models is lower for SME companies.

• Lower profitability and higher earnings volatility make SME firms more vulnerable. More conservative financing only partly compensates for this.

• The creditworthiness of SME companies is more sensitive to the credit cycle.

• If the Basel II model becomes the standard in credit pricing than

- innovative SME companies will face higher costs of debt

- loss making companies might go bankrupt more quickly

• (Internal) credit rating of an SME depends strongly on the specific details of the credit rating system a bank puts in place.

• management of credit risk by companies should get a higher priority

(Part of) Relationship banking is going to disappear in the SME segment

• Internal rating systems are based on “hard” quantitative facts and become more influential in bank’s lending decisions

• These systems offer more transparency within the bank and for the financial authorities (client as well ?)

• Little room for the relationship manager to negotiate with the client

• Relative high costs in the SME segment can be reduced

• They have to be reduced to regain the Basel II investment costs

Consequences of Basel II for the individual SME company H.A. Rijken Vrije Universiteit, Amsterdam...

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