Download - Consequences of Basel II for the individual SME company H.A. Rijken Vrije Universiteit, Amsterdam International Conference Small business banking and financing:

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