Consequences of Basel II for the individual SME company

1
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

2
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

3
For unexpected losses banks must hold capital
capital requirements which are calculated within
the VAR concept
K
Prob lt 1 - C
0
0
PD?LGD
PD?LGD
Assumption no fat tails in the distribution
(Mandelbrot)
4
Capital requirements are determined by the
following formulas

• 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

5
Relationship between PD and capital requirements
differs by approach
6
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 K?required equity rate of return
  operational costs
• LGD ? PD K?15 30bp

7
Expected losses are negligible for ratings above
BBB/Baa1
Expected annual loss PDLGD
8
Basel I bank spread PDLGD KBasel I15 30bp
Basel I banking spread (in practice K 1012 )
Expected Loss PDLGD
9
A gap exists between Basel I spread and market
spread
Spread in bond markets 82 04
Basel I banking spread
Expected Loss PDLGD
FRICTION
10
Basel II bank spread PDLGD KBasel II15
30 bp
Spread in bond markets 82 04
Basel II banking spread
Basel I banking spread
Expected Loss PDLGD
11
Average market spread 82 04 fully overlaps
the Basel II banking spread

• Possible conclusions
• 1. The Basle II model (KBasel II15) 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.

12
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
  investors 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)

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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 SP
  ratings are for all firms in the Compustat
  database
• ? distribution of SP ratings for SME vs. larger
  companies
• 3. Basel II capital requirements and Basel II
  banking spreads are calculated for 7 banking
  subportfolios
• ? Basel II banking spreads for SME vs. larger
  companies taking into account the companies life
  cycle

14
To make the Compustat banking portfolio of more
interest to other countries, 7 subportfolios by
type and Size are formed
Advantages of the COMPUSTAT database (compared to
databases at banks) 1. complete it contains all
defaults in a specific market 2. accurate it
includes all information accurately 3. it covers
a long period 1970 - 2001
15
Credit scoring models a lot a freedom to
specify these models
modeltype logit regression methodology
16
Accuracy of credit scoring models is measured by
the ACR value
ACR shaded surface / 0.5
Low credit quality
High credit quality
17
Three variables profitability, solvability and
Size are dominant in credit scoring models
18

Equivalent SP ratings are defined based on
ranked credit scores
16 SP rating classes
Credit score ranking
Step 1 Observations with a known SP rating are
ranked by credit score. Step 2 16 equivalent
SP ratings are defined with the same
distribution as the actual SP rating
distribution Step 3 For each equivalent SP
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 SP ratings of all other observations
are determined
- - - - - - - - - - - - - - - - - - - - - - - - -
- - - - - - - - - - - - - - - - - -
Cmax
NA
NA
Cmin
19
For stable companies LE companies are slightly
more creditworthy than SME companies
20
For innovative companies differences in credit
risk between LE and SME companies are larger
21
As expected loss-making companies are centered
at low ratings
22
Differences between LE1 and LE2/SME become smaller
0.12
BBB
BBB
0.41
Default rate
BBB-
0.66
BB
1.20
BB
2.23
23
Stable companies Size does matter in terms of
creditworthiness
BBB BBB BBB- BB BB BB-
24
The lower profitability level and higher
volatility level is not fully compensated by more
conservative financing
25
For all credit scoring models the average
equivalent SP ratings are lower in the SME
segment
9 BBB, 8 BBB-, 7 BB, 6 BB, 5 BB-, 4
B, 3 B
26
Debt financing costs are (will be?) 120 bp higher
for SME companies compared to large companies



Computed on he basis of equivalent SP rating
distribution and Moodys statistics
27
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
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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

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(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
  banks 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