Transcript Modelling in Corporate Finance

Drivers of Credit Losses in
Australasian Banking
Slides prepared by
Kurt Hess
University of Waikato Management
School, Department of Finance
Hamilton, New Zealand
Topics
 Motivation
 Literature
review
 Credit loss data Australasia
 Methodological issues
 Results
 Conclusions
7-Jul-15
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2
Motivation
Stability and integrity of banking
systems are of utmost importance to
national economies
 Credit losses, or more generally, asset
quality problems have repeatedly
been identified as the ultimate trigger
of bank failures

[e.g. in Graham & Horner (1988), Caprio & Klingebiel (1996)]
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Motivation

Prudential supervisory agencies need to
understand drivers of credit losses in
banking system
– Validation of proprietary credit risk models
& parameters under Basel II

This is the first specific research of long
term drivers of credit losses for
Australian banking system
7-Jul-15
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4
Literature review
Two main streams of research that
analyse drivers of banks’ credit losses
(or more specifically loan losses):
1. Literature with regulatory focus looks
at macro & micro factors
2. Literature looks discretionary nature of
loan loss provisions and behavioural
factors which affect them
7-Jul-15
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Literature review

Behavioural hypotheses in the
literature on the discretionary nature of
loan loss provisions
– Income smoothing:
Greenawalt & Sinkey (1988)
– Capital management: Moyer (1990)
– Signalling: Akerlof (1970), Spence (1973)
– Taxation Management
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Literature review

Studies with global samples (using
commercial data providers):
–

Cavallo & Majnoni (2001),
Bikker & Metzemakers (2003)
Country-specific samples
–
–
–
7-Jul-15
Austria: Arpa et al. – (2001)
Italy: Quagliarello (2004)
Australia: Esho & Liaw (2002)
(in this APRA report the authors study level of
impaired assets for loans in Basel I risk buckets
for 16 Australian banks 1991 to 2001)
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Literature review

Research based on original published
financial accounts is rare (very large
effort to collect data).
– Pain (2003): 7 UK commercial banks &
4 mortgage banks 1978-2000
– Kearns (2004):
14 Irish banks, early 1990s to 2003
– Salas & Saurina (2002): Spain
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Credit Loss Data Australasia

The database includes extensive
financial and in particular credit loss
data for
– 23 Australian + 10 New Zealand banks
– Time period from 1980 to 2005
– Approximately raw 55 data elements per
institution, of which 12 specifically related
to the credit loss experience (CLE) of the
bank
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Credit Loss Data Australasia
Sample selection criteria
 Registered banks
 Must have substantial retail and/or
rural banking business
 Exclude pure wholesale and/or
merchant banking institutions
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Credit Losses and GDP Growth
(New Zealand Banks)
Provisioning/write-off behaviour correlated to macro factors
Annual
and Charges
to P&L
of Loan
Assets
Total Debt
StockWrite-offs
of Loan Loss
Provisions
as %as
of%Loan
Assets
(excludingBNZ
BNZand
andRural
RuralBank)
Bank)
(excluding
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
Charge
to to
P&L/
Avg
Loans
Charge
P&L/
Avg
Loans
(excl
BNZ,
Rural
Bk)
(excl BNZ, Rural Bk)
1991
1990
1989
1988
1987
1986
1985
1984
1983
1982
1981
0.0%
10.0%
1980
1.6%
2.5%
1.4%
1.2%
2.0%
1.0%
0.8%
1.5%
0.6%
0.4%
1.0%
0.2%
0.0%
0.5%
Net
Write
offs/
Avg
Loans
Net
Write
offs/
Avg
Loans
(excl
BNZ,
Rural
Bk)
(excl BNZ, Rural Bk)
5.0%
0.0%
-5.0%
7-Jul-15
GDP YoY% Real
Note: chart forKurt
NZ Hess,
Bank WMS
sub-sample only
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Credit Loss Data Australasia
4.0%
(on average loans, annualized)
AU Westpac, 1993,
3.7%
AU ANZ
3.5%
AU ANZ, 1993,
2.6%
AU CoWthBk
3.0%
AU NAB
2.5%
AU CoWthBk,
1993, 2.5%
AU Westpac
2.0%
1.5%
1.0%
0.5%
0.0%
1980
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1985
1990
1995
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2000
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Drivers of Credit Losses in
Australasian Banking
Methodology
Principal Model
CLE  Const    x
K
it
zk
k 1 s  0
ks ki ( t  s )
q
   s CLEi (t  s )  uit ;
s 1
i  1,.....,n; t  q  1,....,T
CLEit
xkit
uit

n
T
K
zk
q
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Credit loss experience for bank i in period t
Observations of the potential explanatory variable k for bank i
and period t
Random error term with distribution N(0,),
Variance-covariance matrix of it error terms
Number of banks in sample
Years in observation period
Number of explanatory variables
Maximum lag of the explanatory variable k of the model
Maximum lag of the dependent variable of the model
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Principal Model


Principal model on previous slide
allows for many potential functional
forms.
There are choices with regard to
– Dependent CLE proxy
– Suitable drivers of credit losses and lags
for these drivers
– Estimation techniques
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Determinants of Credit Losses
Macro Factors (1)
Real GDP growth
-ve
Ability of borrowers to service
debt determined by the
economic cycle.
Unemployment
rate
+ve
Unemployment rate not only
reflects the business cycle
(like GDP growth) but also
longer term and structural
imbalances in economy.
Liabilities of
households/firms
as % of disp.
income
+ve
The more households and
firms in the system are
indebted, the more financially
vulnerable they will be.
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Determinants of Credit Losses
Macro Factors (2)
Asset prices /
interest rates
Housing price index
(changes)
-ve
Return leading share -ve
indices
Change real/nominal +ve
interest rates
only CPI growth
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Disturbances in the asset
markets can impair the value
of banks’ assets both directly
and indirectly (i.e. through
reduced collateral values).
Experience shows that
especially the property sector
and the share markets may
play a critical role in triggering
losses in the banking system.
Similar effects are expected in
a volatile interest rate
environment.
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Determinants of Credit Losses
Bank Specific Factors (1)
Past credit
expansion
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+ve
or
-ve
Fast growth of the loan
portfolio is often associated
with subsequent loan losses.
Alternatively, a slow growing
loan portfolio may be caused
by a weak economy and thus
increase CLE.
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Determinants of Credit Losses
Bank Specific Factors (2)
Pricing of risks
( net interest margins)
Characteristic of
lending portfolio
(share of housing
loans)
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+ve/
(-ve)
-ve
A bank’s deliberate choice to
lend to more risky borrowers
is likely reflected in higher
interest margins. Lower past
margins might induce greater
risk-taking by bank
The share of comparably
lower risk housing loans as
% of loans proxies the risk
characteristic of the bank’s
loan portfolio.
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Determinants of Credit Losses
Bank Specific Factors (3)
Diversification
-ve
Market power
+ve/
(-ve)
Cost efficiency
+ve/
(-ve)
(cost-income ratio)
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A bank’s assets in proportion
to the overall banking system
assets provides a crude proxy
for loan portfolio
diversification or market
power
Inefficient banks can be
expected to suffer greater
credit losses. Alternatively,
such banks could maintain an
expensive credit monitoring
procedure and will thus
exhibit lower credit losses.
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Determinants of Credit Losses
Bank Specific Factors (4)
Income smoothing
(Earnings before provisions &
taxes as % of assets)
Capital
management
(Capital measured as tier 1 or
tier 1+2 capital as % of risk
weighted assets)
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+ve
-ve
Some literature finds evidence
of banks using discretionary
provisions to smooth earnings
for a variety of motivations.
General provisions count
towards Basel I minimum
capital and weaker banks
might thus be tempted to
engage in capital
management through
provisioning.
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Pooled regression model as per
equation 1 in paper

Dependent
– Impaired asset expense as CLE proxy

Determinants (as per table next slide)
– Alternative macro factors: GDP growth,
unemployment rate
– Alternative asset shock proxies: share
index, house prices
– Misc. bank-specific proxies
– Bank past growth
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Bankspecific
Aggregate
Dependent variables in model
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Variable
Description
GDPGRW
UNEMP
Macro state proxies: GDP growth or
level/change Unemployment rate
0 to -2
RET_SHINDX Asset price shock proxies: Return
HPGRW
share index or change house prices
0 to -2
CPIGRW
Change CPI
0 to -2
SH_SYSLNS
Share of system loans (size proxy)
NIM
Net interest margin
0 to -2
CIR
Cost-income ratio
0 to -2
EBTP_AS
Pre-provision/tax earnings / assets
0 to -2
ASGRW
Bank past asset growth
0 to -4
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Lags (yrs.)
0
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Drivers of Credit Losses in
Australasian Banking
Empirical results
Results macro state factors
see Table 8, 9,10 in paper
GDP growth (GDPPGRW), change and
level of the unemployment rate
(UNEMP, DUNEMP) have expected
effect (not all lags significant)
 Unemployment with best explanatory
power for overall sample

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Results macro state factors (2)
see Table 8, 9,10 in paper

Country-specific differences between
Australia and New Zealand
– Australia’s results show much greater
sensitivities to GDP growth (see Table 9)
– New Zealand results are less significant
and effects of GDP and UNEMP seem
more delayed
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Results asset price factors
see Table 8, 9,10 in paper
Contemporaneous coefficient of share
index return negative & significant for
overall and Australia. Less significant
for NZ.
 Housing price index has less sigificance
Intuition: early 90s crises not rooted in
particular problems of the housing
sector

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Results CPI growth
see Table 8, 9,10 in paper

Positive, but not significant coefficients for
most regressions, i.e. inflationary pressure
tends to lift credit losses
 Contemporaneous term negative and
significant for Australian sub-sample, in line
with evidence elsewhere that inflation may
lead to temporary improvement of borrower
quality (Tommasi, 1994)
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Results size proxy
see Table 8, 9,10 in paper
Higher level of provisioning for larger
banks – no significance of coefficients,
however
 Intuition: portfolios of smaller institutions
often dominated by (comparably) lower
risk housing loans

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Results net interest margin
see Table 8, 9,10 in paper

Generally negative, contemporaneous and
2yr lagged term significant, i.e.
– Lower past margins lead to higher subsequent
losses (induce risk taking)
– Difficult to explain contemporaneous negative
term

Inconclusive results also in comparable
studies, e.g. Salas & Saurina (2002) for
Spain
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Results net interest margin (2)
see Table 8, 9,10 in paper

Endogenous nature of net interest margins as
postulated by Ho & Saunders (1981) dealership
model. Spread increases with …
–
–
–
–

Market power (inelastic demand)
Bank risk aversion
Larger size of transactions (loans/deposits)
Interest rate volatility
Net interest margins may thus control for other
bank specific & market characteristics
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Results cost efficiency (CIR)
see Table 8, 9,10 in paper
High and increasing cost income ratios
are associated with higher credit losses
 Results reject alternative hypothesis
that banks are inefficient because they
spend to much resources on borrower
monitoring
 Not surprising as “gut feel” would tell
that excessive monitoring might not pay

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Results earnings proxy
see Table 8, 9,10 in paper
Very clear evidence of income
smoothing activities, i.e. banks increase
provisions in good years, withhold them
in weak years.
 Confirms similar results found in many
other studies

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Results past bank growth
see Table 8, 9,10 in paper
Clear evidence of the fast growing
banks faced with higher credit losses in
future (lags beyond 2 years)
 Managers seem unable (or unwilling) to
assess true risks of expansive lending
 Much clearer results than in other
studies. Possibly due to test design with
longer lags considered.

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Conclusions



Model presented here is very suitable
for assessing general / global effects
on impaired assets in the banking
sector
The dynamics of this transmission
seems to differ among systems
A study of particular effects might thus
call for alternative models
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Conclusions (2)

Income smoothing is a reality, possibly
also with new tighter IFRS
provisioning rules as this ultimately
remains a discretionary managerial
decision
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Conclusions (3)


Use data base for comparative studies
of alternative CLE dependent
variables
First results show that they (in part)
correlate rather poorly which means
there must be caution comparing
results of studies unless CLE is
defined in exactly the same way
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Credit Loss Experience of
Australasian Banks
Back-up Slides
Basel II Pillars
 Pillar
1:
– Minimum capital requirements
 Pillar
2:
– A supervisory review process
 Pillar
3:
– Market discipline (risk disclosure)
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Basel II Pillars
Pages in New Basel Capital Accord (issued June 2004)
General:
6 of 216
pages
Pillar 2
Supervisory
Review
Process:
15 of 216
pages
Pillar 1
Minimum
Capital
Requirements
179 of 216
pages
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Pillar 3 Market
Discipline:
16 of 216
pages
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Pro Memoria: Calculation Capital
Requirements under Basel II
Unchanged
Total Capital
Credit Risk + Market Risk + Operational Risk
Significantly
Refined
Relatively
Unchanged
New
 8%
(Could be set higher
under pillar 2)
Source: slide inspired by PWC presentation slide retrieved 27/7/2005 from
http://asp.amcham.org.sg/downloads/Basel%20II%20Update%20-%20ACC.ppt ,
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Basel II – IRB Approach
Two approaches developed for calculating
capital minimums for credit risk:
 Standardized Approach (essentially a slightly
modified version of the current Accord)
 Internal Ratings-Based Approach (IRB)
– foundation IRB - supervisors provide some inputs
– advanced IRB (A-IRB) - institution provides inputs
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Basel II – IRB Approach

Internal Ratings-Based Approach (IRB)
– Under both the foundation and advanced
IRB banks are required to provide
estimates for probability of default (PD)
– It is commonly known that macro factor are
the main determinants of PD
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Primer Loan Loss Accounting
Beginning of period
Transactions during period
End of period
Profit & loss statement (P&L)
- Bad debt charge
Loan balance
Gross loan amount
- Provisions initial
balance
Net loan amount
Provision account
Provisons initial balance
+ New provisions made
- Debt write-offs
+ Recovery of debt
previously written off
Provisons final balance
Loan balance
Gross loan amount
- Provisions final
balance
Net loan amount
Gross loan account
Opening balance
-/+ Loans issued/repaid
- Debt write-offs
+ Recovery of debt
previously written off
Ending balance
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Primer Loan Loss Accounting
Initiation of loan
Potential loan loss
identified
Loan account
1,000
950
+50
Loan account
Loan account
General
provision
recognized
1,000 50 +350
600
Loan write-off
(derecognition)
Additional
specific
provisons
1,000
- 400
600
Cash account
50
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Loan account
600
+ 100
-
+ 700
Cash account
1,000
Bad debt provision
expense
400
- 400
Loan recovery
700
Bad debt provision
expense
350
Bad debt provision
recovery income
-
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100
45
Credit Loss Data Australasia
NZ$ million
BNZ books bad debt
credits 1994-1997
1
2 00
9
1 99
7
1 99
5
Bad debt charge to P&L
1 99
8m
31
0
1 99
1 99
8
1 98
6
Net write-offs
1 98
1 98
4
1,400
1,200
1,000
800
600
400
200
0
(200)
BNZ 1984 - 2002
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Credit Loss Data Australasia
Banks in sample
AUSTRALIA: Adelaide Bank, Advance Bank, ANZ, Bendigo
Bank, Bank of Melbourne, Bank West, Bank of Queensland,
Commercial Banking Company of Sydney, Challenge Bank,
Colonial State Bank, Commercial Bank of Australia,
Commonwealth Bank, Elders Rural Bank, NAB, Primary
Industry Bank of Australia, State Bank of NSW, State Bank
of SA, State Bank of VIC, St. George Bank, Suncorp-Metway,
Tasmania Bank, Trust Bank Tasmania, Westpac
NEW ZEALAND: ANZ National Bank, ASB, BNZ,
Countrywide Bank, NBNZ, Rural Bank, Trust Bank NZ, TSB
Bank, United Bank, Westpac (NZ)
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Credit Loss Data Australasia
Data issues
 Macro level statistics
– Differing formats between NZ and Australia
e.g. indebtedness of households / firms
– House price series back to 1986 only for
Australia
– Balance sheets of M3 institutions only back
to 1988 for New Zealand (use private
sector credit statistics instead)
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Credit Loss Data Australasia
Data issues (2)
 Micro / bank specific data
– Lack of reporting limits choice of proxies
(particularly through the very important
crisis time early 1990)
– Comparability due to inconsistent reporting
(e.g. segment credit exposures)
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Measuring CLE

Dedicated nature of database allows tests
for many proxies for a bank’s credit loss
experience (CLE)
–
–
–
–
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Level of bad debt provisions, impaired assets,
past due assets
Impaired asset expense (=provisions charge to
P&L)
Write-offs (either gross or net of recoveries)
Components of above proxies, e.g. general or
specific component of provisions (stock or
expense)
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Measuring CLE
250
Histogram of selected CLE proxies
200
150
100
50
Im paired asset expense / loans
Im p. asset expense / net interest
0
7-Jul-15
Median +2 StD
and more
Median +1.75 StD to
Median +2 StD
Im paired assets / assets
Median +1.5 StD to
Median +1.75 StD
Median +1.25 StD to
Median +1.5 StD
Stock of provisions / loans
Median +1 StD to
Median +1.25 StD
Median +0.75 StD to
Median +1 StD
Median +0.5 StD to
Median +0.75 StD
Net w rite-offs / loans
Median +0.25 StD to
Median +0.5 StD
Median to
Median +0.25 StD
Median
Median -0.25 Std to
Median
Median -0.25 StD
and less
Im p. asset exp. / gross interest
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Pooled observations of
Australian and NZ
Banks 1980 - 2005
51
Credit Loss Experience of
Australasian Banks
Selected References
Selected References
Bikker, J. A., & Metzemakers, P. A. J. (2003).
Bank Provisioning Behaviour and
Procyclicality, De Nederlandsche Bank Staff
Reports, No. 111.
Caprio, G., & Klingebiel, D. (1996). Bank
insolvencies : cross-country experience.
Worldbank Working Paper WPS1620.
Cavallo, M., & Majnoni, G. (2001). Do Banks
Provision for Bad Loans in Good Times?
Empirical Evidence and Policy Implications,
World Bank, Working Paper 2691.
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53
Selected References
Esho, N., & Liaw, A. (2002). Should the
Capital Requirement on Housing Lending be
Reduced? Evidence From Australian Banks.
APRA Working Paper(02, June).
Graham, F., & Horner, J. (1988). Bank Failure:
An Evaluation of the Factors Contributing to
the Failure of National Banks, Federal
Reserve Bank of Chicago.
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Selected References
Kearns, A. (2004). Loan Losses and the
Macroeconomy: A Framework for Stress
Testing Credit Institutions’ Financial WellBeing, Financial Stability Report 2004.
Dublin: The Central Bank & Financial
Services Authority of Ireland.
Pain, D. (2003). The provisioning experience
of the major UK banks: a small panel
investigation. Bank of England Working
Paper No 177, 1-45.
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Selected References
Salas, V., & Saurina, J. (2002). Credit
Risk in Two Institutional Regimes:
Spanish Commercial and Savings
Banks. Journal of Financial Services
Research, 22(3), 203 - 224.
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