Modelling in Corporate Finance by HC120809014635


									 Drivers of Credit Losses in
   Australasian Banking

         Slides prepared by
             Kurt Hess
University of Waikato Management
 School, Department of Finance
     Hamilton, New Zealand

 Motivation
 Literature review
 Credit loss data Australasia
 Methodological issues
 Results
 Conclusions

9-Aug-12           Kurt Hess, WMS       2
 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)]

9-Aug-12                      Kurt Hess, WMS                       3
   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

9-Aug-12               Kurt Hess, WMS                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

9-Aug-12            Kurt Hess, WMS         5
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

9-Aug-12              Kurt Hess, WMS          6
Literature review
     Studies with global samples (using
      commercial data providers):
    –      Cavallo & Majnoni (2001),
           Bikker & Metzemakers (2003)
     Country-specific samples
    –      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)
9-Aug-12                    Kurt Hess, WMS                     7
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

9-Aug-12              Kurt Hess, WMS           8
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
9-Aug-12               Kurt Hess, WMS             9
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

9-Aug-12           Kurt Hess, WMS       10
  Credit Losses and GDP Growth
                                    (New Zealand Banks)
Provisioning/write-off behaviour correlated to macro factors

                                      Total Stock of Loan and Provisions as % of Loan Assets
                                    Annual Debt Write-offsLossCharges to P&L as % of Loan Assets
                                                     (excluding BNZ and Rural Bank)
                                                   (excluding BNZ and Rural Bank)
                                             Net Write offs/ Avg Loans
                                               Net Write offs/ Avg Loans                                               Charge P&L/ Avg Loans
                                                                                                                     Charge to to P&L/ Avg Loans
                                             (excl BNZ, Rural Bk)
                                               (excl BNZ, Rural Bk)                                                  (excl BNZ, Rural Bk)
                                                                                                                       (excl BNZ, Rural Bk)

























-5.0%                                                                             GDP YoY% Real

  9-Aug-12                                                 Kurt Bank sub-sample only
                                             Note: chart for NZ Hess, WMS                                                                                               11
       Credit Loss Data Australasia
                                                               (on average loans, annualized)
                                  AU Westpac, 1993,
                  AU ANZ               3.7%
                  AU CoWthBk                          AU ANZ, 1993,
3.0%                                                      2.6%
                  AU NAB
                                                         AU CoWthBk,
                  AU Westpac
2.0%                                                      1993, 2.5%



   1980                    1985        1990             1995                    2000

       9-Aug-12                         Kurt Hess, WMS                                          12
Drivers of Credit Losses in
  Australasian Banking

 Principal Model
                          K    zk                         q
CLE  Const    x
       it                             ks ki( t  s )      s CLEi (t  s )  uit ;
                          k 1 s 0                      s 1

i  1,.....,n; t  q  1,....,T
 CLEit      Credit loss experience for bank i in period t
 xkit       Observations of the potential explanatory variable k for bank i
            and period t
 uit        Random error term with distribution N(0,),
           Variance-covariance matrix of it error terms
 n          Number of banks in sample
 T          Years in observation period
 K          Number of explanatory variables
 zk         Maximum lag of the explanatory variable k of the model
 q          Maximum lag of the dependent variable of the model
 9-Aug-12                          Kurt Hess, WMS                                14
Principal Model

     Principal model on previous slide
      allows for many potential functional
     There are choices with regard to
    – Dependent CLE proxy
    – Suitable drivers of credit losses and lags
      for these drivers
    – Estimation techniques
9-Aug-12              Kurt Hess, WMS               15
Determinants of Credit Losses
Macro Factors (1)
 Real GDP growth       -ve      Ability of borrowers to service
                                debt determined by the
                                economic cycle.
 Unemployment          +ve      Unemployment rate not only
   rate                         reflects the business cycle
                                (like GDP growth) but also
                                longer term and structural
                                imbalances in economy.
 Liabilities of        +ve      The more households and
 households/firms               firms in the system are
                                indebted, the more financially
 as % of disp.                  vulnerable they will be.
9-Aug-12                Kurt Hess, WMS                            16
Determinants of Credit Losses
Macro Factors (2)
 Asset prices /                    Disturbances in the asset
 interest rates                    markets can impair the value
                                   of banks’ assets both directly
                                   and indirectly (i.e. through
 Housing price index   -ve         reduced collateral values).
 (changes)                         Experience shows that
                                   especially the property sector
 Return leading share -ve          and the share markets may
 indices                           play a critical role in triggering
                                   losses in the banking system.
 Change real/nominal +ve           Similar effects are expected in
 interest rates                    a volatile interest rate
 only CPI growth                   environment.

9-Aug-12                   Kurt Hess, WMS                               17
Determinants of Credit Losses
Bank Specific Factors (1)

Past credit      +ve       Fast growth of the loan
expansion         or       portfolio is often associated
                 -ve       with subsequent loan losses.
                           Alternatively, a slow growing
                           loan portfolio may be caused
                           by a weak economy and thus
                           increase CLE.

9-Aug-12          Kurt Hess, WMS                           18
Determinants of Credit Losses
Bank Specific Factors (2)
Pricing of risks                     A bank’s deliberate choice to
( net interest margins)    +ve/      lend to more risky borrowers
                           (-ve)     is likely reflected in higher
                                     interest margins. Lower past
                                     margins might induce greater
                                     risk-taking by bank
Characteristic of                    The share of comparably
lending portfolio           -ve      lower risk housing loans as
(share of housing                    % of loans proxies the risk
loans)                               characteristic of the bank’s
                                     loan portfolio.

9-Aug-12                      Kurt Hess, WMS                         19
Determinants of Credit Losses
Bank Specific Factors (3)
Diversification          -ve      A bank’s assets in proportion
                                  to the overall banking system
                        +ve/      assets provides a crude proxy
Market power            (-ve)     for loan portfolio
                                  diversification or market
Cost efficiency         +ve/      Inefficient banks can be
(cost-income ratio)     (-ve)     expected to suffer greater
                                  credit losses. Alternatively,
                                  such banks could maintain an
                                  expensive credit monitoring
                                  procedure and will thus
                                  exhibit lower credit losses.
9-Aug-12                  Kurt Hess, WMS                          20
Determinants of Credit Losses
Bank Specific Factors (4)
Income smoothing                             Some literature finds evidence
(Earnings before provisions &       +ve      of banks using discretionary
taxes as % of assets)                        provisions to smooth earnings
                                             for a variety of motivations.
Capital                                      General provisions count
management                          -ve      towards Basel I minimum
(Capital measured as tier 1 or               capital and weaker banks
tier 1+2 capital as % of risk                might thus be tempted to
weighted assets)                             engage in capital
                                             management through

9-Aug-12                             Kurt Hess, WMS                           21
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
9-Aug-12              Kurt Hess, WMS           22
    Dependent variables in model
               Variable      Description                           Lags (yrs.)

               GDPGRW        Macro state proxies: GDP growth or      0 to -2
               UNEMP         level/change Unemployment rate
               RET_SHINDX Asset price shock proxies: Return          0 to -2
               HPGRW      share index or change house prices
               CPIGRW        Change CPI                              0 to -2

               SH_SYSLNS     Share of system loans (size proxy)        0
               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

    9-Aug-12                         Kurt Hess, WMS                        23
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

9-Aug-12                    Kurt Hess, WMS       25
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

9-Aug-12                    Kurt Hess, WMS        26
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
9-Aug-12                    Kurt Hess, WMS       27
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)

9-Aug-12                    Kurt Hess, WMS         28
Results size proxy
see Table 8, 9,10 in paper

 Higher level of provisioning for larger
  banks – no significance of coefficients,
 Intuition: portfolios of smaller institutions
  often dominated by (comparably) lower
  risk housing loans

9-Aug-12                    Kurt Hess, WMS       29
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
   Inconclusive results also in comparable
    studies, e.g. Salas & Saurina (2002) for

9-Aug-12                    Kurt Hess, WMS       30
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
9-Aug-12                      Kurt Hess, WMS              31
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
 Not surprising as “gut feel” would tell
  that excessive monitoring might not pay

9-Aug-12                    Kurt Hess, WMS       32
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

9-Aug-12                    Kurt Hess, WMS       33
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.

9-Aug-12                    Kurt Hess, WMS       34
     Model presented here is very suitable
      for assessing general / global effects
      on impaired assets in the banking
     The dynamics of this transmission
      seems to differ among systems
     A study of particular effects might thus
      call for alternative models

9-Aug-12              Kurt Hess, WMS         35
Conclusions (2)
     Income smoothing is a reality, possibly
      also with new tighter IFRS
      provisioning rules as this ultimately
      remains a discretionary managerial

9-Aug-12              Kurt Hess, WMS       36
Conclusions (3)
     Use data base for comparative studies
      of alternative CLE dependent
     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

9-Aug-12              Kurt Hess, WMS       37
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)

 9-Aug-12             Kurt Hess, WMS       39
Basel II Pillars
Pages in New Basel Capital Accord (issued June 2004)

                     6 of 216
                                              Pillar 3 Market
                                                 16 of 216

     Pillar 1                                       Pillar 2
   179 of 216                                      15 of 216

9-Aug-12                 Kurt Hess, WMS                         40
Pro Memoria: Calculation Capital
Requirements under Basel II

                  Total Capital
   Credit Risk + Market Risk + Operational Risk                       8%

Significantly           Relatively           New               (Could be set higher
Refined                 Unchanged                                 under pillar 2)

Source: slide inspired by PWC presentation slide retrieved 27/7/2005 from ,

9-Aug-12                             Kurt Hess, WMS                            41
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

9-Aug-12                 Kurt Hess, WMS                42
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

9-Aug-12              Kurt Hess, WMS          43
Primer Loan Loss Accounting
Beginning of period    Transactions during period        End of period

                       Profit & loss statement (P&L)
                          - Bad debt charge

                        Provision account
Loan balance            Provisons initial balance      Loan balance
Gross loan amount       + New provisions made          Gross loan amount
                         - Debt write-offs
- Provisions initial                                   - Provisions final
                         + Recovery of debt
  balance                                                balance
                           previously written off
Net loan amount         Provisons final balance        Net loan amount

                        Gross loan account
                        Opening balance
                        -/+ Loans issued/repaid
                         - Debt write-offs
                        + Recovery of debt
                           previously written off
                        Ending balance
9-Aug-12                       Kurt Hess, WMS                               44
 Primer Loan Loss Accounting
Initiation of loan                Potential loan loss               Loan write-off    Loan recovery
                                      identified                    (derecognition)
                                                                     Loan account       Loan account
   Loan account                      Loan account
                     General                           Additional   1,000       400      600      + 700
 1,000       +50     provision      1,000 50 +350      specific     - 400     - 400    + 100
   950               recognized       600              provisons      600                -
   Cash account                                                                         Cash account
           1,000                                                                          700
Bad debt provision                Bad debt provision
                                                                                      Bad debt provision
    expense                           expense
                                                                                       recovery income
    50                                350                                                 -         100

 9-Aug-12                                        Kurt Hess, WMS                                            45
Credit Loss Data Australasia
  NZ$ million

      1,200                                                              BNZ books bad debt
      1,000                                                               credits 1994-1997
           0                                                                 Net write-offs


                                                                         Bad debt charge to P&L
           1 98

                  1 98

                         1 98

                                1 99


                                             1 99


                                                    1 99

                                                           1 99

                                                                  2 00
                                   1 99

BNZ 1984 - 2002

9-Aug-12                                        Kurt Hess, WMS                                    46
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
Countrywide Bank, NBNZ, Rural Bank, Trust Bank NZ, TSB
Bank, United Bank, Westpac (NZ)
9-Aug-12                  Kurt Hess, WMS                 47
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
    – Balance sheets of M3 institutions only back
      to 1988 for New Zealand (use private
      sector credit statistics instead)
9-Aug-12              Kurt Hess, WMS           48
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)

9-Aug-12              Kurt Hess, WMS            49
Measuring CLE
     Dedicated nature of database allows tests
      for many proxies for a bank’s credit loss
      experience (CLE)
    –      Level of bad debt provisions, impaired assets,
           past due assets
    –      Impaired asset expense (=provisions charge to
    –      Write-offs (either gross or net of recoveries)
    –      Components of above proxies, e.g. general or
           specific component of provisions (stock or

9-Aug-12                    Kurt Hess, WMS                  50
                                                                                                                         Measuring CLE
                                                                                                                              Histogram of selected CLE proxies




                                                                                                                                                                                                                                                       Im paired asset expense / loans
                                                                                                                                                                                                                                                     Im p. asset expense / net interest
                                                                                                                                                                                                                                                   Im p. asset exp. / gross interest
            Median -0.25 StD

                                                                                                                                                                                                                                                 Net w rite-offs / loans
                               Median -0.25 Std to

                                                     Median +0.25 StD
                                                                        Median +0.25 StD to
                and less

                                                                                                                                                                                                                                               Stock of provisions / loans
                                                                                              Median +0.5 StD to
                                                                         Median +0.5 StD

                                                                                              Median +0.75 StD

                                                                                                                   Median +0.75 StD to
                                                       Median to

                                                                                                                                         Median +1.25 StD
                                                                                                                                         Median +1 StD to

                                                                                                                                                                                                                                             Im paired assets / assets
                                                                                                                     Median +1 StD

                                                                                                                                                            Median +1.25 StD to

                                                                                                                                                                                  Median +1.5 StD to
                                                                                                                                                             Median +1.5 StD

                                                                                                                                                                                  Median +1.75 StD

                                                                                                                                                                                                       Median +1.75 StD to

                                                                                                                                                                                                                             Median +2 StD
                                                                                                                                                                                                         Median +2 StD

                                                                                                                                                                                                                               and more

                                                                                                                                                                                                                                                 Pooled observations of
                                                                                                                                                                                                                                                 Australian and NZ
                                                                                                                                                                                                                                                 Banks 1980 - 2005
9-Aug-12                                                                                                                              Kurt Hess, WMS                                                                                                                                   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.

9-Aug-12             Kurt Hess, WMS              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.

9-Aug-12             Kurt Hess, WMS          54
Selected References
Kearns, A. (2004). Loan Losses and the
  Macroeconomy: A Framework for Stress
  Testing Credit Institutions’ Financial Well-
  Being, 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.

9-Aug-12              Kurt Hess, WMS             55
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.

9-Aug-12           Kurt Hess, WMS         56

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