Drivers of credit losses in australasian banking
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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. Topics. Motivation.

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Drivers of credit losses in australasian banking

Drivers of Credit Losses in Australasian Banking

Slides prepared byKurt HessUniversity of Waikato Management School, Department of FinanceHamilton, New Zealand


Topics

Motivation

Literature review

Credit loss data Australasia

Methodological issues

Results

Conclusions

Topics

Kurt Hess, WMS [email protected]


Motivation

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)]

Kurt Hess, WMS [email protected]


Motivation1

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

Kurt Hess, WMS [email protected]


Literature review

Literature review

Two main streams of research that analyse drivers of banks’ credit losses (or more specifically loan losses):

  • Literature with regulatory focus looks at macro & micro factors

  • Literature looks discretionary nature of loan loss provisions and behavioural factors which affect them

Kurt Hess, WMS [email protected]


Literature review1

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

Kurt Hess, WMS [email protected]


Literature review2

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)

Kurt Hess, WMS [email protected]


Literature review3

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

Kurt Hess, WMS [email protected]


Credit loss data australasia

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

Kurt Hess, WMS [email protected]


Credit loss data australasia1

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

Kurt Hess, WMS [email protected]


Credit losses and gdp growth new zealand banks

Credit Losses and GDP Growth (New Zealand Banks)

Provisioning/write-off behaviour correlated to macro factors

Note: chart for NZ Bank sub-sample only

Kurt Hess, WMS [email protected]


Credit loss data australasia2

Credit Loss Data Australasia

Kurt Hess, WMS [email protected]


Drivers of credit losses in australasian banking1

Drivers of Credit Losses in Australasian Banking

Methodology


Principal model

Principal Model

CLEitCredit loss experience for bank i in period t

xkitObservations of the potential explanatory variable k for bank i and period t

uitRandom error term with distribution N(0,),

Variance-covariance matrix of it error terms

nNumber of banks in sample

TYears in observation period

KNumber of explanatory variables

zkMaximum lag of the explanatory variable k of the model

qMaximum lag of the dependent variable of the model

Kurt Hess, WMS [email protected]

14

5-Jun-14


Principal model1

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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5-Jun-14


Determinants of credit losses

Determinants of Credit Losses

Macro Factors (1)

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Determinants of credit losses1

Determinants of Credit Losses

Macro Factors (2)

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Determinants of credit losses2

Determinants of Credit Losses

Bank Specific Factors (1)

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Determinants of credit losses3

Determinants of Credit Losses

Bank Specific Factors (2)

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Determinants of credit losses4

Determinants of Credit Losses

Bank Specific Factors (3)

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Determinants of credit losses5

Determinants of Credit Losses

Bank Specific Factors (4)

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Pooled regression model as per equation 1 in paper

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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5-Jun-14


Dependent variables in model

Dependent variables in model

Aggregate

Bankspecific

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5-Jun-14


Drivers of credit losses in australasian banking2

Drivers of Credit Losses in Australasian Banking

Empirical results


Results macro state factors

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

Kurt Hess, WMS [email protected]


Results macro state factors 2

Results macro state factors (2)

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

see Table 8, 9,10 in paper

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5-Jun-14


Results asset price factors

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 sigificanceIntuition: early 90s crises not rooted in particular problems of the housing sector

Kurt Hess, WMS [email protected]


Results cpi growth

Results CPI growth

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)

see Table 8, 9,10 in paper

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5-Jun-14


Results size proxy

Results size proxy

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

see Table 8, 9,10 in paper

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29

5-Jun-14


Results net interest margin

Results net interest margin

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

see Table 8, 9,10 in paper

Kurt Hess, WMS [email protected]

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5-Jun-14


Results net interest margin 2

Results net interest margin (2)

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

see Table 8, 9,10 in paper

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31

5-Jun-14


Results cost efficiency cir

Results cost efficiency (CIR)

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

see Table 8, 9,10 in paper

Kurt Hess, WMS [email protected]

32

5-Jun-14


Results earnings proxy

Results earnings proxy

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

see Table 8, 9,10 in paper

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33

5-Jun-14


Results past bank growth

Results past bank growth

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.

see Table 8, 9,10 in paper

Kurt Hess, WMS [email protected]

34

5-Jun-14


Conclusions

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

Kurt Hess, WMS [email protected]


Conclusions 2

Conclusions (2)

  • Income smoothing is a reality, possibly also with new tighter IFRS provisioning rules as this ultimately remains a discretionary managerial decision

Kurt Hess, WMS [email protected]


Conclusions 3

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

Kurt Hess, WMS [email protected]

37

5-Jun-14


Credit loss experience of australasian banks

Credit Loss Experience of Australasian Banks

Back-up Slides


Basel ii pillars

Basel II Pillars

  • Pillar 1:

    • Minimum capital requirements

  • Pillar 2:

    • A supervisory review process

  • Pillar 3:

    • Market discipline (risk disclosure)

Kurt Hess, WMS [email protected]


Basel ii pillars1

Basel II Pillars

Pages in New Basel Capital Accord (issued June 2004)

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Pro memoria calculation capital requirements under basel ii

Pro Memoria: Calculation Capital Requirements under Basel II

Unchanged

Total Capital

Credit Risk + Market Risk + Operational Risk

 8%

(Could be set higher under pillar 2)

Significantly

Refined

Relatively

Unchanged

New

Source: slide inspired by PWC presentation slide retrieved 27/7/2005 from http://asp.amcham.org.sg/downloads/Basel%20II%20Update%20-%20ACC.ppt ,

Kurt Hess, WMS [email protected]


Basel ii irb approach

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

Kurt Hess, WMS [email protected]


Basel ii irb approach1

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

Kurt Hess, WMS [email protected]


Primer loan loss accounting

Primer Loan Loss Accounting

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Primer loan loss accounting1

Primer Loan Loss Accounting

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Credit loss data australasia3

Credit Loss Data Australasia

BNZ 1984 - 2002

Kurt Hess, WMS [email protected]


Credit loss data australasia4

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)

Kurt Hess, WMS [email protected]


Credit loss data australasia5

Credit Loss Data Australasia

Data issues

Macro level statistics

Differing formats between NZ and Australiae.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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5-Jun-14


Credit loss data australasia6

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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5-Jun-14


Measuring cle

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 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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50

5-Jun-14


Measuring cle1

Measuring CLE

Histogram of selected CLE proxies

Median

Pooled observations of Australian and NZ Banks 1980 - 2005

Kurt Hess, WMS [email protected]


Credit loss experience of australasian banks1

Credit Loss Experience of Australasian Banks

Selected References


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.

Kurt Hess, WMS [email protected]


Selected references1

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.

Kurt Hess, WMS [email protected]


Selected references2

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.

Kurt Hess, WMS [email protected]


Selected references3

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.

Kurt Hess, WMS [email protected]


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