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Drivers of Credit Losses in Australasian Banking

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

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  1. Drivers of Credit Losses in Australasian Banking Slides prepared byKurt HessUniversity of Waikato Management School, Department of FinanceHamilton, New Zealand

  2. Motivation Literature review Credit loss data Australasia Methodological issues Results Conclusions Topics Kurt Hess, WMS kurthess@waikato.ac.nz

  3. 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)] • Entities in charge of prudential supervision and system stability thus need to understand drivers of credit losses in banking system Kurt Hess, WMS kurthess@waikato.ac.nz

  4. Motivation • Very topical research area in the context of New Basel II Capital Accord • Will allow use of proprietary credit risk models to determine required capital • Validation of models & parameters supervisors • This is the first specific research of long term drivers of credit losses for Australian banking system Kurt Hess, WMS kurthess@waikato.ac.nz

  5. Credit Risk & Basel II A well-known Basel tennis player (Swiss Indoors in Basel, October 27, 2007)Retrieved from http://uk.eurosport.yahoo.com/071027/2/vxmq.html 15 November 2007 Kurt Hess, WMS kurthess@waikato.ac.nz

  6. 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 kurthess@waikato.ac.nz

  7. Literature review Literature which explores macro and micro (bank specific) determinants of loan losses • Examples macro factors: • GDP growth • indebtedness of households and firms • asset prices (real estate, share markets) Kurt Hess, WMS kurthess@waikato.ac.nz

  8. Literature review • Examples of micro (bank specific) factors: • exposure to certain lending, collateral • portfolio diversification • (past) credit growth • net interest margins • efficiency Kurt Hess, WMS kurthess@waikato.ac.nz

  9. 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 kurthess@waikato.ac.nz

  10. 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 kurthess@waikato.ac.nz

  11. 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 kurthess@waikato.ac.nz

  12. 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 kurthess@waikato.ac.nz

  13. 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 kurthess@waikato.ac.nz

  14. 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 kurthess@waikato.ac.nz

  15. Credit Loss Data Australasia • Sample period covers interesting period of major credit losses for some banks in the late 1980s, early 90s • Government bailout of BNZ and SBSA • Major losses for Westpac in Australia (WP lost leading market position as a consequence) Kurt Hess, WMS kurthess@waikato.ac.nz

  16. 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 kurthess@waikato.ac.nz

  17. Credit Loss Data Australasia Kurt Hess, WMS kurthess@waikato.ac.nz

  18. Credit Loss Data Australasia BNZ 1984 - 2002 Kurt Hess, WMS kurthess@waikato.ac.nz

  19. 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) Kurt Hess, WMS kurthess@waikato.ac.nz 19 2-Jun-14

  20. 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) Kurt Hess, WMS kurthess@waikato.ac.nz 20 2-Jun-14

  21. Drivers of Credit Losses in Australasian Banking Methodology

  22. Principal Model CLEitCredit 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 zkMaximum lag of the explanatory variable k of the model q Maximum lag of the dependent variable of the model Kurt Hess, WMS kurthess@waikato.ac.nz 22 2-Jun-14

  23. 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 Kurt Hess, WMS kurthess@waikato.ac.nz 23 2-Jun-14

  24. 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) Kurt Hess, WMS kurthess@waikato.ac.nz 24 2-Jun-14

  25. Measuring CLE Histogram of selected CLE proxies Median Pooled observations of Australian and NZ Banks 1980 - 2005 Kurt Hess, WMS kurthess@waikato.ac.nz

  26. Determinants of Credit Losses Macro Factors (1) Kurt Hess, WMS kurthess@waikato.ac.nz

  27. Determinants of Credit Losses Macro Factors (2) Kurt Hess, WMS kurthess@waikato.ac.nz

  28. Determinants of Credit Losses Bank Specific Factors (1) Kurt Hess, WMS kurthess@waikato.ac.nz

  29. Determinants of Credit Losses Bank Specific Factors (2) Kurt Hess, WMS kurthess@waikato.ac.nz

  30. Determinants of Credit Losses Bank Specific Factors (3) Kurt Hess, WMS kurthess@waikato.ac.nz

  31. Determinants of Credit Losses Bank Specific Factors (4) Kurt Hess, WMS kurthess@waikato.ac.nz

  32. Determinants of Credit Losses Bank Specific Factors (5) Kurt Hess, WMS kurthess@waikato.ac.nz

  33. 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 Kurt Hess, WMS kurthess@waikato.ac.nz 33 2-Jun-14

  34. Dependent variables in model Aggregate Bankspecific Kurt Hess, WMS kurthess@waikato.ac.nz 34 2-Jun-14

  35. Drivers of Credit Losses in Australasian Banking Empirical results

  36. Results macro state factors see Table 8 -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 kurthess@waikato.ac.nz

  37. 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 -10 in paper Kurt Hess, WMS kurthess@waikato.ac.nz 37 2-Jun-14

  38. Results macro state factors (3) Qualitatively similar results as in literature but, like country-specific differences above, the concrete sensitivities found here and in other studies vary greatly. see Table 8 -10 in paper Kurt Hess, WMS kurthess@waikato.ac.nz 38 2-Jun-14

  39. Results asset price factors see Table 8 -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 kurthess@waikato.ac.nz

  40. 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 -10 in paper Kurt Hess, WMS kurthess@waikato.ac.nz 40 2-Jun-14

  41. 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 -10 in paper Kurt Hess, WMS kurthess@waikato.ac.nz 41 2-Jun-14

  42. Results net interest margin Generally negative, contemporaneous and 2yr lagged term significant Inconclusive evidence of some hypotheses in literature, e.g. wide interest margins should be followed by higher credit losses (+ve lagged coefficients) Possibly need to look at longer lags Significance shows that this is still an essential control parameter of bank characteristic (type of lending) see Table 8 -10 in paper Kurt Hess, WMS kurthess@waikato.ac.nz 42 2-Jun-14

  43. 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 -10 in paper Kurt Hess, WMS kurthess@waikato.ac.nz 43 2-Jun-14

  44. 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 -10 in paper Kurt Hess, WMS kurthess@waikato.ac.nz 44 2-Jun-14

  45. 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 -10 in paper Kurt Hess, WMS kurthess@waikato.ac.nz 45 2-Jun-14

  46. 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 kurthess@waikato.ac.nz

  47. Conclusions (2) • Income smoothing a reality, possibly also with new tighter IFRS provisioning rules as this remains a decision of management judgement • Suggest modelling of past bank growth in a similar way for other markets to get more general confirmation of our results. Kurt Hess, WMS kurthess@waikato.ac.nz

  48. 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 kurthess@waikato.ac.nz 48 2-Jun-14

  49. Credit Loss Experience of Australasian Banks Back-up Slides

  50. Basel II Pillars • Pillar 1: • Minimum capital requirements • Pillar 2: • A supervisory review process • Pillar 3: • Market discipline (risk disclosure) Kurt Hess, WMS kurthess@waikato.ac.nz

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