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Drivers of Credit Losses in Australasian Banking

Drivers of Credit Losses in Australasian Banking

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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

  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)] Kurt Hess, WMS

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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

  11. 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

  12. Credit Loss Data Australasia Kurt Hess, WMS

  13. Drivers of Credit Losses in Australasian Banking Methodology

  14. 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 14 5-Jun-14

  15. 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 15 5-Jun-14

  16. Determinants of Credit Losses Macro Factors (1) Kurt Hess, WMS

  17. Determinants of Credit Losses Macro Factors (2) Kurt Hess, WMS

  18. Determinants of Credit Losses Bank Specific Factors (1) Kurt Hess, WMS

  19. Determinants of Credit Losses Bank Specific Factors (2) Kurt Hess, WMS

  20. Determinants of Credit Losses Bank Specific Factors (3) Kurt Hess, WMS

  21. Determinants of Credit Losses Bank Specific Factors (4) Kurt Hess, WMS

  22. 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 22 5-Jun-14

  23. Dependent variables in model Aggregate Bankspecific Kurt Hess, WMS 23 5-Jun-14

  24. Drivers of Credit Losses in Australasian Banking Empirical results

  25. 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

  26. 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 Kurt Hess, WMS 26 5-Jun-14

  27. 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

  28. 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 Kurt Hess, WMS 28 5-Jun-14

  29. 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 Kurt Hess, WMS 29 5-Jun-14

  30. 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 30 5-Jun-14

  31. 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 Kurt Hess, WMS 31 5-Jun-14

  32. 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 32 5-Jun-14

  33. 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 Kurt Hess, WMS 33 5-Jun-14

  34. 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 34 5-Jun-14

  35. 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

  36. 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

  37. 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 37 5-Jun-14

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

  39. Basel II Pillars • Pillar 1: • Minimum capital requirements • Pillar 2: • A supervisory review process • Pillar 3: • Market discipline (risk disclosure) Kurt Hess, WMS

  40. Basel II Pillars Pages in New Basel Capital Accord (issued June 2004) Kurt Hess, WMS

  41. 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 , Kurt Hess, WMS

  42. 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

  43. 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

  44. Primer Loan Loss Accounting Kurt Hess, WMS

  45. Primer Loan Loss Accounting Kurt Hess, WMS

  46. Credit Loss Data Australasia BNZ 1984 - 2002 Kurt Hess, WMS

  47. 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

  48. 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 48 5-Jun-14

  49. 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 49 5-Jun-14

  50. 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 50 5-Jun-14