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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 Topics Motivation Literature review Credit loss data Australasia Methodological issues Results Conclusions 23-May-17 Kurt Hess, WMS [email protected] 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)] 23-May-17 Kurt Hess, WMS [email protected] 3 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 23-May-17 Kurt Hess, WMS [email protected] 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 23-May-17 Kurt Hess, WMS [email protected] 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 23-May-17 Kurt Hess, WMS [email protected] 6 Literature review Studies with global samples (using commercial data providers): – Cavallo & Majnoni (2001), Bikker & Metzemakers (2003) Country-specific samples – – – 23-May-17 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] 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 23-May-17 Kurt Hess, WMS [email protected] 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 bank 23-May-17 Kurt Hess, WMS [email protected] 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 23-May-17 Kurt Hess, WMS [email protected] 10 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% 23-May-17 GDP YoY% Real Note: chart forKurt NZ Hess, Bank WMS sub-sample only [email protected] 11 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 23-May-17 1985 1990 1995 Kurt Hess, WMS [email protected] 2000 12 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 23-May-17 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 Kurt Hess, WMS [email protected] 14 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 23-May-17 Kurt Hess, WMS [email protected] 15 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. 23-May-17 Kurt Hess, WMS [email protected] 16 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 23-May-17 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. Kurt Hess, WMS [email protected] 17 Determinants of Credit Losses Bank Specific Factors (1) Past credit expansion 23-May-17 +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. Kurt Hess, WMS [email protected] 18 Determinants of Credit Losses Bank Specific Factors (2) Pricing of risks ( net interest margins) Characteristic of lending portfolio (share of housing loans) 23-May-17 +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. Kurt Hess, WMS [email protected] 19 Determinants of Credit Losses Bank Specific Factors (3) Diversification -ve Market power +ve/ (-ve) Cost efficiency +ve/ (-ve) (cost-income ratio) 23-May-17 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. Kurt Hess, WMS [email protected] 20 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) 23-May-17 +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. Kurt Hess, WMS [email protected] 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 23-May-17 Kurt Hess, WMS [email protected] 22 Bankspecific Aggregate Dependent variables in model 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 23-May-17 Kurt Hess, WMS [email protected] Lags (yrs.) 0 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 23-May-17 Kurt Hess, WMS [email protected] 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 23-May-17 Kurt Hess, WMS [email protected] 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 sector 23-May-17 Kurt Hess, WMS [email protected] 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) 23-May-17 Kurt Hess, WMS [email protected] 28 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 23-May-17 Kurt Hess, WMS [email protected] 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 term Inconclusive results also in comparable studies, e.g. Salas & Saurina (2002) for Spain 23-May-17 Kurt Hess, WMS [email protected] 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 23-May-17 Kurt Hess, WMS [email protected] 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 monitoring Not surprising as “gut feel” would tell that excessive monitoring might not pay 23-May-17 Kurt Hess, WMS [email protected] 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 23-May-17 Kurt Hess, WMS [email protected] 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. 23-May-17 Kurt Hess, WMS [email protected] 34 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 23-May-17 Kurt Hess, WMS [email protected] 35 Conclusions (2) Income smoothing is a reality, possibly also with new tighter IFRS provisioning rules as this ultimately remains a discretionary managerial decision 23-May-17 Kurt Hess, WMS [email protected] 36 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 23-May-17 Kurt Hess, WMS [email protected] 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) 23-May-17 Kurt Hess, WMS [email protected] 39 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 23-May-17 Pillar 3 Market Discipline: 16 of 216 pages Kurt Hess, WMS [email protected] 40 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 , 23-May-17 Kurt Hess, WMS [email protected] 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 23-May-17 Kurt Hess, WMS [email protected] 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 23-May-17 Kurt Hess, WMS [email protected] 43 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 23-May-17 Kurt Hess, WMS [email protected] 44 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 23-May-17 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 - Kurt Hess, WMS [email protected] 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 23-May-17 Kurt Hess, WMS [email protected] 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 NEW ZEALAND: ANZ National Bank, ASB, BNZ, Countrywide Bank, NBNZ, Rural Bank, Trust Bank NZ, TSB Bank, United Bank, Westpac (NZ) 23-May-17 Kurt Hess, WMS [email protected] 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 Australia – Balance sheets of M3 institutions only back to 1988 for New Zealand (use private sector credit statistics instead) 23-May-17 Kurt Hess, WMS [email protected] 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) 23-May-17 Kurt Hess, WMS [email protected] 49 Measuring CLE Dedicated nature of database allows tests for many proxies for a bank’s credit loss experience (CLE) – – – – 23-May-17 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 [email protected] 50 Measuring CLE 250 Histogram of selected CLE proxies 200 150 100 50 Im paired asset expense / loans Im p. asset expense / net interest 0 23-May-17 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 Kurt Hess, WMS [email protected] 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. 23-May-17 Kurt Hess, WMS [email protected] 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. 23-May-17 Kurt Hess, WMS [email protected] 54 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. 23-May-17 Kurt Hess, WMS [email protected] 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. 23-May-17 Kurt Hess, WMS [email protected] 56