What was the key determinant of loan quality deterioration of Russian banks

Transcription

1 Wha was he key deerminan of loan qualiy deerioraion of Russian banks during he las crisis: macroeconomic condiions or risky business sraegies? 1 Anna Pesova, Mikhail Mamonov 2 Absrac During he laes crisis Russian banking sysem was faced wih a significan deerioraion in he loan qualiy. Russian governmen was forced o carry ou massive recapializaion of he major naional banks o mainain heir sabiliy. To evaluae he effeciveness of such a policy measure i s necessary o disinguish among he credi risk sources of affeced banks. The main purpose of his paper is o separae he influence of macro- and microeconomic facors ha led o an increase in bad loans of Russian banks. To address he research quesion we use single-equaion approach on panel daa covering Russian banks during he period The resuling findings sugges ha mos of he negaive influence on Russian banks loan qualiy was caused by deerioraion of macroeconomic condiions. However, aking ino accoun a considerable heerogeneiy of Russian banks we develop he framework which is aimed a idenificaion banks whose risky sraegy before he crisis led o prevailing par of microfacors conribuion o he overdue loans increase. This insrumen can improve efficiency of governmen decisions on providing financial suppor o credi insiuions. JEL: E44, G21 Keywords: credi risk, Russian banking sysem, dynamic panel daa model This research was suppored by he Economics Educaion and Research Consorium and funded by GDN 2 expers of he Cener for Macroeconomic Analysis and Shor-Term Forecasing. 47, Nakhimovsky prospek, Moscow, Russia. addresses: apesova@forecas.ru (A.Pesova), mmamonov@forecas.ru (M.Mamonov)

2 Conens 1. Inroducion Relaed lieraure review Facors descripion Daa descripion including dealing wih he ouliers Model specificaion and mehodology Esimaion resuls Conclusions Bibliography Appendix...31

3 1. Inroducion During he laes crisis Russian banking sysem was faced wih a significan deerioraion in he loan qualiy. The growh of bad loans caused a sharp increase in banks loan loss reserves. The laer reduced opporuniies for banks o earn profis and exered pressure on banks' capial adequacy raio. Under hese condiions Russian governmen was forced o carry ou massive recapializaion of he major banks o mainain heir sabiliy. In Vnesheconombank (Russian Bank for developmen), graned Russian credi insiuions more han 400 billion rubles in he form of subordinaed loans. Besides, Bank of Russia provided Sberbank he larges Russian bank wih he subordinaed loan of 500 billion rubles. The quesion ineviably arises, how jusified was he decision of Russian governmen o provide financial suppor o credi insiuions? To evaluae he effeciveness of such a policy measure i is necessary o disinguish among he credi risk sources of affeced banks. In paricular, sae capial injecions ino banks wih aggressive pre-crisis business sraegies creaes a disoring incenives for oher banks and may lead o an exacerbaion of moral hazard in middle-erm perspecive. On he conrary, governmen suppor is reasonable for hose banks who suffer decrease in loan qualiy mainly due o he worsening of macroeconomic condiions. The main purpose of our research is o separae he influence of macro- and microeconomic facors ha led o an increase in bad loans of Russian banks using wide ools of panel daa economerics. The main hypohesis o be esed is he predominance of microeconomic facors of credi risk realizaion for mos banks ha received governmen suppor. Tesing such a hypohesis involves esimaion of panel daa regression models explaining he dynamics of bad loans of Russian banks. We ll use as he independen variables hose ha reflec macroeconomic condiions and specific business sraegies of banks. There are a lo of sudies considering deerminans of bad loans a individual bank level. Mos of hem uilize daa on larges banks or on he represenaive sample of banks wihin one counry (among sudies o be menioned laer here are researches on Ialian, Indian, Greek, Spanish, Polish banks). A few papers consider cross-counry daa on individual bank level combining banking unis of similar counries

4 (geographically or economically) ino one panel (GCC, MENA counries). Exising sudies on individual bank level are basically aimed a idenifying macro- and microeconomic deerminans of bad loans and provide empirical evidence for policy implicaions (such as influence of bank compeiion, capializaion, diversificaion, regulaion environmen, ec. on financial sabiliy approximaed by ex pos credi risk). To he bes of our knowledge he quesion of disenangling he relaive imporance of macro- and bankspecific facors of credi risk have no been raised ye 3. However we believe ha his quesion is of grea imporance in he conex of las crisis in he Russian banking secor and in many oher counries when governmens were forced o recapialize injured banks (as repored in (Laeven, Valencia, 2010), during he recen crisis direc fiscal coss o suppor he financial secor were abou 5 percen of GDP in affeced counsies). 2. Relaed lieraure review The accumulaion of credi risk is he mos imporan facor in erms of mainaining financial sabiliy in mos counries, especially in developing ones. The empirical lieraure suggess ha he excessive growh of disressed asses is a sign of an imminen banking crisis. For regulaors aimed a ensuring financial sabiliy i is imporan o predic and foresee he onse of bad deb crisis and o idenify is deerminans. This encouraged a large number of empirical sudies on he economeric analysis of credi risk facors. Empirical sudies on deerminans of credi risk can be divided ino wo main groups - sudies a he level of he banking sysem as a whole (macroeconomic approach - see (Hoggarh, Sorensen, Zicchino 2005), (Babihuga 2007), Pesola (2007)), as well as sudies on individual bank level (microconomic approach - see (Jimenez, Saurina 2005), (Espinoza, Prasad 2010), (Quagliariello, 2007), ec.). Sudies on macroeconomic or aggregae daa are focused on exploring he relaionship beween aggregae measure credi risk (percenage of adversely classified loans in he consolidaed loan porfolio of banking secor or aggregae defaul rae in he corporae secor) and macroeconomic condiions using daa in one 3 excep for (Bercoff, 2002) who, as repored in (Dash, Kabra, 2010), used survival analysis o separae inffluence of macroeconomic and bank specific facors. Though, his paper was no published ye ha s why i isn publicly available now. We were no able o ge acquained wih is resuls and mehodology.

5 (Hoggarh, Sorensen, Zicchino 2005) or several counries (Nkusu, 2011). The resuling economeric models can be applied o op-down sress-ess 4 of banking secor (Hoggarh, Sorensen, Zicchino 2005) or o invesigae he feedback effecs beween loan qualiy and is macroeconomic deerminans (Nkusu, 2011). The oher branch of empirical lieraure invesigaes deerminans of credi risk on financial firm level daa. These papers are of larges ineres for us as hey explain qualiy of loan porfolio of individual banks using microeconomic (bank level) and macroeconomic daa. This direcly corresponds o he opic of our research. Sudies on individual bank level differ in choosing he measure of credi risk. The firs indicaor which is frequenly used in empirical work is he raio of loan loss provisions o oal loan porfolio (Quagliariello 2007), (Głogowski 2008). Unforunaely i has a high proporion of noise compared o he real size of he credi risk (because of differences in managemen policies of banks over he credi cycle). Anoher credi risk indicaor is he percenage of adversely classified / nonperforming loans a individual bank level. This measure is mos ofen used in empirical research on credi risk deerminans (Dash, Kabra, 2010), (Louzis,Vouldis, Meaxas, 2011), (Jimenez Saurina 2006), (Boudriga, Boulila Takak, Jellouli, 2009), (Salas, Saurina, 2002), (Espinoza, Prasad, 2010). In (Quagliariello 2007) flow of new bad loans is also used insead of commonly used sock measure. As noed by (Quagliariello 2007) his indicaor can be inerpreed as a defaul rae. However he raio of new bad loans (classified in he reference period) o he performing loans ousanding don ake ino accoun recovery of loans, which were adversely classified in he pas. The drawbacks of loan loss provisions and flow of new bad loans as he measures of credi risk induces us o use he percenage of nonperforming loans or is proxy as he dependen variable in our research. Banks borrower and sraegy heerogeneiy may resul in differen sensiiviy of credi risk indicaors o macroeconomic condiions and microeconomic sraegies. For example, he raio of problem loans in savings and commercial banks in Spain have differen sensiiviy o GDP growh in (Salas, Saurina, 2002), he same holds in Greece for consumer and corporae loans in (Louzis, Vouldis, Meaxas, 2011). However mos of he sudies invesigae deerminans of credi risk using heerogeneous sample of 4 (Sorge, 2004) provides a comprehensive review of sress-es mehodologies

6 banks conrolling for banks differen sraegies by means of dummy variables and aking ino accoun share of bank resources used in specific niches (reail, corporae, ec. see below). Mos of reviewed sudies provide an empirical confirmaion ha afer conrolling for common for all banks facors (sysemaic or macroeconomic), ha deermine business cycle phase, borrowers financial condiions and heir abiliy o repay debs, credi risk of individual bank is also deermined by he riskiness of adoped business sraegy (idiosyncraic or microeconomic facors). Indeed, even in he same macroeconomic environmen banks have access o he borrowers of differen credi qualiy depending on bank s marke power, risk appeie, qualiy of screening, ec. In paricular, banks loan qualiy unevenness becomes apparen during he insabiliy periods. For example, (Salas, Saurina, 2002) found ha dispersion of problem loans raio across banks rises subsanially in he downward phase of he business cycle. To es he significance of bank-specific (microeconomic) facors of Greek banks credi risk (Louzis, Vouldis, Meaxas, 2011) firs esimaed a «baseline model» which included only macroeconomic facors. Then he auhors examined if he incorporaion of bank-specific deerminans helped o improve explanaory power of he model. (Salas, Saurina, 2002) considered significan effecs of microeconomic variables in heir problem loans equaion by means of esing he hypohesis ha all hese variables have coefficiens equal o zero. 3. Facors descripion a. Macroeconomic facors Mos of he sudies under review use GDP growh raes as he main indicaor of macroeconomic condiions and deb susainabiliy of wide group of borrowers. An increase in GDP growh raes ranslaes ino higher income and improves deb servicing capaciy of borrowers, which resuls in lower credi risk of banks. Some papers include solvency indicaors of individual economic agens: households and corporae secor (Salas, Saurina, 2002), (Głogowski 2008), cenral governmen (Louzis, Vouldis, Meaxas, 2011). Deb service cos is usually approximaed by real ineres raes on loans. Some auhors include asse prices (approximaed by inflaion rae, house price growh and sock marke growh) o ake ino accoun influence of collaeral inflaion, asse bubbles and wealh effec on banks credi risk

7 (Quagliariello 2007), (Nkusu 2011). In he sudies on banking secors of emerging markes auhors sress he imporance of aking ino accoun foreign currency exposure because in hese counries confidence o naional currency can be limied in comparison o inernaionally recognized currencies (U.S. dollar, euro). This resuls in large proporion of FX loans. The credi qualiy of hese loans is highly dependen on he exchange rae dynamics. To cach his effec (Głogowski 2008) and (Dash, Kabra, 2010) include real or nominal exchange rae of naional currency ino heir models of credi risk. b. Bank-specific facors To invesigae he relaive imporance of bank-specific facors for credi risk explanaion exising sudies consider he following lis of facors: resriciveness of banks lending policy and risk appeie (approximaed by loan or branch growh raes 5, lending rae, ne ineres margin, share of collaeralized loans ec.), marke power (approximaed by marke share or Lerner index), managemen efficiency (measured by cos o income raio), bank performance (profiabiliy), loan porfolio and income diversificaion (measured by bank size as a proxy for loan porfolio diversificaion, Herfindahl or enropy indexes for revenue diversificaion and indusry and region loan porfolio concenraion), solvency raio (capial adequacy raio). Some papers also inroduce bank profile and ownership srucure dummy variables o explain differences in risk profile of differen groups of banks. The lending policy of banks is expeced o be highly correlaed wih he credi risk indicaors. The possible explanaion of his phenomenon is he following: rapid loans growh reflecs decreasing lending sandards as i corresponds wih he reducion in ime dedicaed o consideraion of loan applicaions, decline in monioring qualiy, ec. and afer a ime (ofen during he economic conracion) i may resul in problem loans increase. Several empirical sudies find significan influence of pre-crisis credi expansion on he size of problem loans in banking secor (see, for example, Quagliariello (2007), Espinoza, Prasad (2010), Jimenez, Saurina (2005)). High value of lending rae and ne ineres margin can be a sign of deliberaely risky credi policy (high ex ane credi risk, buil in risk premium) ha can lead o rise of problem loans (ex pos credi risk). 5 (Głogowski 2008) poins ou ha his indicaor may be of limied usefulness because i may no reflec he accumulaion of credi risk in case if level of financial deph was small a he saring poin and economic agens rapidly increase use of financial services in an environmen where access o hem was limied before.

8 Besides, increase in ineres rae raises deb service coss for borrowers hus leading o excess defaul rae. However, mos auhors didn find significan influence of bank-specific lending rae on financial firm s credi risk. There is large body of lieraure providing convincing evidence in favor of conradicory hypoheses on bank s marke power sabiliy relaionship (marke power fragiliy, marke power sabiliy). However auhors use daa on differen ses of counries and differen measures of marke power and risk. The mos commonly used indicaors of compeiion in bank-level panel sudies are he Lerner index and concenraion raios such as Herfindahl-Hirschman index (HHI). In measuring bank individual risk auhors prefer non-performing loan raio (NPL), which is of primary ineres in our research, and Z-scores (Roy, 1952). The posiive relaion beween Lerner index and NPL as well as beween HHI in loan (deposi) marke and NPL are found in (Berger, Klapper, Turk-Ariss, 2008) which parially confirms compeiion-sabiliy view for he sample of 30 developed counries. From he opposie side, (Jimenez, Lopez, Saurina, 2007) reveal he srong evidence of compeiion-fragiliy nexus using he sample of Spanish banks. There is no consensus in he lieraure abou he relaion beween credi risk and bank efficiency. On he one hand, high values of cos efficiency indicaor may reflec reducion of resources allocaed o risk managemen and borrower s monioring, which leads o loan qualiy deerioraion («skimping» hypohesis, pu forward by (Berger, DeYoung, 1997)). On he oher hand, low cos efficiency indicaes low qualiy of bank managemen, hus leading o problem loans increase (assumed ha inefficien managers are unable o cope wih credi risk managemen - («bad managemen» hypohesis, (Berger, DeYoung, 1997)). Besides low values of his variable may induce banks o ake on more risk in order o improve profiabiliy a he expense of he qualiy of loan porfolio. The invesigaion of causaliy direcion beween cos efficiency and problem loans provide more empirical evidence in favor of «bad managemen» hypohesis (Berger, DeYoung, 1997), (Louzis, Angelos, Meaxas, 2011), (Quagliariello, 2007), (Podriera, Weill, 2008). A number of auhors consider influence of bank s pas performance measured by profiabiliy (ROA reurn on asses, ROE reurn on equiy) on fuure problem loans raio. In paricular

9 (Quagliariello, 2007) and (Głogowski 2008) examine if «income smoohing» hypohesis holds. This hypohesis implies ha banks earn more in ime of economic expansion in order o cushion ineviable deereoraion of loans qualiy during conracion. If his proposiion holds han lagged profiabiliy should have posiive sign of influence on credi risk. (Louzis, Angelos, Meaxas, 2011) es he same direcion of influence explaining i by «procyclical credi policy hypohesis». Their reasonong is ha bank managemen aimed a increasing marke share of financial firm may inflae earnings by means of more liberal lending policy («negaive NPL exension of credi») hus seeding he seeds of fuure problems. This hypohesis also predics posiive sign of profiabiliy influence. Empirical evidence suggess ha income sources and loan porfolio diversificaion are effecive means of lowering credi risk. Firs, involvemen in operaions no associaed wih credi risk aking (paymen ransacions, broking, ec. income diversificaion) allows banks o earn less risky income hus, reducing incenives o finance speculaive projecs. Second, having opporuniy o lend money o a diversified range of borrowers banks can successfully reduce heir impaired loans (minimizing risk of individual borrower). (Salas, Saurina, 2002) found empirical confirmaion of borrower diversificaion hypohesis. I is widely acceped in lieraure ha low capial adequacy raio is associaed wih higher probabiliy of bank s defaul, because i may induce managemen o involve in more risky projecs («moral hazard» hypohesis, (Berger, DeYoung, 1997)). The confirmaion of his saemen was found in (Salas, Saurina, 2002) and (Berger, DeYoung, 1997). (Głogowski 2008) menion ha he level of credi risk differs beween ypes of loans and classes of borrowers. I is obvious ha banks percenage of problem loans can be influenced by he composiion of is loan porfolio (weigh of reail / corporae loans). Differen ypes of borrowers have unequal deb susainabiliy under he same macro-environmen ceeris paribus. For example, (Louzis, Angelos, Meaxas, 2011) repor ha corporae loans defaul raes are more sensiive o worsening of macroeconomic condiions. (Głogowski, 2008) oulines ha in many counries morgage loans exhibi lowes defaul raes. To ake hese differences ino accoun (Głogowski 2008) explores significance of bank business profile by inroducing corresponding dummy variables (reail, corporae, universal banks,

10 ec.). The oher imporan deerminan of managemen qualiy and riskiness of he sraegy is he ype of bank ownership. (Boudriga, Boulila Takak, Jellouli, 2009) found ha foreign ownership has a posiive impac on loan qualiy as i promoes impors of human capial, managemen skills and echnologies and provides opporuniy o raise funds cheaply on inernaional markes. (Boudriga, Boulila Takak, Jellouli, 2009). Sae banks are repored o have more incenives o involve in risky projecs since hey are more prone o «oo big o fail problem». (Micco, 2004) invesigaed bank performance in 119 counries and concluded ha sae-owned bank in developing counries have higher raio of nonperforming loans. However, (Hu, Li, Chiu, 2004) found nonlinear U-relaionship beween problem loans and he percenage of governmen shareholdings in bank capial. (Głogowski, 2008) proposed an idea of specific ransformaions of facors o improve heir explanaory power. Firs, in his paper households income and deb susuainabiliy characerisics were muliplied by individual bank s share of reail loans. This allows o accoun ind individual bank s exposure o paricular source of risk. Second, (Głogowski, 2008) ook bank-level loan growh and capial adequacy raio in deviaions from secor median. This variables ransformaion helps o idenify bankspecific ype of risk excluding common for all banks effecs. c. Seleced explanaory facors Overview of credi risk deerminans used in exising empirical sudies allowed us o idenify several groups of facors included in bad loans equaions (a deailed lis of facors and expeced direcion of heir influence see in Appendix, Tables 2a-2b): macroeconomic facors including o naional economy characerisics; o households deb and income indicaors; o corporae secor indicaors; o prices of collaeral; o exernal secor and exchange marke condiions; banking secor facors including

11 o lending policy indicaors; o ineres raes; o efficiency indicaors; o performance characerisics; o diversificaion indicaors; o marke power index. Our lis of facors doesn conain any indicaors reflecing Russian governmen deb susainabiliy indicaors. A he same ime i is widely known ha governmen finance squeeze in developed European counries have imposed considerable risks on banks wih governmen bonds of risky counries (PIIGS and some ohers) in heir porfolios. However, Russian governmen has relaively low level of deb o GDP raio in comparision wih mos OECD counries (figure 1). Tha is why we did no consider risks originaing from governmen finance deerioraion. Figure 1. Cenral governmen deb o GDP in 2010, % Greece Ialy Belgium Porugal Unied Kingdom Iceland Israel Hungary France Ausria Unied Saes Ireland Neherlands Spain Poland Germany Turkey Finland Denmark Slovak Republic Czech Republic Canada Slovenia Sweden Korea New Zealand Mexico Norway Swizerland Luxembourg Ausralia Russia Chile Esonia Source: OECD, CMASF calculaions Rissian banks issue a high proporion of foreign currency loans (before he 2008 crisis he share of FX loans in oal loans was more han 20 percen). Borrowers abiliy o repay for hese loans is sensiive o he ruble exchange raes (especially naional currency depreciaions noiceably affecs deb burden and

12 deb service coss hus increasing defaul rae on hese loans). Tha s why following (Głogowski, 2008) we ake ino empirical consideraion nominal and real exchange raes and balance of paymen indicaors which can lead currency marke dynamics (see Table 2a in Appendix). Following (Berger, Hannan, 1998) we exend heir ideas abou individual Herfindahl-Hirschman indexes calculaed on he bank level. For his purpose we use weighs of differen kinds of asses (liabiliies) in oal bank's asses and hen muliiply hem by he banking indusry concenraion raios in he respecive markes of asses (liabiliies). These indicaors (B-HHI EA and B-HHI EL, see Table 2b in Appendix) represen bank involvemen ino differen asses or liabiliies markes and serve as asubsiue for Lerner index. Since he main purpose of our research is o separae he relaive imporance of macro- and bankspecific facors of credi risk we sared our empirical sudy from invesigaing correlaion beween hese groups of facors. I is obvious ha if we include boh ypes of facors as explanaory variables ino equaion of credi risk and a he same ime dynamic of bank-specific facors can a leas parially be explained by macroeconomic condiions we ll ge a bias in our esimaion of relaive conribuion of boh groups of facors. In paricular, if bank-specific facors are correlaed wih macroeconomic ones we will overesimae he relaive imporance of microeconomic facors. In his case macro facors will have direc effec on bank risk hrough heir own coefficiens in credi risk equaion and indirec effec hrough he influence on banking acions. The possible and he easies soluion of his problem is o avoid inclusion ino equaion hose bankspecific facors ha are ighly correlaed wih macro condiions. I is clear ha his correlaion can be mainained only by link beween dynamics common for all banks (sample mean) and macoeconomic environmen. Our correlaion analysis suggess ha only average loan growh raes, growh of loans o deposis raio, he share of non-ineres income in oal income and profiabiliy afer loan loss provisioning (ROAa) are closely correlaed wih macroeconomic facors approximaed by GDP growh raes (figure 2). Tha is why we are no able o include hese variables on individual bank level. Insead of his we can include hese bank-specific facors in deviaions from banking secor mean (ha are uncorrelaed wih macrofacors due o consrucion) see Table 2b in Appendix. Oher bank-specific

13 facors (ha are uncorrelaed wih macroecomonic facors) were included in levels. This approach allowes us o avoid bias in our assessmen and o esimae conribuion of each group of facors consisenly. Figure 2. Correlaion beween seleced banking facors (measured as sample average) and macroeconomic condiions (approximaed by GDP growh raes) Real loans growh rae, per year (%) and GDP growh rae, per year (%) - righ scale Growh of loans o deposis raio, per year (perc. p.) and GDP growh rae, per year (%) - righ scale Coefficien of pairwise correlaion = 0, Coefficien of pairwise correlaion = 0, июн.04 дек.04 июн.05 дек.05 июн.06 дек.06 июн.07 дек.07 июн.08 дек.08 июн.09 дек.09 июн.10 дек.10 июн июн.04 дек.04 июн.05 дек.05 июн.06 дек.06 июн.07 дек.07 июн.08 дек.08 июн.09 дек.09 июн.10 дек.10 июн RLns-growh-y GDP_growh_y dltd_y GDP_growh_y Non ineres income o oal income (%) and GDP growh rae, per year (%) - righ scale Reurn-on-asses afer loan loss provisioning (%) and GDP growh rae, per year (%) - righ scale Coefficien of pairwise correlaion = 0, июн.04 дек.04 июн.05 дек.05 июн.06 дек.06 июн.07 дек.07 июн.08 дек.08 июн.09 дек.09 июн.10 дек.10 июн.11 июн.04 дек.04 июн.05 дек.05 июн.06 дек.06 июн.07 дек.07 июн.08 дек.08 июн.09 дек.09 июн.10 дек.10 июн.11 NII-o-TI GDP_growh_y ROAa GDP_growh_y Source: Bank of Russia, Federal Sae Saisic Service, auhors calculaions As we have previously noed, we inend o use he percenage of nonperforming loans 6 as he dependen variable in our research. Unforunaely, Russian banks are no required o publish hei financial accouns in line wih inernaional reporing sandards. On he basis of Russian accouning 6 The enire loan becomes nonperforming if paymen of ineres or principal is pas due by 90 days or more

14 sandards i is impossible o esimae his indicaor. Bu for inernaional comparisons IMF uses an approximaion for Russian banks nonperforming loans defined as he share of problem and bad loans, ha is, loans of IV and V qualiy caegories according o Regulaion of he Bank of Russia 254-P. However, Bank of Russia doesn disseminae form 115 of individual banks records (only form 101 and 102 which will be he saisical base of our research see below). Tha is why we use he raio of overdue loans 7 o oal loans as he only available subsiue for nonperforming loans as he dependen variable. Figure 3 demonsraes ha here is a close dependence beween hese indicaors Figure 3. Percenage of nonperforming loans and overdue loans in Russian banking sysem Nonperforming loans o oal loans Overdue loans o oal loans Source: Bank of Russia, auhors calculaions 4. Daa descripion including dealing wih he ouliers We exploi wo ypes of deerminans of banks loan porfolio qualiy. They are as follows: 1. bank variables: on micro level colleced from Bank of Russia 8 ; on macro level as banking sample average; 7 Percenage of overdue loans includes only he overdue paymens of loan, no he inire loan as i n case of nonperforming loans 8 Publicly available financial accouns balance shee saisics (form 101) and profi & loss accouns (form 102), hp://

15 2. macroeconomic variables colleced from Federal Sae Saisics Service websie 9. We use monhly bank-level daa from balance shee saisics (form 101) and quarerly bank-level daa from profi & loss accouns (form 102) repored by Russian commercial banks over he period 2004Q1 2011Q2 (Table 1) 10. All he quarerly bank specific indicaors aggregaed on he basis of form 102 (ineres income, ineres and operaing expenses, ec.) are aken in annual erms, i.e. as moving sum of each indicaor values for he four previous quarers, o avoid he problems associaed wih seasonaliy. To ensure comparabiliy of he forms 101 and 102 we reorganize monhly bank-level indicaors coming from he form 101 o quarerly basis. Daa Sources: Variables Table 1. Sources and daa srucure for Russian commercial banks Balance shee saisics (form 101) Profi & Loss accouns (form 102) Asses: households and corporae loans, graned iner-bank loans, purchased securiies, oal asses, ec. Liabiliies: funds, deposis, araced iner-bank loans, foreign liabiliies, ec. Capial and performance: oal equiy, loan loss provision, profi Duraion monhly quarerly Availabiliy from January 2004 from 2004Q1 Income: ineres income, operaing income, ec. Expense: ineres expenses, operaing expenses, ec. The reporing of financial accouns has one special feaure in Russia i is no necessary for bank o make is individual daa publicly available. Accordingly, from quarer o quarer sample size of banks, who agreed o publish heir accouns, can vary widely from 705 o 1024 financial insiuions a he beginning of 2004 and a he end of 2010Q1, respecively. Following he aim of our research we exclude from he sample hose banks, whose lending sraegy is concenraed on banking owners business (affiliaed banks) insead of being based on marke condiions. This issue is closely relaed o he problem of financial accouns falsificaion underaken by Russian banks o saisfy supervisory sandards. To deal wih his problem and is possible negaive effecs on our esimaion resuls we sugges he following procedure of such banks idenifying o be furher excluded. 9 Daa on GDP, inflaion, exchange raes, unemploymen, ec: hp:// 10 Earlier daa is unavailable

16 Firs of all, we disinguish beween banks wih primarily corporae and reail lending sraegy due o heir differen risk profile reflecing in reurn-on-asse raio (ROAb 11 ) being equal approximaely 5-7% for he group of reail banks and only 2-3% for corporae banks, (Mamonov, 2011). If a a given poin of ime he share of corporae (reail) loans in bank s loan porfolio exceeds 80% we assume i o be a corporae (reail) bank 12. Oherwise, if boh share of corporae and reail lending exceeds 20% we classify such bank as universal financial insiuion. As a nex sep we ry o assess he scope of financial accouns falsificaion on he bank-level basis wihin each lending sraegy. For his purpose we compare he magniude of corporae overdue loan raio a he peak of he crisis wih is pre-crisis (normal) level (see Figure 4 as an example for he median bank in our sample). Figure 4. Percenage of overdue loans in reail and corporae loan porfolios Source: Bank of Russia, auhors calculaions Corporae banks. If a corporae bank provide cusomer lending on marke condiions and does no falsify is accouns han i mus experience a sizable increase in overdue loans due o boh macroeconomic deerioraion and (or) specificiies of is business sraegies. Such banking insiuions are of grea ineres for our furher research. Oherwise, a corporae bank is a falsifier and should be excluded 11 before loan loss provisioning 12 One should noice ha he average banking indusry share of corporae loans decreased significanly during observed ime period from 85% in 2004Q1 o 76% in 2011Q2 exhibiing a lile upward endency during he financial crisis

17 from he sample as an oulier. Figures 5 and 6 demonsraes corporae banks disribuion densiy of he increase of corporae overdue loan raio over Q Q1 2010, ha exhibis significan hump in he disribuion locaed around 0-1 percenage poins of he raio increase. Correspondingly, we assess he hreshold of corporae lending qualiy falsificaion a approximaely 1 percenage poins he lower values indicaes banks-falsifiers o be excluded from our sample. Noe ha he median value is wo imes higher (2.07 p.p.). There are 222 corporae banks locaed below he falsificaion hreshold accouning 21% in banking sysem corporae loan porfolio (excluding he big four Sberbank, VTB, Gazprombank, Rosselhozbank). Figure 5. Absolue frequency of deviaions of problem loans issued o corporae secor a he peak of he crisis (2010Q1) compared o pre-crisis level 13 (2008Q3), percenage poins Noe: esimaed upper hreshold of banks suspeced in falsificaions of qualiy of heir corporae loan porfolios is 1 percenage poin Source: Bank of Russia, auhors calculaions 13 values above 2.25 are hidden for illusraive purposes

18 Figure 6. Absolue frequency of deviaions of problem loans issued o households a he peak of he crisis (2010Q1) compaed o pre-crisis level 14 (2008Q3), runcaed from above, percenage poins Source: Bank of Russia, auhors calculaions Reail banks. We found no significan humps in increase of reail overdue loan raio during he laes crisis. Bu following he logic of cusomer lending based on marke condiions we qualify reail banks as falsifiers if hey exhibied increase in bad reail loan raio lower hen 0 provided ha share of reail loan in loan porfolio of such a bank is no lesser han 10% (for he sake of ouliers absence). We found ha here are 85 reail banks saisfying his condiion holding only 3% of banking sysem reail loan porfolio (excluding Sberbank and VTB24 he leaders of reail banking marke in Russia). Besides, we reorganize our sample excluding observaions below 1 s and above 99 h perceniles in all variables represening relaive indicaors on he bank level o deal wih he problem of significan ouliers. Ulimaely, afer all excluding procedures we have observaions from abou o of 14 values above 2.25 are hidden for illusraive purposes

19 quarerly bank level daa due o unbalanced panel. Our resuling sample covers up o 730 commercial banks represening approximaely 90% of oal asses of Russian banking sysem. 5. Model specificaion and mehodology Following (Salas, Saurina, 2002), we assume ha he share of overdue loans in loan porfolio is closely relaed o is values in previous periods, because overdue loans canno be immediaely wriendown and may remain on banks' balance shees up o several years. In oher words overdue loan raio shows a endency o persis over ime. I necessiaes he use of dynamic specificaion of economeric equaions describing he relaion beween overdue loans and is bank specific and macroeconomic deerminans insead of common saic equaions. Dynamic specificaions were also esimaed in (Jimenez, Saurina, 2005), (Quagliariello, 2007), (Espinoza, Prasad, 2010). Noneheless, we use boh saic and dynamic approaches o ensure robusness of our conclusions concerning he conribuion of micro- and macroeconomic facors o overdue loan raio. The firs one sands for he preliminary conclusions, while he second one brings us o he ulimae resuls Saic specificaion Following saic approach we wrie he overdue loan raio equaion as: OL i, ( m) ( m) ( BSi, k BI k ) + μi ν i, N1 N2 N3 N4 ( j) ( j) ( s) ( s) ( h) ( h) ( m) ξ + β M k + γ BI k + θ BSi, k + δ + j= 1 s= 1 h= 1 m= 1 = where (1) lower-case leers refers o bank i, quarer ( = Q Q2 2011) and quarer ime lag k ( k = 0, 1,..., 4 ). Number of banks varies significanly in differen specificaions (from 619 o 645 afer all ouliers excluding procedures described in Secion 4) depending on he choice of covariaes and due o he unbalanced panel of banks. OL i, overdue loan raio (combining reail and corporae borrowers) of bank i in quarer ; upper-case leers j, s, h and m refers o differen ses of macroeconomic (M), banking indusry

20 (BI), bank specific (BS) deerminans and deviaions of bank specific from banking indusry (BS BI) variables, respecively (see heir descripion in Secion 3) 15. N 1, N 2, N 3 and N 4 are heir respecive quaniies which vary in differen specificaions. i i, ν μ + composie error erm, such ha i μ represens individual effec of bank i and i, ν is idiosyncraic componen which is ( ) 2 0,... ν σ d i i ; δ θ γ β ξ,,,, vecors of parameers o esimae. In esimaing saic version of our models we apply boh well-known fixed and random effecs esimaors and also pooled OLS approach. The bes error erm specificaion are chosen according o F- es on individual effecs, Breush-Pagan LM-es on random effecs and Hausman es Dynamic specificaion We rewrie our saic eq. (1) in wo ways. Firs, we add he quarerly lags of dependen variable in he righ hand side of equaion o accoun for persisency of overdue loan raio. Second, following (Blundell, Bond, 1998) we combine dynamic equaion in levels wih is analogue in firs differences which exclude bank s fixed effecs and help us o ge consisen esimaion resuls. Ulimaely, we wrie a sysem of equaions as: ( ) ( ) + Δ Δ + Δ + Δ + Δ + Δ + Δ = Δ = = = = = = = = = i N m m k m k i m N h h k i h N s s k s N j j k j i i i i i N m m k m k i m N h h k i h N s s k s N j j k j i i i BI BS BS BI M OL OL OL BI BS BS BI M OL OL OL, 1 ) ( ) (, ) ( 1 ) (, ) ( 1 ) ( ) ( 1 ) ( ) ( 2, 1 1, 1,, 1 ) ( ) (, ) ( 1 ) (, ) ( 1 ) ( ) ( 1 ) ( ) ( 2, 1 1, 1, ν δ θ γ β α α ν μ δ θ γ β α α ξ These equaions are esimaed via he one-sep sysem GMM esimaor as inclusion of dependen variable lags ino he se of covariaes leads o inconsisency of FE esimaor. Besides, he esimaion of dynamic specificaion of overdue loan equaion can also solve he problem of endogeniy of some variables included in he righ hand side of he equaions. In case of bank-level panel daa he endogenous variables are hose bank-specific covariaes, which are simulaneously deermined wih he dependen variable. For example, we use real ineres rae on banks loans o cusomers as an explanaory 15 As we menioned in Secion 3, we include he fourh ype of covariaes ino regressions if pairwise correlaion beween banking indusry deerminan and macroeconomic environmen (proxied by real GDP growh) is more han 0.5 o deal wih he possible problem of mulicollineariy which lead o overesimaed conribuion of microeconomic facors o fied values of dependen variable (overdue loan raio). (2)

21 variable of overdue loan raio in dynamic specificaion a he same quarer because here are shor-erm loans in reail (consumer lending) and corporae (small and medium business) segmens. These ypes of loans may become overdue insanly wihin one quarer in he case of macroeconomic deerioraion. Bu a he same ime he ineres raes are affeced by increasing of overdue loans because banks managers have he incenives o compensae for heir declining ineres incomes by means of higher price charged on loans. 6. Esimaion resuls In his secion we provide esimaion resuls of boh saic and dynamic specificaions of overdue loan raio equaion. Here we inerpre he resuling empirical links beween overdue loan raio (an indicaor of bank insabiliy) and indicaors of marke power and an indicaor of bank operaing efficiency. Then we esimae he conribuion of micro- and macroeconomic facors o he overdue loan raio wihin each of he presened equaions. Finally, we generalize our conclusions and provide policy implicaions. Firs of all, we presen descripive saisics of dependen variable and covariaes which were finally included in boh saic and dynamic equaions (see Table 3 in Appendix). I shows ha he number of macro- and microeconomic deerminans equals 8 and 11, respecively, and one specific variable which is he produc of micro- and macroeconomic facors. Besides, afer all excluding procedures, described in Secion 4, we have no significan ouliers in bank-level daa, bu considerable heerogeneiy of banks is sill remain. For example, he maximum real ineres rae on loan is enfold larger han he median value (7.14%). The same resul is for he ROE (before loan loss provisioning) which reflecs ha banks in our sample pursue differen lending sraegies. Reail lending (abou 4% of sample) is he highes yielding segmen of Russian banking marke while corporae sraegy (29% of banks) is radiionally less profiable. Parameers esimaes of fixed effecs regressions (saic specificaion) are presened in he four panels of Table 4 in Appendix. Mos coefficiens are of high significance and are robus o changes in specificaions. In he absence of dependen variable lags in he righ hand side of he equaions we

22 achieve abou 55% o 58% of goodness of fi calculaed on he basis of leas squares dummy variables (LSDV) approach. There is a srong evidence in favor of presence of individual effecs in our sample according o F-es, which are fixed raher han random in all specificaions as Hausman es shows. In Table 6 in Appendix we presen marix of pairwise correlaions beween differen facors included in one of he fixed effecs models (FE2a). I demonsraes no significan pairwise correlaion beween he explanaory variables. Economeric esimaion of FE models has shown ha qualiy of loan porfolios of all banks is ighly correlaed wih macroeconomic condiions. Firs, GDP growh raes demonsrae significan and robus o specificaion inverse influence on he percenage of overdue loans. During he periods of economic expansion credi risk ends o be lower while conracion periods are followed by loan qualiy deerioraion. Second, disinflaion eners ino equaion significanly. The laer indicaes ha sudden sop of income and asse price inflaion can reduce borrowers deb susainabiliy. Third, our resuls confirm hypohesis of significan influence of exchange raes devaluaion on he qualiy of loans denominaed in foreign currency. Fourh, reail banks are more exposed o business cycle since changes in unemploymen level is a source of addiional push up effec on credi risk 16. Our resuls provide srong evidence in favor of derimenal effec running from bank-level ineres raes o is loan qualiy. An increase in one sandard deviaion of real ineres rae (approx. 7.5 percenage poins across he sample of banks) causes rise of overdue loans in 1.6 percenage poins in he nex one quarer. I is he larges negaive effec among all bank specific variables. We found ha increasing bank-level concenraion (HHI 17 A ) represening banks involvemen ino differen markes of asses 18 leads on average o declining of overdue loan raio confirming he compeiion-fragiliy view. An increase in one sandard deviaion of HHI A (approx. 294 basis poins) causes decrease of overdue loans in percenage poins in he nex wo quarers. The laer reflecs ha improvemen in he loan qualiy is no an immediae process. Thus, if a bank has an 16 This phenomenon can be explained by low share of morgage loans in oal loans o households (abou one-quarer). Nonmorgage loans which ake he prevailing share of households loans are considered as more risky han corporae ones. 17 bank-level averaged HHI across differen markes of asses 18 We consider he main four domesic markes of banking asses including reail lending, corporae lending, iner-bank lending and privae & public securiies markes. The mean values of HHI concenraion raios for hese markes are 1520, 1148, 300 and 2330 poins, respecively.

23 opporuniy o expand is share on more concenraed banking markes hen his bank can exrac addiional profis creaing more financial buffers o absorb differen kinds of shocks. This resul remains robus when we employ radiional HHI calculaed for Russian loan marke. Our resuls also reveal he possible opposie effec of banks profiabiliy on is loan qualiy. I shows ha increases in profiabiliy may be achieved by banks engaging in riskier lending sraegies which imply higher credi risks in fuure (procyclical credi policy, income smoohing). Thus, we found ha an increase in one sandard deviaion of ROEb (15.8 percenage poins) leads o a moderae rise of overdue loan raio in only percenage poins in he nex four quarers. Noneheless, his negaive effec is abou four imes lower han he posiive effec of increasing concenraion. In addiion, we found ha banks providing lending sraegy oo rapidly should face loan qualiy deerioraion in fuure. In oher words, if a banks yearly increase in loan-o-deposi raio deviaes from banking sysem more han in 70 percenage poins i causes 0.13 percenage poins increase in overdue loan raio in nex four quarers. Again, his negaive effec is much lower han he posiive concenraion effec associaed wih lending expansion. One of he mos ineresing resul concerns he revealed magniude of negaive effec running from inefficiency o loan qualiy. We found ha an increase in one sandard deviaion of operaing coso-income raio (0.18) raises overdue loan raio in in he nex four quarers in favor of bad managemen hypohesis. I implies ha if a bank achieves success in expanding is shares in differen concenraed markes bu becomes inefficien due o he quie life effec is managers will face wih he loan qualiy deerioraion in near fuure as he posiive effec of increased concenraion is neuralized by he negaive one coming from inefficiency. Anoher ineresing resul suggess ha if a bank ends o deviae from he banking sysem in expanding is share of non-ineres income (e.g. increasing diversificaion) oo rapidly i have o face wih he loan qualiy deerioraion. This is because such banks managers swich heir effors from lending sraegy o oher kinds of aciviies (securiies, fee & commission income, ec.) which may cause he worsening of credi monioring of remaining borrowers. We nex describe he esimaion resuls obained in dynamic version of overdue loans equaion

24 using one-sep sysem GMM (see Table 5 in Appendix). As we expeced here is a srong ineria in dependen variable and i made us o include wo quarer lags of overdue loan raio o ake ino accoun all possible auocorrelaion in he depended variable. Again, we provide he correlaion marix of explanaory variables for he sake of pairwise mulicollineariy concerns (see Table 7). In dynamic specificaion of overdue loans equaions boh GDP and exchange rae devaluaion demonsrae he same influence on he dependen variable as in he saic version. One of he new facor in his specificaion is loan-o-deposi raio a he banking sysem level. I reflecs common for all banks dispariy beween araced and allocaed financial resources. The increase of his dispariy a he macro level can be he resul of macroeconomic overheaing led o over-opimisic expecaions of managers, which inensified rivalry for marke share and profiabiliy. This herd behavior resuls in problem loans increase wih large lag a year and a half. Apar from his we found a posiive effec of income diversificaion a he level of banking sysem as a whole on average loan porfolio qualiy. This is explained by lowering pressure o involve in risky projecs due o emergence of oher sources of income. Besides we obained negaive influence of collaeral price growh raes (approximaed by house prices) on credi risk. The effec is simulaneous and is likely o reflec credi squeeze due o banks reducion of risk percepion as a resul of collaeral price drop during economic conracions. As our esimaes show, resuling credi raioning only exacerbaes he problem of defauls on loans. To ensure he robusness of our previous findings we changed lag srucure of bank specific explanaory variables in dynamic specificaions and achieve basically he same resuls. As in he saic version boh he profiabiliy of bank business sraegy and real ineres raes charged on loans lead o an increase in credi risk 19. The disincive feaure of dynamic version is he aemp o accoun for possible nonlinear relaionship beween overdue loan raio and bank concenraion of liabiliies (HHI 20 L ), efficiency and capializaion. In all hree cases we found more or less srong suppor for he exising of 19 A he same ime we didn obain confirmaion ha rapid income diversificaion negaively affecs banks exposure o credi risk a he bank level. Moreover, we found some evidence in suppor of he conrary view on he banking sysem level. Ulimaely, we rea his resul wih cauion. 20 The analogue of individual concenraion of asses (HHI A ) calculaed for he six markes of liabiliies reail accouns and deposis, corporae accouns and deposis, borrowed funds from iner-bank marke, borrowed funds from he Bank of Russia, banks securiies issued, and banks foreign liabiliies. The mean values of HHI concenraion raios for hese markes are 2719, 699, , 414 and 426 basis poins, respecively.

25 facing upward parabolic relaionships. Firsly, he inflecion poins of HHI L esimaed in differen specificaions from 1588 o 1644 divide our sample in proporion 2:1 which implies ha approximaely 2 / 3 of he observaions lie below he inflecion poins. Increasing concenraion up o some exen posiively affecs loan porfolio qualiy because he more a bank involves ino differen liabiliies markes he more i has o pay for funds. The laer necessiaes loan qualiy improvemen o provide more ineres income ( marke power-sabiliy effec). Bu afer some poin of concenraion here is a quie life effec, e.g. a bank deepens ies wih is deposiors considerably and begins o exrac addiional (quasimonopoly) ren by lowering paymens for funds which allow i o weaken borrowers monioring procedures. The laer may resul in loan qualiy deerioraion ( marke power-fragiliy effec). Thus, our resuls confirm he heoreical findings of (Marinez-Miera, Repullo, 2010) concerning nonlinear relaionship beween compeiion and risk. Efficiency esimaes resuls show ha abou 2 / 3 of daa lie below he inflecion poin ( ) in he hird and fourh equaions 21 represening skimpers in Russian banking sysem. Thus, he oher par of he sample consiss of bad managers. Also, our analysis revealed ha he vas majoriy of observaions (reflecing 90% of Russian banks) lie below he inflecion poin (39%) of equiy-o-asse raio, e.g. more capialized banks end o be less risky. We nex provide facors decomposiions in various esimaed equaions for he median bank in our sample. As depiced in Figures 8a and 8b (see Appendix) reflecing fixed effecs esimaion resuls he overdue loan raio was mainly influenced by deerioraion of macroeconomic facors ( macro + on he figures) namely he decline of GDP and weakening of exchange raes during he crisis of Also o some lesser exen a se of microeconomic facors ( micro + ) basically higher real ineres rae caused an increase in overdue loans. The same is rue for he dynamic esimaion resuls (see Figures 8c and 8d in Appendix) wha provides robusness o our conclusions. Facors from boh macro- and microeconomic sides which are o be responsible for overdue loans decrease ( macro and micro, respecively) didn play significan role afer and before he laes crisis. We also aggregae facors decomposiions resuls from all of he esimaed equaions. We calculaed he increases in all groups of facors over he period of overdue loans growh, namely 2008Q3 21 he fifh equaion in Table 5 implies only 1 / 3 of daa lying below he inflecion poin (0.50).