1 Uncerainy and Inernaional Banking * Claudia M. Buch (Deusche Bundesbank) Manuel Buchholz (Halle Insiue for Economic Research) Lena Tonzer (Halle Insiue for Economic Research) July 2014 Absrac We develop a new measure of uncerainy derived from bank-level daa. We apply he measure of firm-level uncerainy developed by Bloom and ohers (2012) o banking. Uncerainy is measured as he cross-secional of shocks o banking-secor specific variables. We hen analyze how uncerainy in banking affecs lending by domesic and foreign-owned banks. We find ha, firs, higher uncerainy in banking has negaive effecs on bank lending. Second, he effec is heerogeneous across banks: Lending by banks which are beer capialized and have higher liquidiy buffers ends o be affeced less. Third, foreign-owned banks do no reac differenly o uncerainy in he hos counry compared o domesically-owned banks. JEL-codes: G01, F34, G21 Keywords: Inernaional banking, uncerainy, financial inermediaion * Corresponding auhor: Claudia Buch (Prof. Dr.), Deusche Bundesbank, Wilhelm-Epsein-Sraße 14, Frankfur am Main, Germany, Manuel Buchholz (MSc), Halle Insiue for Economic Research, Germany, Lena Tonzer (PhD), Halle Insiue for Economic Research, Germany, This paper has been wrien for he conference Inernaional Banking: Microfoundaions and Macroeconomic Implicaions, organized by he Inernaional Moneary Fund (IMF) and he Duch Naional Bank (DNB). The auhors would like o hank he ediors Pierre-Olivier Gourinchas and Luc Laeven, one anonymous referee, Franziska Bremus, Jacob de Haan, Marlene Karl, Michael Koeer, Felix Noh, Kaheryn Russ, Gregor von Schweiniz, and Emmanuel de Veirman for helpful commens. We hank Florian Hüfner for efficien research assisance. All errors and inconsisencies are solely in our own responsibiliy. The opinions expressed in his paper are hose of he auhors only and no necessarily hose of he Deusche Bundesbank or is saff.
2 2 1 Moivaion Since he oubreak of he financial crisis, many counries experienced sagnaing or even declining levels of bank credi. Boh demand and supply side effecs are behind his decline (Corne and ohers 2011). Banks have also wihdrawn from inernaional markes a a large scale. In his paper, we analyze he role of increased uncerainy in he banking secor for he decline in bank credi. We develop a new measure for uncerainy ha explois bank-level informaion, and we explore he impac of uncerainy in banking on he lending behavior of domesic and foreign-owned banks. By analyzing he link beween bank lending and uncerainy, his paper conribues o a large body of research documening he impac of uncerainy on invesmen. In a recen survey, Bloom (2014) shows ha uncerainy increases in recessions and ha i has a negaive impac on shor-run hiring and invesmen in he manufacuring secor. Moreover, measures of uncerainy based on firm-level micro-daa are srongly counercyclical and negaively affec economic growh. The reason migh be ha firms exercise an opion value of waiing : The higher he degree of uncerainy, he more firms benefi from posponing invesmen projecs, in paricular if hey are irreversible (Bloom, Bond, and Van Reenen 2007). Similar o an invesmen by a nonfinancial firm, bank lending is a longer-erm conracual arrangemen. Consequenly, i migh be beneficial o pospone he loan decision in he presence of uncerainy. Uncerainy can affec banks' financial inermediaion funcion hrough various channels. Firs, banks inermediae shor-erm funds ino long-erm loans. This exposes hem o liquidiy risk and mauriy mismach. In uncerain imes, refinancing in inerbank markes migh become more difficul leading banks o resrain credi supply. Second, banks reduce informaion asymmeries and faciliae access o credi. In an environmen characerized by higher uncerainy, credi risk increases and banks may resric lending o informaion-sensiive borrowers. Third, he probabiliy ha banks are hi by large shocks increases in uncerain imes such ha invesors demand a higher funding premium. Hence, banks migh face ighened exernal financing consrains which resric he abiliy o provide loans as shown by Valencia (2013). Higher uncerainy in he banking secor can hus be considered o be a key facor behind he relucance o lend domesically and he wihdrawal of inernaional banks from foreign markes. However, here are surprisingly few applicaions of he lieraure on firm-level uncerainy for banks. In his paper, we consruc a measure of uncerainy
3 3 based on bank-level daa and analyze is impac on bank lending of boh domesic and foreign-owned banks. We use a daase which builds on Bankscope and on informaion on foreign ownership of banks provided by Claessens and van Horen (2014). These daa are used o generae measures of uncerainy in banking derived from bank-level daa and o capure he degree of inernaionalizaion of banks. Wih hese daa a hand, we ask hree research quesions. Firs, how can we measure uncerainy in banking and wha have been paerns of uncerainy in banking during he crisis? Uncerainy is ofen measured hrough flucuaions (he volailiy) of highfrequency ime series such as sock prices. The advanage of his mehod is ha i allows analyzing shor-run changes in uncerainy. The disadvanage is ha i is applicable o lised banks only. For Europe, smaller banks accouning for a significan fracion of he marke are no covered. No only are marke daa unavailable for hese banks, relevan daa are also available a a low (annual) frequency only. We hus use he of bank-level shocks o growh raes in oal asses, shor-erm funding, produciviy, and profiabiliy as an alernaive measure for uncerainy in banking. Descripive saisics show ha he of bank-level shocks has increased during he crisis, which we inerpre as higher uncerainy. Moreover, he of bank-level shocks is no highly correlaed wih sandard measures of uncerainy. This suggess ha we measure a disinc feaure of uncerainy. Second, how does uncerainy affec bank lending? We closely follow previous lieraure analyzing he impac of funding shocks on banks invesmen paerns. Corne and ohers (2011) have developed an empirical model which can be used o analyze he impac of funding shocks on he lending behavior of banks. 1 They find ha, during liquidiy crises, banks wih a relaively large share of illiquid asses reduce lending more. Valencia (2013) analyzes he relaionship beween loan supply and uncerainy for a sample of US commercial banks and he period He shows ha banks wih relaively low levels of capializaion decrease lending more if uncerainy increases. Uncerainy is hereby measured as he of professional forecass or sock marke volailiy. We insead develop a cross-secional measure of uncerainy derived from bank-level daa. We find ha higher uncerainy in banking has negaive effecs on bank lending. The effec is heerogeneous across banks: Lending by banks which are beer capialized and which have higher liquidiy buffers is affeced less. These resuls are essenially in line wih hose by Valencia (2013). 1 For research on he ransmission of shocks across counries hrough inernaionally acive banks, see he work by Ceorelli and Goldberg (2011, 2012).
4 4 Third, does he inernaionalizaion of banks have an impac on heir response o higher uncerainy? Previous lieraure shows ha inernaionally acive or foreign-owned banks decreased heir loan supply more han domesic and locally funded banks (De Haas and van Lelyveld 2014, Ongena, Peydro and van Horen 2013). This rerenchmen of inernaional lending can be aribued o a fligh home effec (Giannei and Laeven 2012), and i depends on he geographical disance of he foreign marke (De Haas and van Horen 2013). We conribue o his lieraure by asking wheher he ownership srucure of banks maers for he response of individual banks o uncerainy. We do no find conclusive evidence ha foreign-owned banks are affeced less by uncerainy in banking in he hos counries han domesically-owned banks. In he following second par, we presen a sylized model o illusrae he concep of uncerainy. In par hree, we describe he daa ha we use and discuss how we measure uncerainy in banking. In par four, we show he evoluion of uncerainy in banking across counries and ime, and we relae uncerainy in banking derived from bank-level daa o alernaive measures of uncerainy. In par five, we presen our empirical resuls linking bank lending of domesic and foreign-owned banks o uncerainy. In par six, we conclude. 2 A sylized model To illusrae he concep of uncerainy which underlies his paper, we presen a sylized model. Based on he model by Shin (2012), we can assess he impac of higher uncerainy in banking on banks' loan supply. Assume ha a ime, he balance shee of he bank looks as follows: Asses Loans Liquid Asses/Cash Liabiliies l Deposis c Equiy The bank makes loans l a ime and receives a reurn (=loan rae) a ime +1 of ~ r + 1. The loan rae ~ r + 1 is risky as borrowers migh no pay back he full loan bu larger han zero in expecaion ( E [ ~ 1 ] > 0 ). The deposi rae and he reurn on liquid asses are assumed o be risk free and equal o zero. r + The value of equiy a ime +1 is hen given by: e l ~ r c d e ~ + 1 = ( ) + = + r + 1l (1) d c
5 5 The bank defauls in +1 if he value of he equiy is negaive ( e < +1 0), i.e. if he reurn on loans is smaller han he amoun of equiy per uni of loans available o cover poenial losses: ~ r +1 < e l 2.1 The VaR consrain (2) We assume ha he bank is risk-neural bu operaes under a value a risk (VaR) consrain which is given by: e Pr ob r 1 < l ~+ 1 α (3) The value a risk is defined as he loss no o be exceeded wih probabiliy1 α, i.e. VaR α = e / l. We can hink of he VaR consrain as reflecing how he bank manages is risk, or, alernaively, a minimum capial requiremen imposed by he regulaor. The VaR consrain can also be defined as a deviaion from he mean measured in erms of sandard deviaions σ, which is assumed o be known a ime, i.e. ( ~+ < µ φσ ) 1 α Pr ob r 1 whereφ is some consan. (4) We assume ha he bank maximizes is shareholder value e + 1 a ime +1. In he absence of he VaR consrain, he risk-neural bank would give ou as many loans as possible. The reason for his is ha he expeced reurn is larger han zero. In principle, he size of he balance shee would hus be indeerminae. However, he VaR consrain under which he bank operaes deermines he size of he loan porfolio. This can be seen by combining equaions (3) and (4): e l = (5) φσ µ 2.2 Uncerainy in banking and loan raes We assume ha loan raes of bank i follow a sochasic process wih ime-varying volailiy: r = µ + σε i+ 1 i i+ 1 where E[ ε i ] = 0 and ε i ~ N(0,1) which implies ha he mean of he loan rae condiional on ime informaion ( I i ) is consan, so Er [ i+ 1 I i ] = µ. While he assumpion of i µ i being consan migh appear oo resricive, is inerpreaion can easily be generalized o he (6)
6 6 prediced value of he loan rae a ime wihou making a specific assumpion on he underlying predicion model. Therefore, we can simply refer o µ i as he prediced par of he loan rae and o σ ε i+ 1 as he unprediced par for each bank i. The volailiy of he bank-specific shock ε i + 1 o he loan rae is ime-varying. Regarding he iming convenion, we follow Bloom e al. (2012) and assume ha banks know in advance abou any poenial change in business condiions, which would be refleced in a change in he disribuion of shocks and hus he volailiyσ. A higher σ can be inerpreed as higher uncerainy because i widens he disribuion of ~ r i+ 1. Hence, i consiues a measure of uncerainy in banking. More specifically, we assume ha in, he bank can condiion is porfolio decision on he level of uncerainyσ. While he bank knows ha he disribuion of shocks has widened, he bank does no learn abou he realizaion of he loan rae ~ r i+ 1 before +1. In he following, we show how higher uncerainy in banking lowers he volume of loans relaive o oal asses of a bank. Insead banks hold more liquid asses as hey yield a safe reurn of zero. 2.3 Uncerainy in banking and loan supply Saring from he opimal size of loans on he asse side given by equaion (5) which is assumed o hold in all periods, we can derive he change in loans from -1 o relaive o oal asses a ime -1 ( a 1 = e 1 + d 1). This will consiue he dependen variable in he empirical analysis below: 2 e 1 r e (7) l a l l 1 1 ϕσ 1 µ a 1 = = a a ϕσ µ ϕσ µ We assume ha a ime, he bank learns abou changes in uncerainy in banking and can incorporae his informaion ino is loan supply decision. The effec of higher uncerainy on he change in loans relaive o oal asses in -1 is given by he firs parial derivaive of equaion (7) wih respec o uncerainy in banking σ : l ϕe 1 r (8) 1+ a a ϕσ µ = < 0 σ ϕσ µ ( ) 2 We skip he bank index i in he following.
7 7 The resul implies ha he bank reduces he volume of loans on is balance shee, i.e. i supplies less loans, if uncerainy in banking increases. The inequaliy holds as long as r > µ ϕσ 1, which implies ha he bank is solven in (see equaion (2)). 2.4 The role of he capial buffer In he empirical analysis below, we invesigae how he response of banks o uncerainy depends on heir characerisics. One of hese characerisics is he capial buffer ha a bank holds. A bank migh volunarily choose o hold capial above he regulaory requiremen o shield iself agains unexpeced losses. This is paricularly imporan under he aspec of uncerainy. The reason is ha from he perspecive of he individual bank, uncerainy affecs he probabiliy o incur such an unexpeced loss. Wihin he logic of he model, a bank wih a capial buffer is subjec o a more sringen (bu volunarily chosen) VaR consrain a ime -1 which can be relaxed a ime ( φ 1 > φ ). We show in he Appendix ha a bank holding a capial buffer reduces he loan volume by less as long as i sill ges a posiive reurn on is loans ( r > 0 ). This model illusraes a specific mechanism how uncerainy in banking - modeled as an increase in he sandard deviaion - affecs banks' behavior. We ransfer his idea o our empirical analysis and measure uncerainy as he cross-secional of bankspecific shocks. In realiy, banks migh be affeced by uncerainy hrough a range of oher channels. Therefore we vary he variable from which he measure of uncerainy in banking is derived. We also include oher bank-level variables besides capial o conrol for heerogeneiy in banks' business models and liquidiy managemen. 3 Daa and measuremen issues In his paper, we ask hree quesions: How can we measure uncerainy in banking? How does uncerainy in banking affec bank lending? And are domesic and foreign-owned banks affeced differenly? In his secion, we discuss he daa sources ha we use and oher issues relaed o measuremen. 3.1 Bank-level daa Banks' balance shee and income saemen daa are aken from Bankscope. Our sample is based on banks in 48 counries which belong o he OECD, he EU, and/or he G20. This ensures having a sufficienly homogenous se of indusrialized counries while, a he same ime, exploiing a sufficien degree of heerogeneiy wih regard o uncerainy
8 8 in banking. We keep only counries wih more han 50 bank-year observaions and banks wih a leas five consecuive observaions. The sample period spans he years Our explanaory variables include balance shee srengh and banks' liquidiy risk managemen as in previous papers in he field such as Corne and ohers (2011). We consruc hese variables from Bankscope, and we winsorize hem a he op and he boom percenile. Liquidiy is measured as he raio of liquid asses o oal asses (in %). Capializaion is measured as he Tier 1 regulaory capial relaive o oal asses (in %). We conrol for cusomer deposis relaive o he oal size of he balance shee by including he deposis o asses raio (in %). Addiionally, we include he log of oal asses (in housands US-dollars). We also include he fracion of commied loans relaive o he sum of commied loans and oal asses (in %). For more informaion, see he daa descripion in he Appendix. The corresponding summary saisics are provided in Table 1. We use sandard procedures o correc for ouliers and implausible values. Firs, we exclude observaions for which oal asses are missing as well as he boom percenile of oal asses. Second, o accoun for mergers, we drop observaions for which he annual change in asses is larger han 40% (Corne and ohers 2011). Third, we drop observaions if asses, equiy, or loans are negaive. We do he same if loans o asses, equiy o asses, or non-performing loans raios are larger han one. Fourh, a bank is kep in he sample if i is a bank holding company, a commercial bank, a cooperaive bank, or a savings bank. To measure he degree of inernaionalizaion of individual banks, we resor o daa compiled by Claessens and van Horen (2014). These daa provide informaion abou he ownership saus of a given bank. The daase covers 5,324 banks in 137 counries for he period Counries are included in he sample if hey have more han five acive banks in For advanced counries, only he larges 100 banks (based on heir asses in he year 2008) are included. Despie hese resricions, 90 percen of a counry's banking sysems' asses are covered. As such, he daabase provides comprehensive informaion on banks' ownership saus. We exploi informaion on wheher a bank is foreign or domesically-owned. In addiion, if a bank is foreign-owned, we know he counry of origin of he larges foreign shareholder. We can hus es wheher he lending decision of a foreign-owned bank differs from a domesically-owned bank, and we can conrol for uncerainy in banking in he residence counry of he larges foreign shareholder. We mach hese daa o he banklevel daa obained from Bankscope.
9 9 3.2 Measuring uncerainy in banking Cross-secional as a measure of uncerainy To measure he impac of uncerainy on bank lending, we concepualize he erm uncerainy as follows. Times of uncerainy are imes during which fuure oucomes become less predicable. For insance, he abiliy of individual banks o forecas fuure performance of borrowers or he availabiliy of funding migh decline in he presence of higher uncerainy. One reason for which predicabiliy decreases can be ha he underlying disribuion of shocks o he oucome variable widens. In he heoreical model, his was refleced by a widening of he disribuion of shocks o he loan rae. Alernaively, one could imagine a widening of he disribuion of he repaymen performance of firms. This suggess measuring uncerainy in banking as he of bank-level shocks. Empirical measures of uncerainy include (implied) sock marke volailiies as well as measures based on firm-level sales growh or produciviy shocks. Ofen, uncerainy is measured using (lagged) sock price volailiy as a measure of hisoric volailiy (Bloom 2007). This approach is based on high-frequency marke daa. Similarly, measures of implied volailiy draw on marke daa such as prices of sock opions (Sein and Sone 2012). Given ha sock marke based uncerainy measures have been used in many previous sudies, resuls are easily comparable. However, for many applicaions of ineres, such high-frequency marke daa are no available for all firms. This is he case in banking, which we sudy here. Reliable marke daa on banks share prices are difficul o obain for many emerging markes and developing counries. Even for key developed marke economies such as Germany, sock marke daa would resric he sample o a very small se of lised banks. For his reason, we need a measure of uncerainy which can be compued based on lower frequency balance shee or profiabiliy daa. Jurado, Ludvigson, and Ng (2013) argue ha a meaningful measure of uncerainy needs o relae o he unprediced componen of a variable. We hus do no use balance shee or profiabiliy daa as such bu exrac he unexplained componens of bank-level variables. This is similar o De Veirman and Levin (2014) who derive firm-specific volailiy measures from residuals of sales or earnings growh regressions of US firms. Consequenly, we compue he cross-secional across hese unexplained componens across all banks in a given counry and year. This implies ha he increases if he disribuion of hese unexplained componens widens: on average across
10 10 all banks, he fuure becomes less predicable. An increase in he cross-secional of shocks can hus be inerpreed as a higher degree of uncerainy in banking. This is in line wih he approach and findings of Bloom and ohers (2012). They show ha he cross-secional derived from firm-level daa can be a useful approximaion for uncerainy, and ha i can be used o explain variaions in business cycle movemens across ime and space Applicaion o banking Bloom and ohers (2012) compue he cross-secional of produciviy shocks based on a sample of US manufacuring firms. To he bes of our knowledge, he concep o use he cross-secional o measure uncerainy has no ye been applied o banking. In banking, produciviy is more difficul o define compared o manufacuring because he disincion beween inpus and oupus is less clear (Degryse, Kim, and Ongena 2009). Deposis, for example, may be considered as being an inpu ino he producion of loans, bu overdraf deposis migh also urn ino loans. Also, banks have o balance he opimal use of inpus and oupus o generae sufficien reurns while also managing he risk of heir operaions. The resuling heerogeneiy in business models needs o be aken ino accoun. We hus do no consrain our analysis o bank produciviy bu calculae four annual cross-secional measures: Dispersion of shocks o oal asse growh: We use he of shocks o oal asse growh as a proxy for asse-side shocks affecing banks. These asse-side shocks can be relaed o loan demand shocks bu hey can also capure oher facors affecing he volume of banks' asses. Dispersion of shocks o shor-erm funding growh: In uncerain imes, access o funding migh differ significanly across banks. Banks which heavily rely on cusomer deposis may be affeced less by a funding shock han banks relying on wholesale funding. As a resul, he of shocks o shor-erm funding across banks widens. We measure shor-erm funding as deposis from banks, repos and cash collaeral, oher deposis, and shor-erm borrowings. Dispersion of shocks o produciviy growh: We esimae bank produciviy using an empirical mehodology in he spiri of Levinsohn and Perin (2003) and applied o banks by Nakane and Weinraub (2005) or Buch, Koch, and Koeer (2009): ln yi = β 0 + β l xi + β k ki + β mmi + ωi + η i. Bank oupu is given by y i, xi denoes he free inpu variables, ki he fixed inpu and mi he inermediae inpu. The error
11 11 consiss of an unobserved produciviy ermω i and a random erm η i. The approach accouns for simulaneiy beween produciviy and he facor inpu choices of banks. This is achieved by inroducing he inermediae inpu which correlaes wih produciviy. Produciviy shocks hus primarily accoun for supply-side facors. The oupu of banks is defined as he oal lending volume. We choose wo free inpu variables. The firs is oal long-erm funding. The second accouns for bank saff and is proxied by personnel expenses. Banks have o mainain branches or subsidiaries o provide loans. These canno be adjused rapidly and we capure he fixed inpu by fixed asses. For he inermediae inpu good, we choose oal equiy. Dispersion of shocks o profiabiliy (RoA): During crisis imes, adverse shocks become more likely. This can cause he disribuion of profiabiliy shocks o widen. These shocks can, for insance, be relaed o an increase in credi risk. Profiabiliy is proxied by reurns on asses (RoA) defined as he raio of operaing profis o oal asses (in %). Uncerainy in banking is hen measured as he cross-secional of shocks for each of hese four variables. To compue he cross-secional of shocks, we proceed in wo seps. In a firs sep, we derive bank-year specific shocks for each of hese four variables from he following regression model: log( X ij ) log( X ij 1 ) = log( X ij ) = α i + α j + ε ij (11) where log( X ij ) is he growh rae of bank i's asses (shor-erm funding or produciviy) in percen a ime in counry j and α i are bank fixed effecs. 3,4 We conrol for heerogeneous effecs of common facors a he counry level by including ime varying counry fixed effecs α j. The residuals ε ij are used o calculae he cross-secional measures. By focusing on shocks raher han he variables as such, we relae uncerainy o he unexplained componens. This is in line wih Jurado, Ludvigson, and Ng (2013) who argue ha any measure of uncerainy should be based on he variaion in he unforecasable componen of he oucome variable. If banks forecas according o equaion (11), he regression residual capures he individual forecas error in each year. In a second sep, we calculae uncerainy in banking as he cross-secional across all bank specific shocks ε ij per counry and year. We compue he cross-secional 3 Resuls remain unaffeced if we esimae equaion (11) separaely for all banks in one counry. 4 Because reurn on asses (RoA) is a flow variable, we esimae his equaion for he levels of RoA.
12 12 as he sandard deviaion (SD). This gives he measure for uncerainy in banking derived from bank-level daa, which we calluncbank for counry j a ime : UncBank = ε ) j SD( ij This can be seen as he empirical counerpar of he ime-varying volailiy σ capuring uncerainy in banking in he heoreical par. I is a condiional measure as we do no use he variables as such bu he esimaed errors of regression (11). The corresponding summary saisics of he measures are provided in Table 2. Noe ha he values canno be easily compared across he differen measures. The reason is ha he summary saisics of he sandard deviaions depend on he definiion and he levels of he underlying variable. For comparabiliy across ime, we describe he paern of he sandardized measures for uncerainy in banking in Secion 4.1 (Figure 1). 3.3 Alernaive uncerainy measures To compare our measures of uncerainy in banking derived from bank-level daa o oher uncerainy measures, we use he following macroeconomic variables: Firs, we calculae bank volailiy based on weekly bank sock price indices aken from Daasream. To capure uncerainy in financial markes as a whole, we consruc a measure for sock marke volailiy using monhly sock price indices from Daasream. In conras o bank-level daa based measures of uncerainy, which are calculaed for cross-secions, hese variables are ime-series measures of uncerainy. Second, uncerainy in he broader economy is covered by he following four measures: Poliical uncerainy in he economy is proxied by he economic policy uncerainy index for he US (Baker, Bloom, and Davis 2013). To conrol for business cycle flucuaions, we include GDP growh which is aken from he IMF World Economic Oulook. In addiion, disagreemens in forecass end o widen during recessions and can serve as a measure for uncerainy (Bloom 2014). Finally, we use he in firm reurns obained from Bloom (2014). The alernaive measures for uncerainy are depiced in Figure 2. Overall, hey show a counercyclical paern. We see increased levels of uncerainy during he recen financial crisis. In a similar vein, GDP growh has dropped sharply during he crisis, and disagreemen among forecasers has increased. Mos of he uncerainy measures have sared o decline again a he end of he sample period. However, his does no hold rue for he economic policy uncerainy index which remains a an elevaed level. j (12)
13 13 4 Uncerainy in banking: descripive saisics 4.1 Has uncerainy in banking changed over ime? In Figure 1, we plo he cross-secional measures over ime. For comparabiliy, we have sandardized hese uncerainy measures. Alhough counries inside and ouside he Euro Area have been affeced differenly by he subprime and he sovereign deb crisis, he ime rends for he Euro Area and he non- Euro Area are similar. Prior o he crisis, he of shocks o oal asses, produciviy, and profiabiliy (RoA) has ended o decline. This rend has been inerruped by an increasing of shocks during he crisis bu coninued aferwards. The levels of uncerainy differ hough beween Euro Area and non-euro Area counries. Euro Area counries end o show a lower for all four uncerainy measures han non-euro Area counries. The paerns of he of shocks o shor-erm funding are quie differen from his general picure: he has increased in he up-run o he crisis, and i has declined subsequenly. Inerpreing a higher sandard deviaion as a higher degree of uncerainy, his indicaes ha uncerainy in banking was ransmied hrough a wider of shocks o he funding side of banks' balance shee. This is consisen wih he inerpreaion of he financial crisis as a crisis of bank funding. In sum, we find ha cross-secional measures based on bank balance shee daa, produciviy, and profiabiliy show differen paerns. Neverheless, he for all bank-level daa based uncerainy measures is higher during he financial crisis. This suggess ha hese measures capure higher uncerainy during crisis imes. 4.2 Do differen measures of uncerainy measure he same? Table 3 provides pairwise correlaions of differen measures of uncerainy. Alhough our four measures of uncerainy in banking are posiively correlaed, absolue values of he correlaions are small. Hence, our measures pick up differen feaures of uncerainy in he banking secor. One excepion is he correlaion among he of shocks o oal asses and profiabiliy (0.51). The alernaive uncerainy measures include, for example, bank volailiy or sock marke volailiy. Correlaions wih he bank-level based measures end o be small while hree of our uncerainy measures correlae significanly wih bank volailiy. The uncerainy measure based on reurn on asses shows a significan correlaion wih mos of he oher proxies for uncerainy. The calculaed from banks'
14 14 produciviy shocks correlaes posiively wih he in forecass and firm reurn (Table 3). Figure 3 compares he developmen of he common measures for uncerainy wih he sandardized measures derived from bank-level daa. Measures based on high frequency daa like bank volailiy flucuae more. In conras, shor-erm flucuaions are smoohed ou in he measures derived from annual bank-level daa. Correlaions or simple ime series plos do no allow accouning for common rends and counry-specific shocks. Table 4 hus gives resuls of univariae panel regressions using he measures as he dependen and alernaive uncerainy measures as he explanaory variables. These regressions include counry- and year-fixed effecs. 5 All variables are posiively and significanly correlaed wih bank volailiy, excep he crosssecional uncerainy measure based on he of shocks o produciviy. Hence, our cross-secional measures for uncerainy in banking behave similarly o commonly used ime series measures for uncerainy in he banking secor. For he remaining alernaive uncerainy measures, he picure is less clear-cu. The index of uncerainy wih regard o economic policy is negaively correlaed wih he in asse shocks (Table 4a). Lower GDP growh or a higher of firm reurns are associaed wih a higher uncerainy in banking measured as he of shocks o profiabiliy as depiced in Table 4d. This is consisen wih he previous resuls. In sum, uncerainy in banking is, o a large exen, unrelaed o alernaive uncerainy measures and in paricular o macroeconomic uncerainy. This is in conras o wha has been found for uncerainy measures based on microeconomic firm-level daa (Bloom and ohers 2012), which are highly correlaed wih economic aciviy. 6 The resul suggess ha our uncerainy measures based on bank-level daa conain addiional informaion on uncerainy in he banking secor. This is confirmed by heir significan correlaion wih he volailiy of sock reurns in he banking secor. 5 In he case of economic policy uncerainy, which does no vary across counries, we conrol only for counry fixed effecs. 6 In conras, a recen paper by De Veirman and Levin (2014) finds only limied evidence ha firm-specific volailiy is couner-cyclical.
15 15 5 How does uncerainy affec bank lending? 5.1 The empirical model To analyze he effec of uncerainy in banking on bank lending, we sar wih he following benchmark model: Loans Asses ij ij 1 + α UncBank 5 = v + v + α loggdpdeflaor i j * X ij ε ij j + α loggdp 2 j + α X 3 ij 1 + α UncBank 4 j (13) where Loans ij / Assesij 1 denoes he difference in he loan volume relaive o oal asses in -1 (in %). We conrol for ime invarian bank characerisics and common ime rends by including he fixed effecs v i and v. Uncerainy in banking is described byuncbank j, which is he cross-secional across bank-specific shocks. Changes in he broader economy are capured by he change in he naural logarihm of he GDP deflaor ( GDPDeflao rj ) and real GDP ( GDP j ). X ij 1 are ime-varying bank characerisics capuring liquidiy, capializaion, he share of cusomer deposis in oal asses, size, and commied loan obligaions. Lagging he bank characerisics accouns for simulaneiy, i is no mean o address endogeneiy. When analyzing he impac of uncerainy on bank lending, we are facing wo idenificaion issues. The firs idenificaion issue relaes o he endogeneiy of uncerainy. Uncerainy migh drive bank lending bu he dynamics of lending migh also affec uncerainy. This endogeneiy concern is parly remedied because lending and uncerainy are measured a differen levels: our dependen variable is bank-level lending while uncerainy is measured a he counry level. Hence, if we assume ha individual banks do no drive aggregae uncerainy, his should be a minor concern. The second idenificaion issue relaes o demand and supply effecs. Uncerainy affecs banks he suppliers of credi as well as he firms who demand credi from banks. We disenangle demand and supply effecs by making use of bank-level heerogeneiy. The measure for uncerainy in banking is ineraced wih he bank-level explanaory variables UncBank j * X ij 1. This allows for differen responses of banks o uncerainy depending on heir balance shee srengh and liquidiy managemen. Assuming a differenial response condiional on hese bank-level variables allows idenifying he supply side effec. A similar idenificaion sraegy has been applied by Corne and ohers (2011) for he case of funding shocks or Valencia (2013) for aggregae uncerainy. This idenificaion scheme is valid as long as borrowers are no
16 16 sysemaically similar in he respecive balance shee characerisic o he banks hey borrow from. We sar from a baseline regression, including macroeconomic conrol variables (Table 5). To see wheher he resuls differ when our measures for uncerainy or oher proxies are used, we exchange hem by e.g. sock marke volailiy or firm reurn (Table 6). We hen replace he macroeconomic conrols by counry-year fixed effecs (Table 7). These models have he advanage ha all unobservable macroeconomic facors are absorbed. Ye, he effec of uncerainy in banking which varies across counries and years only canno be idenified. The focus is hen on he ineracion erm of he bank-level daa based uncerainy measure wih bank characerisics. Based on his model, we perform robusness ess for differen counry samples and ime periods (Tables 8-11). Finally, we conrol for he ownership saus of banks (Tables 12-13). 5.2 Baseline regressions including macroeconomic conrols Table 5 shows he resuls for he baseline regressions including macroeconomic conrols. Banks reduce loan supply as response o higher uncerainy in banking (UncBank ). This holds for all cross-secional uncerainy measures derived from bank-level daa excep he of shocks o shor-erm funding. A one uni increase in uncerainy in banking reduces bank lending on average from 1.3 percen in he case of he of shocks o profiabiliy o 4.3 percen in he case of he of shocks o oal asses. 7 The adverse effec of uncerainy on loan supply is also significan when no ineracion erms are included. 8 Based on he esimaed coefficiens, we can assess he quaniaive scale on which uncerainy in banking impacs banks' loan supply. Across all bank-year observaions included in he esimaions, he change in loans relaive o oal asses of he previous period (our dependen variable) amouns o 3.9 percen on average. We do an in-sample predicion which ses uncerainy in banking firs o is minimum value as observed in he sample and hen o is maximum value. For he sake of breviy, only he resuls for he of shocks o profiabiliy are considered. I urns ou ha he change in loans would have been equal o 5.7, i.e. 1.8 percenage poins higher, if uncerainy in banking 7 Given ha we have sandardized our uncerainy measures, a one uni increase in UncBank corresponds o one sandard deviaion. The bank-level variables are evaluaed a heir means. 8 We presen esimaion resuls only for specificaions including ineracion effecs. Resuls wihou ineracion effecs are available upon reques.
17 17 had been a is minimum. Equivalenly, he change in loans would have been equal o percen on average, i.e. 5.4 percenage poins lower, if uncerainy in banking had been a is maximum. 9 Which banks are mos affeced by uncerainy in banking? To accoun for he effec of (pre-deermined) characerisics of banks and following our idenificaion sraegy, we inerac UncBank wih he bank-level variables. These ineracion erms show ha he effec of uncerainy on bank lending is heerogeneous. More liquid banks reduce lending by less given a rise in uncerainy in banking (Table 5, Columns 1 o 3). This suggess ha in uncerain imes hese banks can draw on heir liquidiy buffers o sabilize lending. Figure 4 shows average marginal effecs of uncerainy in banking on loan supply, condiional on he liquidiy raio of banks. I shows no only he poin esimaes presened in Table 5, bu i also varies he liquid asse share from zero o 80 percen. For all measures, he conracion in lending following an increase in uncerainy is smaller he more liquid asses a bank holds. More liquid banks can hus shield heir supply of loans agains an increase in uncerainy. For banks wih sufficienly high liquidiy, he marginal effecs even urn insignifican. In his case, bank lending is no affeced by uncerainy in a significan way. Turning o he effecs of he level of capializaion, he resuls in Table 5 show ha higher levels of capial migh isolae bank lending agains higher uncerainy (Columns 1 and 4). Beer capialized banks reduce lending by less relaive o heir peers if he of shocks o oal asses or profiabiliy increases. This would be in line wih he observaion ha regulaory capial requiremens become increasingly binding in uncerain imes as illusraed in he heoreical model (Secion 2). Banks wih low capial buffers have o adjus by shifing heir porfolio from risky invesmens such as loans o less risky ones. In conras, beer capialized banks decrease lending relaive o he average bank if he of shocks o shor-erm funding increases (Column 2). Figure 5 confirms hese resuls. The negaive average marginal effec of uncerainy on loan supply declines wih a higher capial raio. This holds if uncerainy is measured as 9 For he oher hree measures for uncerainy in banking, he numbers are as follows. Dispersion of asse shocks: 11.2 (UncBank a minimum) and (UncBank a maximum); Shor-erm funding shocks: 7.4 and 0.5; Produciviy shocks: 6.5 and -6.9.
18 18 he of shocks o oal asses or profiabiliy. In he laer case, he marginal effec even urns posiive for highly capialized banks. Banks wih a higher share of commied credi lines reduce lending by more if hey face an increase in uncerainy measured as he of shocks o shor-erm funding (Column 2). This is plausible as firms end o draw on heir credi lines in uncerain imes. 10 Banks compensae for he increase in loan demand by reducing heir supply of non-commied (new) loans accordingly. Corne and ohers (2011) documen a similar effec in response o (firs-order) liquidiy shocks for US banks during he crisis. As regards oher deerminans of bank lending, Table 5 confirms prior research. Banks exend loan supply relaive o heir overall balance shee if hey are beer capialized, have a higher deposis-o-asses raio, and if hey hold more liquid asses. Including he GDP deflaor of he respecive counries allows inerpreing he changes in bank lending in real and no only in nominal erms. The posiive poin esimae indicaes ha a one percenage poin increase in he inflaion rae (measured as GDP deflaor) increases nominal loan supply by 0.34 percen. The growh rae of real GDP is posiively relaed o bank lending. A one percenage poin increase in real growh leads o an increase in bank lending by 0.62 percen, which can be driven by boh demand and supply side effecs. Do effecs differ for alernaive measures of uncerainy? For comparison, we consider he effec of alernaive uncerainy measures which are relaed o he financial and he real secor: bank volailiy, sock marke volailiy, and firm reurn (Table 6, Columns 1 o 3). In line wih our descripive saisics, bank sock reurn volailiy yields resuls similar o hose for he cross-secional measures. Lending declines as uncerainy increases. Hence, cross-secional and ime-series volailiy measures relaed o he banking secor capure similar feaures of uncerainy. A general measure for sock marke volailiy has no significan effec, in conras. A higher level of firm reurn, which migh capure uncerainy in he real secor and borrower defaul risk, has a negaive effec on bank lending. This is in line wih Valencia (2013) who shows ha banks reduce lending in imes of increased defaul risk 10 Ivashina and Scharfensein (2010) and Campello and ohers (2011) documen ha privae firms drew exensively on commied credi lines during he recen financial crisis.
19 19 of banks bu also of borrowers riggered by higher uncerainy. In conras o he measures derived direcly from bank-level daa, effecs do less depend on banks' balance shee characerisics. Liquidiy srains in inerbank markes increase he uncerainy for banks abou coss and access o inerbank liquidiy. Thus, we include he inerbank ineres rae spread (Table 6, Column 4). Oher han he previous measures, which are based on second momens, he ineres rae spread capures firs momen shocks. We do no find a significan effec on bank lending excep for he ineracion wih he deposi share. This seems reasonable because banks migh be less affeced by increased spreads in he inerbank marke if hey can resor o heir deposis, which is in line wih he findings by Corne e al. (2011) for US banks. In unrepored regressions, we simulaneously included i) one measure and is ineracions wih he bank-level variables and ii) one of he aforemenioned four measures and he ineracions wih he bank-level variables. This helps ensure ha he esimaed effec of our measure on lending is no driven by omied variables. The effec of our cross-secional measures on bank lending remains qualiaively unalered, excep for he case of of shocks o profiabiliy: The coefficien drops insignifican if bank sock reurn volailiy is included simulaneously. 5.3 Baseline regressions including counry-year fixed effecs In order o conrol for macroeconomic condiions affecing all banks in one counry, we include counry-year fixed effecs v j. Now, he counry-level variables including our measure of uncerainy in banking (UncBank) are omied, and we focus on he ineracion effecs. The regression model now looks as follows: Loans Asses ij ij 1 = v i + v j + α X + α UncBank * X + ε 1 ij 1 2 j ij 1 ij (14) This specificaion conrols for a wide range of poenially unobservable facors influencing bank lending. The disadvanage is ha i does no longer allow assessing he direc impac of uncerainy in banking on bank lending. The previous specificaion, however, demonsraed ha he impac of uncerainy in banking works hrough bank characerisics. Table 7 shows he resuls. The ineracion effecs are sill idenified in he regressions, and he resuls for he impac of uncerainy condiional on bank characerisics, i.e. he ineracion effecs, remain robus in mos of he cases: In columns (1) and (3), he ineracion erm of uncerainy in banking wih he liquidiy raio remains significan and
20 20 posiive, in columns (1) and (4) he same holds rue for he ineracion of uncerainy in banking and he capial raio. Again he resuls for uncerainy measured as he of shocks o shor-erm funding deviae from he general picure. 5.4 Robusness ess We conduc various robusness ess. Firs, we resric he analysis o OECD and Euro Area counries. Second, we consider he period before he crisis ( ) and he crisis period ( ). Overall, he previous findings are confirmed. For he OECD sample (Table 8) higher liquidiy or capializaion sill allow banks reducing loans by less. Banks coninue responding o higher expeced loan demand as proxied by he commied loans raio by reducing he share of loans in heir porfolio. Because he OECD sample migh sill comprise heerogeneiy, we conduc he analysis for he Euro Area (Table 9). Implicily, we also conrol for effecs of a join moneary policy. For he Euro Area counries, he effecs of higher liquidiy buffers sabilizing lending are confirmed for all four uncerainy measures. The resuls for he shor-erm funding measure are in line wih he ohers, possibly reflecing he effecs of he common moneary policy. We furher invesigae he role of banks' balance shee srengh for differen ime periods. Table 10 is based on a non-crisis sample ( ), while Table 11 is based on he years Resuls for he pre crisis sample sugges ha bank heerogeneiy does no maer much for he supply of loans in imes of low uncerainy (Table 10). The analysis for he period from 2008 confirms he role of liquidiy in sabilizing loan supply if he of shocks o oal asses or produciviy are considered (Table 11, columns 1 and 3). 5.5 Are domesic and inernaional banks affeced differenly? The analysis so far has shown ha bank lending declines when uncerainy increases. Bu we have no ye accouned for he inernaional dimension of his adjusmen. In recen decades, banking has become more inernaional and shocks migh be ransmied hrough inernaional aciviies of banks (Ceorelli and Goldberg 2011, De Haas and van Horen 2012). Banks migh hus adjus heir lending decisions in response o uncerainy in he home counry or in response o uncerainy in foreign counries. Are foreign-owned 11 We omi he year 2007 because i is no clear wheher i should be defined as a crisis or a non-crisis year.
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Defaul Risk in Equiy Reurns MRI VSSLOU and YUHNG XING * BSTRCT This is he firs sudy ha uses Meron s (1974) opion pricing model o compue defaul measures for individual firms and assess he effec of defaul
Finance and Economics Discussion Series Divisions of Research & Saisics and Moneary Affairs Federal Reserve Board, Washingon, D.C. The Effecs of Unemploymen Benefis on Unemploymen and Labor Force Paricipaion:
The Ineres Rae Risk of Morgage Loan Porfolio of Banks A Case Sudy of he Hong Kong Marke Jim Wong Hong Kong Moneary Auhoriy Paper presened a he Exper Forum on Advanced Techniques on Sress Tesing: Applicaions
An Empirical Comparison of Asse Pricing Models for he Tokyo Sock Exchange Absrac In his sudy we compare he performance of he hree kinds of asse pricing models proposed by Fama and French (1993), Carhar
Does Sock Price Synchroniciy Represen Firm-Specific Informaion? The Inernaional Evidence Hollis Ashbaugh-Skaife Universiy of Wisconsin Madison 975 Universiy Avenue Madison, WI 53706 608-63-7979 firstname.lastname@example.org
Inroducion Chaper 14: Dynamic D-S dynamic model of aggregae and aggregae supply gives us more insigh ino how he economy works in he shor run. I is a simplified version of a DSGE model, used in cuing-edge
Business Condiions & Forecasing Exponenial Smoohing LECTURE 2 MOVING AVERAGES AND EXPONENTIAL SMOOTHING OVERVIEW This lecure inroduces ime-series smoohing forecasing mehods. Various models are discussed,
LEASNG VERSUSBUYNG Conribued by James D. Blum and LeRoy D. Brooks Assisan Professors of Business Adminisraion Deparmen of Business Adminisraion Universiy of Delaware Newark, Delaware The auhors discuss
A Noe on Using he Svensson procedure o esimae he risk free rae in corporae valuaion By Sven Arnold, Alexander Lahmann and Bernhard Schwezler Ocober 2011 1. The risk free ineres rae in corporae valuaion
Proceedings of he Second Asia-Pacific Conference on Global Business, Economics, Finance and Social Sciences (AP15Vienam Conference) ISBN: 978-1-63415-833-6 Danang, Vienam, 10-12 July 2015 Paper ID: V536
DEMAND FORECASTING MODELS Conens E-2. ELECTRIC BILLED SALES AND CUSTOMER COUNTS Sysem-level Model Couny-level Model Easside King Couny-level Model E-6. ELECTRIC PEAK HOUR LOAD FORECASTING Sysem-level Forecas
Moneary Policy & Real Esae Invesmen Truss * Don Bredin, Universiy College Dublin, Gerard O Reilly, Cenral Bank and Financial Services Auhoriy of Ireland & Simon Sevenson, Cass Business School, Ciy Universiy
Real long-erm ineres raes and moneary policy: a cross-counry perspecive Chrisian Upper and Andreas Worms, 1 Deusche Bundesbank 1. Inroducion The real rae of ineres is a cenral concep in economics. I represens
Currency swaps Wha is a swap? A swap is a conrac beween wo couner-paries who agree o exchange a sream of paymens over an agreed period of several years. Types of swap equiy swaps (or equiy-index-linked
WP/05/6 Sock Marke Liquidiy and he Macroeconomy: Evidence from Japan Woon Gyu Choi and David Cook 2005 Inernaional Moneary Fund WP/05/6 IMF Working Paper IMF Insiue Sock Marke Liquidiy and he Macroeconomy:
Migraion, Spillovers, and rade Diversion: he mpac of nernaionalizaion on Domesic Sock Marke Aciviy Ross Levine and Sergio L. Schmukler January 6, 006 Absrac his paper sudies he relaion beween inernaionalizaion
Usefulness of he Forward Curve in Forecasing Oil Prices Akira Yanagisawa Leader Energy Demand, Supply and Forecas Analysis Group The Energy Daa and Modelling Cener Summary When people analyse oil prices,
Inernaional Journal of Economics and Financial Issues Vol. 4, No. 3, 04, pp.65-656 ISSN: 46-438 www.econjournals.com How Useful are he Various Volailiy Esimaors for Improving GARCH-based Volailiy Forecass?
I. Inroducion Do Fuures and Opions rading increase sock marke volailiy Dr. Premalaa Shenbagaraman * In he las decade, many emerging and ransiion economies have sared inroducing derivaive conracs. As was
This version: March 2014 Price Conrols and Banking in Emissions Trading: An Experimenal Evaluaion John K. Sranlund Deparmen of Resource Economics Universiy of Massachuses-Amhers James J. Murphy Deparmen
ly-traded versus Privaely-Held: Implicaions for Bank Profiabiliy, Growh, Risk, and Accouning Conservaism D. Craig Nichols Assisan Professor of Accouning Johnson Graduae School of Managemen Cornell Universiy
Anicipaing he fuure from he pas: he valuaion implicaion of mergers and acquisiions 1 Ning Zhang Deparmen of Accouning, Fuqua School of Business Duke Universiy June, 2012 Preliminary and commens welcome
4. Inernaional ariy ondiions 4.1 urchasing ower ariy he urchasing ower ariy ( heory is one of he early heories of exchange rae deerminaion. his heory is based on he concep ha he demand for a counry's currency
The Impac of Surplus Disribuion on he Risk Exposure of Wih Profi Life Insurance Policies Including Ineres Rae Guaranees Alexander Kling 1 Insiu für Finanz- und Akuarwissenschafen, Helmholzsraße 22, 89081
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Chaper 1.6 Financial Managemen Par I: Objecive ype quesions and answers 1. Simple pay back period is equal o: a) Raio of Firs cos/ne yearly savings b) Raio of Annual gross cash flow/capial cos n c) = (1
UNDERSTANDING THE DEATH BENEFIT SWITCH OPTION IN UNIVERSAL LIFE POLICIES Nadine Gazer Conac (has changed since iniial submission): Chair for Insurance Managemen Universiy of Erlangen-Nuremberg Lange Gasse
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The Effec of Moneary Policy on Privae Money Marke Raes in Jamaica: An Empirical Microsrucure Sudy Derek Leih Research Services Deparmen Research and Economic Programming Division Bank of Jamaica Absrac
DYNAMIC MODELS FOR VALUATION OF WRONGFUL DEATH PAYMENTS Hong Mao, Shanghai Second Polyechnic Universiy Krzyszof M. Osaszewski, Illinois Sae Universiy Youyu Zhang, Fudan Universiy ABSTRACT Liigaion, exper
Does Opion Trading Have a Pervasive Impac on Underlying Soc Prices? * Neil D. Pearson Universiy of Illinois a Urbana-Champaign Allen M. Poeshman Universiy of Illinois a Urbana-Champaign Joshua Whie Universiy
USE OF EDUCATION TECHNOLOGY IN ENGLISH CLASSES Mehme Nuri GÖMLEKSİZ Absrac Using educaion echnology in classes helps eachers realize a beer and more effecive learning. In his sudy 150 English eachers were
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Small and Large Trades Around Earnings Announcemens: Does Trading Behavior Explain Pos-Earnings-Announcemen Drif? Devin Shanhikumar * Firs Draf: Ocober, 2002 This Version: Augus 19, 2004 Absrac This paper
Predicing Sock Marke Index Trading Using Neural Neworks C. D. Tilakarane, S. A. Morris, M. A. Mammadov, C. P. Hurs Cenre for Informaics and Applied Opimizaion School of Informaion Technology and Mahemaical
2012-2 Swiss Naional Bank Working Papers Liquidiy Effecs of Quaniaive Easing on Long-Term Ineres Raes Signe Krogsrup, Samuel Reynard and Barbara Suer The views expressed in his paper are hose of he auhor(s)
Bid-ask Spread and Order Size in he Foreign Exchange Marke: An Empirical Invesigaion Liang Ding* Deparmen of Economics, Macaleser College, 1600 Grand Avenue, S. Paul, MN55105, U.S.A. Shor Tile: Bid-ask
Loans, Ineres Raes and Guaranees: Is There a Link? 1 G. Calcagnini, F. Farabullini e G. Giombini 1. Inroducion This paper aims a shedding ligh on he influence of guaranees on he loan pricing (banking ineres
Foreign exchange marke inervenion and expecaions: an empirical sudy of he yen/dollar exchange rae by Gabriele Galai a, William Melick b and Marian Micu a a Moneary and Economic Deparmen, Bank for Inernaional
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YEN FUTURES: EXAMINING HEDGING EFFECTIVENESS BIAS AND CROSS-CURRENCY HEDGING RESULTS ROBERT T. DAIGLER FLORIDA INTERNATIONAL UNIVERSITY SUBMITTED FOR THE FIRST ANNUAL PACIFIC-BASIN FINANCE CONFERENCE The
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Foreign Exchange Marke Microsrucure Marin.. Evans 1 Georgeown Universiy and NBER Absrac This paper provides an overview of he recen lieraure on Foreign Exchange Marke Microsrucure. Is aim is no o survey
House Price Index (HPI) The price index of second hand houses in Colombia (HPI), regisers annually and quarerly he evoluion of prices of his ype of dwelling. The calculaion is based on he repeaed sales
Are Employee Sock Opions Liabiliies or Equiy? Mary E. Barh Sanford Universiy email@example.com Leslie D. Hodder Indiana Universiy firstname.lastname@example.org Sephen R. Subben The Universiy of Norh Carolina a Chapel
Dynamic Hybrid Producs in Life Insurance: Assessing he Policyholders Viewpoin Alexander Bohner, Paricia Born, Nadine Gazer Working Paper Deparmen of Insurance Economics and Risk Managemen Friedrich-Alexander-Universiy
Chaper 3: Forecasing From Time Series Models Par 1: Whie Noise and Moving Average Models Saionariy In his chaper, we sudy models for saionary ime series. A ime series is saionary if is underlying saisical