Modelling and Measuring Business Risk and the Resiliency of Retailbanks

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ARTICLE IN PRESSG ModelJFS-308; No. of Pages 10Journal of Financial Stability xxx (2014) xxxxxx

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Journal of Financial Stability

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Modelling and measuring business risk and the rbanks

Mohamea Universit de b Universit de

a r t i c l

Article history:Received 26 JuReceived in reAccepted 18 AAvailable onlin

JEL classicatioG21D24

Keywords:Bank solvencyRetail bankingBusiness riskEfciency analProt

d theks sua newnexpelogy etwe

Using the distance function to compute banks protability, we take the distance to the frontier of bestpractices as a measure of prot inefciency, i.e. of unexpected losses related to underperformance. Inthis approach, shifts in the efciency frontier induced by adverse shocks to banks volumes serve asa measure of business risk. This measure of prot volatility allows a measurement to be made of theimpact of volume changes on banks prots. This method is applied to a database containing half yearlyregulatory accounting reports over the 19932011 period for a sample of quite all French banks running

1. Introdu

Every rpotential lovolume, main customeother changresponds toto adapt itsbusiness risof earnings risks) or othbusiness ris

The views those of their

CorresponFort Noire, 67

E-mail addmichel.dietsch

http://dx.doi.o1572-3089/ e this article in press as: Chaffai, M., Dietsch, M., Modelling and measuring business risk and the resiliency of retail banks. J.Stability (2014), http://dx.doi.org/10.1016/j.jfs.2014.08.004

ysisa retail banking business model. Our results verify a low level of business risk in retail banking, thusconrming the resiliency of the retail banks business model.

2014 Elsevier B.V. All rights reserved.

ction: business risk concept and measurement

m is subject to business risk. Business risk refers tosses due to adverse, unexpected changes in businessrgins and costs. These losses can be the result of changesr preferences, an increase in competitive pressures ores in a banks environment. Thus, business risk also cor-

managerial risks, and it depends on the rms capacity policy to unexpected events and changes. In banking,k is a non-nancial risk that is linked to the uncertaintynot associated with nancial risks (market, credit, ALMer types of non-nancial risk (operational risk). Banksk must not overlap with these other risks, not does it

expressed in this paper are those of the authors and do not representinstitutions.ding author at: LARGE, Universit de Strasbourg, IEP, 47, avenue de la000 Strasbourg, France. Tel.: +33 607148326.resses: [email protected], [email protected],@urs.u-strasbg.fr (M. Dietsch).

incorporate interest rate risk, default risk or credit risk becausethese risks are already taken into account in other forms of risk.

The banking sector devoted little attention to business riskbefore the subprime crisis. As mentioned in a 2007 economic cap-ital survey, management of business risk still lags behind corenancial risks (IFRI/CRO, 2007). The survey demonstrated thatbusiness risk is considered an important risk type over 85% of par-ticipants include it in their economic capital frameworks, and theaverage impact is 10% of the aggregate economic capital require-ment. However, business risk is probably also the risk type thatis being debated most actively at present, with discussions focus-ing on the most appropriate measurement approach. A varietyof approaches are taken to reect business risk, and the level ofsophistication generally appears to be less pronounced than in thecase of core nancial risks. For this key non-nancial risk, a rangeof different capital calculation approaches can be employed thatcould lead to signicantly different results and, as a result, man-agement incentives. Overall, there is no clear convergence in theapproach to measuring business risk.

One reason for this lack of attention to business risk in the bank-ing industry is that in the booming nancial markets of the 1990sand 2000s, business risk hardly seemed to be a signicant risk

rg/10.1016/j.jfs.2014.08.0042014 Elsevier B.V. All rights reserved.d Chaffai a, Michel Dietschb,

Sfax, Tunisia Strasbourg and ACPR - Banque de France, France

e i n f o

ne 2013vised form 18 February 2014ugust 2014e xxx

n:

a b s t r a c t

The recent banking crisis has revealebusiness model. On average, retail banmarket changes. This paper proposes is dened as the risk of adverse and uin the banks activities. This methodospecically, on the duality property b/ locate/jfstabil

esiliency of retail

existence of strong resiliency factors in the retail bankingffered less than other nancial institutions from unexpected

methodology to measure retail banks business risk, whichcted changes in banks prots coming from sudden changesis based on the efciency frontier methodology, and, moreen the directional distance function and the prot function.

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ARTICLE IN PRESSG ModelJFS-308; No. of Pages 102 M. Chaffai, M. Dietsch / Journal of Financial Stability xxx (2014) xxxxxx

for banks. But the recent subprime crisis demonstrated that bankscan suffer from this business risk more than non-nancial rms.Indeed, during the crisis, the extinction of some bank activities canbe considered to be the consequence of business risk. For example,activity in tand IPOs ddue largelyket disruptbanks decliinvestmentness risk ca

By contstronger reEven if retastructure, mbusinesses forming locand work, rture whichof stress. Thters of risk because theeven if credtor couldfunding liquspecicatioand busines

Today, bto businessvision requregulatory fat strengthethe same diaging bankthat this ris

This papsuring busiMore specidirectional increase inconsidered tability. Ubanks locattrating theunfavouraba unique dance sheetbanks midentied acollected onThis samplegroups.

It is orgcurrent methe propose4 presents tdiscusses th

2. Survey o

While cpurely statibased on reapplied to t

2.1. Earnings-at-risk methodologies

The current methods used to model business risk can beclassied in

g-at- rstusinchmausinnksializty asa un

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he markets for syndicated loans, structured productsropped substantially, or even disappeared altogether,

to severe asset depreciations and strong nancial mar-ions. Consequently, the revenues of most investmentned sharply. The relatively exible cost structure of

banks allowed them to adjust costs quickly, but busi-sts doubt on the resiliency of this bank business model.rast, the recent crisis has revealed the existence ofsiliency factors in the retail banking business model.il banking is characterized by a relatively rigid costost deposit-taking banks focused on retail banking

have come through the recent crisis quite well. By trans-al deposits into lending in the areas where people liveetail banks benet from a quite stable nancing struc-

allows them to maintain lending activities in periodey can act as shock absorbers rather than transmit-to the nancial system and the real economy. This isy are exposed to a low level of credit risk on average,it risk concentrations especially in the real estate sec-

be an issue, and also because they can better manageidity risk. Overall, the recent crisis has shown that the

n of business risk sources varies across banks activitiess models.anking supervisors call for more attention to be paid

risk. The Basel Banking Committee on Banking Super-ires it to be taken into account in Pillar II, the internalramework of Basel II. Recent Basel III proposals aimedning the resiliency of the banking sector are heading inrection. Thus, the new regulatory framework is encour-s to look at this risk. Nevertheless, regulators concedek is hard to measure.er proposes a new approach for modelling and mea-ness risk based on the efciency frontier methodology.cally, it exploits the duality property between thedistance function and the prot function. Thus, any

one banks distance to its efciency frontier may beto be the consequence of a decline in that banks pro-sing this approach, we take the performance of theed in the last percentiles of inefciency scores as illus-

worst situation a bank will potentially encounter ifle business risk factors materialize. The paper usesatabase containing regulatory information about bal-s and income statements for more than 90 Frenchainly regional and cooperative banks that can bes running a retail banking business model. Data are

a half-yearly frequency over the 19932011 period. contains all banks belonging to major French banking

anized as follows. Section 2 presents a survey of thethods used to measure business risk. Section 3 outlinesd directional distance function methodology. Sectionhe data and the specication of the frontier. Section 5e results and Section 6 concludes.

f current methods used to measure business risk

urrent estimation methodologies of business risk usestical models, we propose a structural model which iscent developments in the production and cost theoryhe banking sector.

earninThe

each bas benbank bized baof specvolatililine in

Theings vobusinesists indata (loand in (EaR). is to asthen cof wothese arevenuare buoperat(with aexpectextensvariabl

UsinstochaHe sugcash omodelof the compoEaR meneeded

Onein termit requIndeedof risks

2.2. A

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WhcommoStability (2014), http://dx.doi.org/10.1016/j.jfs.2014.08.004

ring business risk and the resiliency of retail banks. J.

two categories: the benchmark approach and therisk (EaR) approach.

one proposes to compute specic earnings risk foress unit of a given bank by taking specialized banksrks. In other words, the earnings volatility of differentess models is derived from the assessment of special-

earnings volatility. Thus, it consists in nding a paneled banks and taking information about their earnings

a proxy for the volatility of the corresponding businessiversal bank.nd method, the EaR method, compares a banks earn-ity with the rigidity of operating costs, and measuresk in terms of the volatility of bank net income. It con-puting historical earnings volatility with banks internalerm time series on volumes, margins, revenues or costs)forming this volatility into a measure of earning-at-riskimplest way to obtain such a measure of business riske a specic distribution for the prot components, tote the earnings at a given level of condence, a sort

ase earnings, and nally to determine the loss underptions. As a rst step, the probability distribution of

om fees and commissions and revenues from interestd a given quantile is chosen. Then, as a second step,

osts are assumed to be totally constant in the short run-year horizon, in fact), and they are subtracted fromvenues to determine expected earnings. However, anf the approach could decompose costs into xed andts.statistical approach, Klaus Bcker (2008) proposed aodel to determine the EaR and quantify business risk.d a multivariate continuous-time model for the futuref the different earnings components chosen. Under thisication, he computed the value of the EaR on the basisibution property of the chosen equation of earningss. Then, he determined a dynamic relation between thee and the capital-at-risk measure (the economic capitalbusiness risk).kness in this EaR approach is that it is quite demandingthe length of the time period. Another weakness is thatbusiness risk to be isolated from other forms of risk.enue volatility could be strongly driven by other types

it is not so easy to isolate different risk sources.

tural approach to business risk

propose an alternative measure of business risk basedral approach to modelling bank technology and mea-

performance. As noted by Hughes and Mester (2010)ch is choice-theoretic, and it relies on a theoreticale banking rm and the concepts of cost minimizationaximization. In this approach, the bank is viewed as a

main objectives are to solve information problems inrowers relationships, to manage risks and to providervices to the economy. As demonstrated in the bank-re, commercial banks uniqueness or superiority overial rms is largely derived from its high leveraged cap-e, e.g. the funding of informationally opaque borrowerserm deposits. Such foundations help to understand thedel of retail banks and to choose accordingly the inputs

s in the bank production.scussing the economic performance of a producer, it is

describe it as being more or less efcient. The rms


ARTICLE IN PRESSG ModelJFS-308; No. of Pages 10M. Chaffai, M. Dietsch / Journal of Financial Stability xxx (2014) xxxxxx 3

efciency refers to the differences between observed and optimalvalues of its inputs and outputs. In practice, efciency is mea-sured by comparing observed and optimal cost, revenue or prot.How one mdepends oninputs or meconomics oa prot funcbusiness risefcient, thtion so thatcorrespondrectly choosso as to respproducts ansponds to edecisions afof measurinspecic and

Businesstaking bankare linked tthe provisideposits in ingly exposare also conRetail bankincome, thaowing to chproducts anperformancsources of function. Cliquidity prprocess wh

More prdecreases intainty overuncertaintyputs producis dened ato the outpthe gap betshocked frociency, whprots andcorrespondtier. In otheshock, the its owing tinefciencyinefciencythat, we trapost-shock of managem

Thereformethodologparametric ciency meaof their abduality betwfunction, as(2004). Thetance volatvolumes, m

margins or costs affects the distance and thus the inefciency mea-sure. The variability of the distance corresponds to the EaR, which isa measure of the variability of prot efciency. Moreover, it is possi-

hoic direcombion focksk fuen coage octionfci

fromre re

outpks, tnts

n thisextreuencn-par of slly, ocy. Sbankusibgral e also gauommact o

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entn ththe dproy precom

y anency

and envdy, e funty odeed

of v direirst,ion is ban annt i

fron. Thnctig grore tion n. Ine this article in press as: Chaffai, M., Dietsch, M., Modelling and m

easures bank performance in the structural approach whether one views the bank as an entity that minimizesaximizes prot. The structural approach relies on thef cost minimization or prot maximization. Estimatingtion seems more relevant when the issue is to computek because it might tell us if the bank is economicallyat is not only whether managers organize the produc-

the amount of output produced is maximized, whichs to technical efciency, but also whether they cor-e the level of inputs and outputs and their combinationond to changes in relative prices, changes in demand ford other changes in a banks environment, which corre-conomic efciency. Moreover, to the extent that bankfect bank risk, this approach ts well with the objectiveg the overall risk of banking production in the face of

systematic economic shocks. risk affects the bank performance. In retail deposit-s, business risk comes primarily from sources whicho the two main functions of these banks: lending, andon of liquidity. Indeed, due to the regular decline ofthe portfolios of private agents, retail banks are increas-ed to the risk of a shortage of short-term funding. Theyfronted with growing competition in lending markets.s also suffer from potential volatility in non-interestt is, from changes in commissions and other fee incomeanges in the volumes of various nancial or insuranced payment services. The structural approach of banke measurement allows considering all these potentialbusiness risk into the specication of the productiononsequently, in this paper, we consider lending andovision as the main outputs of the banking productionich can be impacted by shocks.ecisely, business risk refers to situations in which

prots result from adverse changes owing to uncer- output volumes, margins and costs. Because output

creates volatility in banks prots, shocks to the out-e unexpected changes to output volumes. Business risks the gap between optimal prot before adverse shocksuts and observed prot after shocks. It is measured byween the initial benchmark efciency frontier and thentier. This gap corresponds to an increase in prot inef-ich is actually a decline in prots. Adverse shocks affect

move banks away from their efciency frontier and to an increase of the distance to the pre-shocks fron-r words, for a given bank, and a given outputs volumencrease in this distance represents the decrease in pro-o the decline in the outputs volume. But to compute

coming from adverse shocks, we have to neutralize coming from differences in management ability. To donslate all individual banks towards the pre-shock andfrontier, as if all banks were equally efcient in termsent capacity.e, our measurement proposition consists in using they of the efciency frontiers and more precisely thedirectional distance function to build a prot inef-sure that allows similar banks to be ranked in termsility to maximize prot. This choice is based on the

een the directional distance function and the prot demonstrated by Chung et al. (1997) and Fre et al.

volatility of earnings can be computed by the dis-ility, which varies depending on the uncertainty ofargins or costs of the bank. A change in volumes,

ble to clinkingin the mediatwell shthe ban

Whadvantcontraprot ederivedthe motion orof shocrepresevalue iing an consequse nonumbe

Finaresilienvidual but plaan intethey arvisors tmost cthe impa simu

3. Met

As mbetweeUsing sured recentlpaper dciencinefciChaffaience ofthis studistancvolatilirisk. Inimpact

Thetages. Fexpanswhich functiosuremederivedfunctioenue fubankinbank (FestimafunctioStability (2014), http://dx.doi.org/10.1016/j.jfs.2014.08.004


e a functional form of the distance function that allowsctly the distance that is the prot to any changeination of inputs and outputs which depicts the inter-unction of the bank. Consequently, we can consider as

on outputs such lending supply as well as shocks onnding.nsidering the business risk, this methodology offers thef taking into account the possibility of a simultaneous

of inputs and expansion of outputs in constructing theency frontier. Thus, the technical inefciency measure

a directional distance function is more complete thanstricted measures derived from an input distance func-ut distance function. Then, if we simulate a successionhe set of consecutive distance increases in the samplethe distribution of individual prot losses. An extreme

distribution serves as a measure of the EaR. Indeed, tak-me value means that we retain one of the most severees of the shock in terms of a decrease in prots. Werametric Monte-Carlo simulations to compute a largehocks and consecutive business risk values.ur approach provides a kind of stress-test of the banktress testing describes various techniques used by indi-s to assess their potential vulnerability to exceptionalle adverse events or shocks. Stress tests have becometool of banks risk management (Drehmann, 2008), ando techniques that are frequently used by banking super-ge the resiliency of the nancial system as a whole. Theon of these techniques involves the determination ofn the portfolio of a bank of a given scenario that createsous change in a combination of specic risk factors.

ology: the directional distance function

ioned above, Chung et al. (1997) emphasized the dualitye prot function and the directional distance function.irectional distance function, Fre et al. (2004) mea-

t efciency in US banks, and Park and Weber (2006)esented similar measures for Korean banks. The rst

poses prot technical inefciency into allocative inef-d technical inefciency, while the second focuses on

changes and productivity changes. More recently still, Dietsch (2009) used this approach to measure the inu-ironmental characteristics on branches protability. Inwe use the methodology of the parametric directionalction to build an inefciency measure and to take the

f this inefciency indicator as a measure of business, prot volatility allows us to give consideration to theolume changes on banks prots.ctional distance function methodology has three advan-

it allows a simultaneous contraction of the inputs andof the outputs in constructing the efciency frontier,sed on the duality between the directional distanced the prot function. Thus, technical inefciency mea-s more comprehensive than the restricted measuresm an input distance function or the output distanceese two functions are dual to the cost function and rev-on respectively. Second, the aggregate inefciency of aup is the sum of the individual inefciencies of each

et al., 2005). Third, less information is required for theof the directional distance function than for the prot

fact, only information on output and input amounts is

Please citFinancial


needed, thusuring pricewhich are paccounting

3.1. The par

We conX = (x1, x2,(y1, y2, . . .,of all the ctor Y. We alconditions.2

data in the

D(X, Y; gx

The direexpanding g, which nesure the matechnically prot is at correspondby improvin

It is impderived fromthe data ardirection veonly allowsmodel referrevenue funcontractionthe input dgy) = (1, 1that the baits costs in

To illustprojected inAA* for banboth techni

1 That couldhard to measu

2 The set T iable.

To estimate the frontier, two methods could be used: thenon-parametric method, which uses the linear programmingmethodology, and the parametriceconometric method, which isknown as the stochastic frontier approach. In this paper, we choosethe stochastic frontier to estimate the directional distance function.The main rrandom noi

The direty3 (s thety. Cnal fd it

beenPark ly ononal ear rnslatur sngesn is:

gx

e fol

pgy

jgy

jigy

jjgyFig. 1. Illustration of the direction distance function.

s avoiding problems arising from the difculty of mea-s (which are needed to estimate the prot function),articularly severe in banking applications when usingdata.1

ametric directional distance function

sider that each bank uses a vector of inputs . . ., xk) +k to produce a vector of outputs Y =

yp) +p . Let T denote the production possibilities setombinations of inputs X which can produce the vec-so assume that this set satises the familiar regularityThe directional distance function encompasses the

direction vector g = ( gx, gy) and is dened by:, gy) = Max

{(X gx, Y + gy) T} (1)

ctional distance function is dened by simultaneouslythe outputs and contracting the inputs in the directioneds to be specied. The scalar solution of (1) will mea-ximum expansion of outputs and contraction of inputspossible. For any combination of inputs and outputs,its maximum when the bank is on the frontier, whichs to = 0. If not, > 0 and the bank could boost protsg its technical efciency in the g-directional vector.ortant to mention that the measure of inefciency

this model depends on the direction vector g in whiche projected on the frontier. Two important particularctors should be mentioned. The rst direction g = (0, gy)

for an expansion of outputs given a level of inputs. Thiss to the output distance function which is dual to the

propersuch aproperfunctioters, anIt has 2005; the ondirectiadd linthe tra

In oral chafunctio

D(X, Y;

with therty:

pj=1pj=1pj=1pj=1e this article in press as: Chaffai, M., Dietsch, M., Modelling and measuStability (2014), http://dx.doi.org/10.1016/j.jfs.2014.08.004

ction. The second direction g = ( gx, 0) allows for input given the level of produced outputs, which refers toistance function. Here, we retain the direction g = ( gx,) which allows us to measure the technical efciencynk can achieve if it increases its revenues and reducesthe same proportion.rate this distance function, we consider Fig. 1. Bank A is

A* by estimating the frontier for all banks. The distancek A measures gross technical inefciency incorporatingcal inefciency and allocative inefciency.

particularly be the case for banks cost of real physical which is veryre using accounting data.s non-empty and convex. Both outputs and inputs are freely dispos-

In additand ii =

To estimwe used thetion of the dand takes th

0 = D(X, Y;where = v

Then, wtance funct

3 The directgx, Y + gy; ring business risk and the resiliency of retail banks. J.

eason for this choice is that this method distinguishesse and technical inefciency under a banks control.ctional distance function should verify the translationChambers et al., 1998). Not all exible functional forms

Translog function or the Fourier function verify thishambers et al. (1998) proposed the exible quadraticorm. This form is linear with respect to the parame-provides a good representation of banking production.

used in several studies devoted to banks (Fre et al.,and Weber, 2006), among others, and it appears to bee used in the actual empirical studies dealing with thedistance function. The estimation of the model needs toestrictions to the model parameters in order to verifyion property.pecication, we add time parameters to take tempo-

into account. Thus, the quadratic directional distance

, gy) = 0 + 0t +121t +

pj=1jYjt +

ki=1 iXit

+ 12

pj=1

pj=1jjYjYj +

pj=1jYj +

ki=1iXi

+ 12

ki=1

ki=1iiXiXi +

pj=1

ki=1jiYjXi (2)

lowing linear restrictions to verify the translation prop-

ki=1igx = 1

ki=1 igx = 0

ki=1iigx = 0 i = 1, . . ., k

ki=1jigx = 0 j = 1, . . ., p

ion, the symmetry restrictions are imposed: jj = jjii.ate the parameters of the directional distance function,

stochastic frontier approach. The stochastic specica-irectional function was proposed by Fre et al. (2005)e form:

gx, gy) + (3) u, vN(0, 2v ) and uN+(0, 2u ).e apply the translation property of the directional dis-ion to the previous equation with respect to one of the

ional distance function satises the translation property if D(X gx, gy) = D(X, Y; gx, gy) .

Please cit easuring business risk and the resiliency of retail banks. J.Financial


outputs of the model, Yp for example. Using the directional vectorg = ( gx, gy) = (1, 1), we obtain:

Yp = D(X gxYp, Y + gyYp; gx, gy) + (4)

and the stochastic frontier becomes:

Yp = 0 +1

p1 k

+pj=

+ 12

+ 12

+pj=

In Eq. (5technical inturbance. Tlikelihood mLovell, 2000

In the prdistributiontwo error c{0; 1; 0; 11, . . ., phood methestimated v = v u =

Here, wenents are ob(v u), as is

The maithe model that it is poparametersinequality rfunction.4

3.2. Using tmeasure ret

The distprot-inefhas a score practices),increase pro

In this ssure busineresult from

4 The other tion could be likelihood meremains.

es, mcreat

to ths to se inn oththe ito thssionple

reme serveanck in-Carlutive

simu

rst sg intsouicesenchsecoing pe is modied (in fact, reduced) by a certain percentage at

drawing ( in Fig. 2). Then, we re-estimated the distanceion, taking into account new shocked values of bank outputsew values of inputsoutputs combinations ( in Fig. 2) soobtain a new shocked frontier. We note that if shocks reducees, prot-inefciency increases and the new shocked fron-

is below the initial one; it means that the distance increases,

hird step, we computed the difference between the distance by the shocked frontier when we take initial observed out-and inputs, and the distance to the initial benchmark frontier

Fig. 2). The difference measures the business risk, which islly the reduction in prots measured by an increase of protciency generated by the shocks to the outputs volumes:

D(Xit, Yit; ) D(Xit, Yit; s)D(Xit, Yit; )

(6)e this article in press as: Chaffai, M., Dietsch, M., Modelling and m

0t + 21t +j=1j(Yj + Yp)t +

i=1 i(Xi Yp)t

1

1

j(Yj + Yp) +ki=1i(Xi Yp)

p1

j=1

p1j=1jj (Yj + Yp)(Yj + Yp)

k

i=1

ki=1ii (Xi Yp)(Xi Yp)

1

1

ki=1ji(Yj + Yp)(Xi Yp) + v u (5)

), u 0 is a one-sided disturbance term which capturesefciency, and v is a usual normal two-sided noise dis-his frontier could be estimated using the maximumethod or the method of moments (Kumbhakar and

).evious equation, u is assumed to follow a half-normal

and v a normal distribution. We assume, too, thatomponents are independent. The set of parameters1, . . ., p; 1, . . ., k1; 11, . . ., p1p1; 11, . . ., kk;1k; u; v} is estimated using the maximum likeli-od. This information can be used to compute thealue of Xk and then the value of the global residualYp (Yp).

assume that u is half-normal. The inefciency compo-tained by taking the expected value of u conditional on

suggested by Jondrow et al. (1982).n advantages of the stochastic approach are (i) thattakes into account possible noise in the data and (ii)ssible to conduct inference tests of the value of model. But, it presents the disadvantage of not imposing theestrictions on the derivatives of the directional distance

he parametric directional distance function toail banks business risk

ance, as dened in the previous section, provides aciency score at the bank level. A prot-efcient bankequal to 0 (this bank is located on the frontier of best

whereas a bank with a score equal to % ( > 0) couldt by %.

tudy, we use the directional distance function to mea-ss risk, meaning situations in which decreases in prots

adverse changes owing to uncertainty over output

disadvantage is the endogeneity problems with the inputs. The solu-to use instrumental variable estimators instead of the maximumthod. But the decomposition of the u terms from the residuals still

volumtainty shockschangedecreavalue. Ishock, owing a succethe samAn extbutionvalue mthe shoMonteconsec

The

- In a takininputpractas a b

- In a drawvolumeachfunctand nas to volumtier too.

- In a tgivenputs ( inactuainef

Risk =Stability (2014), http://dx.doi.org/10.1016/j.jfs.2014.08.004Fig. 2. Illustration of the simulation procedure.

argins and costs. Because we assume that output uncer-es volatility in banks prots, we have to implemente outputs in this framework, representing unexpectedoutput volumes. The idea is that these shocks imply a

prots which corresponds to an increase of the distanceer words, for a given bank, and a given outputs volumencrease in distance represents the decrease in protse decline in the outputs volume. Thus, if we consider

of shocks, the set of consecutive distance increases in represents the distribution of individual prot losses.

value (high 90% and 95% percentiles) in this distri-es as a measure of the EaR. Indeed, taking an extremes that we retain one of the most severe consequences of

terms of a decrease in prots. We use non-parametrico simulations to compute a large number of shocks and

business risk values.lation procedure to compute business risk is as follows:

tep, we estimated the parametric directional distance,o account the observed technology and the observedtputs combinations. The distance to the frontier of best

provides a measure of prot inefciency that will servemark for the next-steps comparisons ( in Fig. 2).

nd step, we generated shocks to outputs by using arocedure we will present below. Thus, each outputs



where the initial frontier is D(Xit, Yit; ) and the shocked frontier isD(Xit, Yit; s).

This procedure, which consists in computing the differencebetween the frontiers, also offers the advantage of neutralizingchanges in inefciency owing to managerial inefciency. Fig. 2illustrates the simulation procedure, which is done separately foreach output.

The diffefor each shof the samcomposed yearly freqbetween frof the distues for eachtimes, so thto N distincrisk) resultivalue of theiteration.

The valuof the studylarge percebut not tooretained thcompromisin this papeallows takidetermine tput associawhich are c

To implsimple proscores distobserved sasequence oshocks havestudy. For ingiven bank conditions adrawing, w5% inefcieput is reducimplies thaproportion tability. Thnew valuesindividuallyence betwefrontier as difference rof managerence gives consequenc

The scensame 10% pever the ye

5 We also im10% percentileinto account, wscenario are vrst one.

4. Data and specication of the prot function

The sample is composed of quite all French banks running aretail banking business model. It includes 91 French banks we haveidentied as running a retail banking business model. Retail bank-ing is identied by considering the share of loans to the householdsand small businesses clients in the asset side of the balance sheet,and the share of deposits of the same clients in the liability side. This

is mainly composed of regional cooperative banks formingr main networks of French mutual banks Banques Pop-, Caisses dEpargne, Caisses de Crdit Agricole and Caissesdit Mutuel but the sample also contains corporate banks,s CIC banks or Crdit du Nord.6 We excluded large Frenchsuch as BNPP or Socit Gnrale because the accountingation we use does not allow to be truly isolate the perfor-

in rearly o 201arly

coveperaankiur nd inhis sary tcicencys vece, wen apuentres och, wodeks: ve aty toe: (i)ringonal

actiprodage t

whic) trative e sheues,

sertors s.8 B

nstitu bankslable a

data cnous droviden proe divbalanckets ees.e this article in press as: Chaffai, M., Dietsch, M., Modelling and m

rence in distances provides a measure of business riskock to a given output. Each shock affects all banksple. Thus, for each simulation, because our sample isof 91 banks over an 19-year time period and a half-uency (see below), we get 3276 values for the gapontiers. Then, we retain the 90% and 95% percentilesribution of these differences as earnings-at-risk val-

simulation. The simulation procedure is replicated Nat we obtain N values for business risk correspondingt shocks. The nal value for business risk (earnings-at-ng from shocks to this output is given by the average

N high percentiles values we have obtained for each

e of the chosen percentile depends upon the objective and the size of the sample. We choose two sufcientlyntiles to cover potential losses due to severe shocks,

high to avoid extreme values. It is the reason why wee 90% and 95% percentiles, which appears to be a goode. By implementing such quite large shocks, the idear is to provide a kind of stress-test. Our methodology

ng account for any realistic change of the outputs. Tohe proportion of the change in the volume of each out-ted to specic scenarios calls for other methodologiesomplementary to ours.ement output shocks, we have chosen a realistic andcedure, which consists in drawing in the inefciencyribution itself. Indeed, this distribution exhibits themples prot volatility, which is assumed to be the con-f real shocks determined by the business cycle. Such

affected the banks over the 19-year time period understance, an inefciency score of 10% in a given year for ais assumed to be the consequence of adverse economicffecting that bank in that year. More precisely, for eache get a value of the banks inefciency (for instance, ancy value), and we assume that the volume of each out-ed by the same proportion. Such a drawing proceduret the shocked output decreases at each drawing. Therepresents the output change that impacts on bank pro-en, we re-estimated the distance frontier using these

for each output. We used the same logic for each output, and for the three outputs, and we computed the differ-en the initial estimated frontier and the new estimateda measure of each banks business risk. Note that thiseects only the impact of shocks, and not the impactial or operational inefciency. The value of this differ-a measure of each banks prot reduction which is thee of each shock.ario we have implemented consists in drawing in theercentile of the distribution of inefciency scores, what-ar.5

plemented a second scenario which consists in drawing in the last of the distribution of inefciency scores, taking only the worst yearshich corresponds in fact to strong shocks. The results of this second

ery close. Consequently, we will concentrate the presentation on the

samplethe fouulairesde Crsuch abanks informmancehalf-ye1993 thalf-yeperiodM&A oative bbuild oinvolve

In tnecessthe speinefcioutput

Herductioconseqmeasuapproalows. Mfor bannot haliquidiproducmonitoa fractitrading

To brokergies inand (iiqualitabalanctechniqmationdeposiservice

6 An iregionalare availevel.

7 The homoge

8 To pformatiocannot bin their cial martechniquStability (2014), http://dx.doi.org/10.1016/j.jfs.2014.08.004


tail banking and in investment banking. The study usesbalance sheets and income data7 over the period from1. Thus, our sample is constituted of more than 3200observations of banking rms. Over the nineteen-yearred by this study, a large number of mainly internaltions have modied the geography of regional cooper-ng groups. We have registered all these operations toal bank sample, merging the accounting data of banks

M&A operations.tudy, we focus on business risk in retail banks. So, it iso integrate all the sources of risk of a retail bank intoation of the distance function. Indeed, the measure of

may be sensitive to the specications of the inputs andtors.

do not use the conventional intermediation and pro-proaches to specify model inputs and outputs andly, we do not use stocks of assets or liabilities asf banking outputs. Instead, we use a value-creationhich uses ows of services and may be justied as fol-rn banking theory highlights two main raisons dtreto provide funding to dependent borrowers who dony other sources of external funding, and to provide

the economy. To assume these economic roles, banks information services through borrower screening and

activities, and (ii) liquidity services by implementing reserve methodology (to provide funding liquidity) orvities (to provide market liquidity).uce such services, banks use various technologies: (i)echnologies, which are pure intermediary technolo-h banks do not transform the characteristics of assets,nsformation technologies, in which banks perform aasset transformation, which implies a mismatched banket in terms of risk, liquidity and maturity. Using these

retail banks provide various ows of services: infor-vices through lending, liquidity provision services toand short-term creditors, and nancial and brokerageank customers are prepared to pay for these services.

tional peculiarity of large French mutual banks groups is that the are the groups main shareholders. Thus, detailed regulatory reportst both the regional bank level as well as at the consolidated group

ome from regulatory accounting reports, which facilitate reliable andata.

such low-default services, banks have to bear risks and manage trans-cesses. They are implementing inter-temporal risk smoothing (whichersied) by holding assets and liabilities of different characteristicse sheet, and cross-sectional risk-sharing that contributes to nan-quilibrium, which necessitates the use of efcient risk management

Please citFinancial


Table 1Descriptive statistics in the banks sample (in D 1000).

Y1 Y2 Y3 X1 X2 X3

Mean 190 244 186 532 306 512 128 739 114 502 711 938Median 134 611 113 446 5251 94 552 71 240 438 330Std 273 330 302 976 2 955 635 175 091 278 161 1 370 549Min 472 512 68 210 4 484 694 10 233 10 858 69 887Max 3 504 366 5 299 506 5.30e+07 1 976 325 4 199 188 1.57e+07

Source: ACPR and authors calculations.

This approaidentied aalso invitesows.9

Thereforretail bankssured in terliquidity seone consistusing nancand insuranfolio and oof services ing. These rform of feeof credit cament of cuor savings est marginsmethodologcorrespondmeasuremepensation fthe interestbetween thinterest ratpensation tin exchangea premiumrisk, includthe provisioany elemenpaid by the supplies thelending serspread. Olending servspread mlogic is appltomers: it isand a spreon depositsthe rate of rlected in mthe bank pathe provisiolowing the s

9 See also Wsheet or stocbanks as proceon a hypothesof nancial pro

asurio anhis apf outhat servis usinby nred bsalar

of rs coseece lowntaincted

s insle 1 pd in

resu

ot in

t, weng tod usekeliha haes ase theasu

e if ittion.

presRemefc

han a scor

4 illur thehe iner han bes to best vic cch to banking production allows banking services to bend distinguished from nancial services. The approach

a measurement of banking outputs in terms of income

e, the model considers these three main outputs of, linked to the main functions of banks and largely mea-ms of income ows. The outputs are lending services,rvices and nancial and brokerage services. The latters in implementing a nancial transformation processial market instruments and in selling savings productsce products, and corresponds to the supply of port-

ther nancial consultancy services. These three typesprovide the main sources of revenues in retail bank-evenues are provided in two forms: (i) directly, in thes and commissions which are direct prices for the salerd services and other services linked to the manage-stomer accounts, as well as for the sale of insuranceproducts, and (ii) indirectly, as a component of inter-

on loans and deposits. Thus, it is necessary to adopt ay to identify the component of interest margins whichs to the purchase of banking services. Consider, rst, thent of the component of interest margins serving as com-or the lending services supplied by banks. In this case,

margin should be computed by taking the spreade interest rate paid by the borrower and a referencee. Formally, this reference interest rate is the com-he investor receives for forgoing current consumption

for future consumption. This compensation includes for the relevant risks (such as liquidity risk or defaulting the risk of the banks insolvency) associated withn of funds by creditors. This compensation is free ofts of banking services. Thus, the effective interest rateborrower contains a compensation for the investor who

funds and bears the risks, and a compensation for thevices supplied by the bank, which corresponds to thenly this spread should be taken as the compensation forices. Finally, the sum of commissions on loans and this

easures lending services as a banking output. The sameied to measure liquidity services provided to banks cus-

the sum of commissions on liquidity services providedad which is a component of the interest rate margin. This spread is measured by the difference between: (i)eturn the bank gains when it invests the deposits col-oney and interbank markets, and (ii) the interest rateys to depositors. This spread compensates the bank forn of liquidity services. The third output is measured fol-ame approach, adding the interest margin on securities

and treportfol

In tows oprices These procesinputs measuby the the usesysteminput rprovidto maiunexpea bank

Tabputs an

5. The

5.1. Pr

Firsspondimethomum lifollow and givwe chous to machievpropor

WeFig. 3. bank incient thave a

Fig.tier ovehand, tthe othcycle cappearthe loweconome this article in press as: Chaffai, M., Dietsch, M., Modelling and measuStability (2014), http://dx.doi.org/10.1016/j.jfs.2014.08.004

ang and Basu (2006) and Wang et al. (2008). For these authors, balanceks measures of bank outputs do not coherently reect the role ofssors of information and transaction services, because they are basedis of xed proportionality between the ow of services and stocksducts, which is not always veried in practice.

Table 2Distribution o

Inefciency y transactions to commissions directly associated withd insurance brokerage services provided by the bank.proach of banking production, all banking services aretput which are measured by the explicit and implicitbanks customers are willing to pay for such services.ces are produced by implementing a transformationg real resources and risk capital. Two inputs are realature: labour and physical capital. The real inputs arey the corresponding costs. Labour costs are measuredies paid to employees and the expenses connected witheal physical capital (ofce rental costs and informationts) by the corresponding operating charges. The thirdts the fact that to perform risk-bearing activities and-default risk products and services, retail banks have

equity capital, the role of which is to absorb potential losses. Equity capital provides such protection againstolvency risk.rovides descriptive statistics of the sample banks out-

puts.

lts

efciency measurement the benchmark frontier

estimated the directional distance function corre- the frontier with three inputs and three outputs. Thed for estimating the parameters is the stochastic maxi-ood method. The inefciency component is assumed tolf-normal distribution. It represents prot inefciency

value equal to the inefciency score. Remember thate directional vector g = ( gx, gy) = (1, 1), which allowsre the degree of technical efciency that the bank can

increases its outputs and reduces its inputs in the same

ent the distribution of inefciency scores in Table 2 andember that the distance is a measure of the degree ofiency. Therefore, a bank with a low score is more ef-

bank with a high score (banks located on the frontiere equal to 0).strates the volatility of the distance to the initial fron-

19932011 time period (end-of-year data). On the oneefciency score dispersion is quite large each year. Onand, the inter-temporal variability across the business

highlighted. Indeed, the average annual score clearlye affected by the business cycle. We have veried thatalues of efciency scores are correlated with the worstonditions. Accordingly, the worst values of the scoresring business risk and the resiliency of retail banks. J.

f the inefciency scores in the sample (in %).

P5 P10 Q1 Median Mean Q3 P90 P95

score 3.2 3.6 4.2 5.0 5.5 6.0 7.4 8.9

Please citFinancial


correspond2003, and 2

5.2. Busines

We nowdened as toutputs. It ifrontier andto an increaprots. In thas a percen

As menin drawingscores, all ycated this peach bankspercentilessponding toof the businis given byobtained at

We consbanks facin

- In the rs can be

prot specication of the distance function to estimate prots,and we choose the directional vector g = (1, 1), which allows

measure inefciency if the bank could reduce its costs iname proportion as the outputs. In this case, the bank cant its cost structure in the face of a shock to its main incomees.e second case, we assume that inputs are rigid and can-e reduced in the short term. Consequently, we have usedrevenue specication of the distance function, choosing thetional vector g = (0, 1). In this case, the bank is unable toce its

mai

noteor eaeducptionof bule 3 l projustmartile

shocencyn aveand,o assy corand Fig. 3. Distribution of inefciency scores.

us tothe sadjussourc

- In thnot bthe direcreduto its

Wesame fput is rassumterms

Tabof totacost adlast qustronginefcisents aother halent tactuallto deme this article in press as: Chaffai, M., Dietsch, M., Modelling and measuStability (2014), http://dx.doi.org/10.1016/j.jfs.2014.08.004

Fig. 4. Dispersion of inefciency scores by year.

to periods of severe recession in France (19941996,0092011).

s risk measurement simulation results

present the results of the simulations. Business risk ishe response by a banks prots to adverse shocks to thes measured by the gap between the initial benchmark

the successive shocked frontiers. This gap correspondsse in prot inefciency, which is actually a decline ine following, the business risk measures are presented

tage of total prot.tioned above, we dened a scenario which consists

in the 10% last-percentile distribution of inefciencyears good or bad included. For each output, we repli-rocedure 500 times so that we obtained 500 values for

business risk (measured either by the 90% or the 95% of the distribution of gaps between frontiers), corre-

500 distinct shocks to a given output. The nal valueess risk (earnings-at-risk) due to shocks to this output

the average value of the 500 high-percentiles values each stage of the simulation.ider two polar cases depending on the possibility forg a shock to reduce their costs:

t case, we assume that input amounts that is, costsreduced in the short term. Consequently, we use the

In this smultiplyingobtained byciency sco

For instathe estimatin that bansimulated dwe consideprot decreadjust coststotal prot.services procosts and st

First, ouness modelquite low ininstantaneohave no capof a strong demand forsible scenarprot inef

Second, put shock, rstrongest dts, and 16.capacity to

10 Here, advewhich is a synbased on distaUsing differenthe model, thescenarios.ring business risk and the resiliency of retail banks. J.

costs when confronted with sudden unexpected shocksn revenue sources.

that for each simulation, the shock magnitude is thech output. The assumption that the volume of each out-ed in the same proportion is mainly illustrative.10 This

allows us to compare the results between outputs insiness risk.below presents measures of business risk in percentt of the banks sample, and for the two cases (withent and without). As mentioned above, drawing in the

of the original inefciency score distribution simulatesks to outputs, as shown by the previous distribution of

scores (Table 1). Indeed, on average, the shock repre-rage output reduction of around 30% in that case. On the

drawing in the complete distribution of scores is equiv-uming an average reduction of 12% of outputs, whichresponds to a signicant shock to business volumes orfor banking services.imulation, major shocks to outputs are computed by

every banks output by the value of inefciency scores drawings in the last quartile of the distribution of inef-res.nce, if we consider a strong shock to a banks loans,ed value of business risk is equal to a 4.42% decreaseks total prot if (i) we take the 90% percentile of theistribution of the banks prots after the shock and (ii)r that the bank can adjust its costs. It reaches a 6.53%ase if we take the 95% percentile. If the bank cannot, the reductions reach 8.88% and 53.9%, respectively, of

Note that shocks to demand for deposits and to otherduce weaker declines in prots if the banks can adjustronger declines in prots if they cannot.r results verify the resiliency of the retail banking busi-. Indeed, business risk measured by prots at risk is

the case of strong shocks to outputs. To nd very largeus decreases in prots, we have to assume that banksacity to reduce real inputs in the short term in the faceshock to demand for other services (which is actually

insurance or savings products) and in the worst pos-ios which corresponds to the 95% percentile of theciency distribution.whether costs can be adjusted or not following an out-esults show that shocks to lending services provoke theeclines in prots (equal to 4.4% and 6.5% of total pro-9% and 110.9% of total prots, depending on the banks

reduce costs or not, when we consider the 90% and

rse shocks are built by drawing in the efciency scores distributionthetic measure of performance. So, technically, using a methodologynce function invites to consider similar shocks to different outputs.t proportions for different outputs would need to estimate, outside

sensitivity of outputs to the macro variables which dene adverse

Please citFinancial


Table 3Measures of business risk as the value of the 90% and 95% percentiles of the distribution of prot decreases following major shocks to banks outputs: (business risk denedas declines in prot in % of total prot).

Percentiles

90% 95%

95% percensensitivity oIndeed, lendmost protacial crisis, trecently, 20Moreover, tby strong iincome thasion servicelower than large extendeterminession of insufrom fees pof nancialvolatile, du

To summity is quite demand shcrucial roleshocks by dThat is we abanks of threach this sour results cumulate thlikely not dyears.

6. Conclus

As the rshould be cindustry. Hthe same inmore resilieness modelbanks, whicinstitutionssis has reveretail bankiized by a refocused on rcrisis quite of business models.

This paping businesrisk in bankmethodologmore specion the dua

unct, thet dehockteretion,nd ind onics o-efcuirelemeery bble ors anting risinhis st

of Fr whiodelretaieducshornside

ndss me ouant e in are uore

d bys tha, shoc, whi

the s is lctivit

wled

woun, MWith costs adjustment g(1, 1)

Shocks to lendingservices

Shocks to liquidityservices

Shocks to otherservices

4.4 3.5 2.9 6.5 4.1 3.4

tile). This result might be the consequence of the higherf a banks prots to the supply of this kind of services.ing activity is likely to be the most strategic, if not theble, activity for retail banks. During all periods of nan-hat is, in the periods 19931995, 20012003, or more082009, lending activity decreases quite substantially.he lending market during the 2000s was characterizednterbank competition, which eroded the interest ratet banks earned on loans. If we consider liquidity provi-s, the sensitivity of this source of prot is not so muchthat of lending activity. In fact, this sensitivity is, to at, attributable to the volatility of interest rates which

the margin on deposits. Lastly, concerning the provi-rance services, where revenues are derived principallyaid by households as banks customers, even in periods

crisis, revenues from this type of activity seem lesse to large inertia effects.arize, results show that the decline in banks protabil-

sustainable even if banks are impacted by quite severeocks. The ability to adjust real costs certainly plays a

in this result. Recall that in our approach we createrawing in the tail of the distribution of efciency scores.ssume that most banks reach the situation of the worste sample. And, even if retail banks all together wouldituation, the value of the decrease in prots given by(a decrease of prot equal to around 10% per year if wee three outputs shocks at the 90% percentile), wouldeplete their available capital buffers before several

ion

ecent nancial crisis has demonstrated, business riskonsidered as one of the major risks facing the bankingowever, this type of risk did not affect all banks withtensity during the crisis. Some banks appeared to bent to shocks than others, depending on the main busi-

they were running. This is notably the case for retailh seemed to be less affected by the crisis than nancial

running other business models. Indeed, the recent cri-aled the existence of stronger resiliency factors in theng business model. Even if retail banking is character-latively rigid cost structure, most deposit-taking banksetail banking businesses have come through the recent

prot fIndeedof proSuch sencounIn addiputs afoundeconomis dataare reqto imptreat evprotafrontieemanathose a

In tsamplebanks ness mwhere ity to rin the and co

Ourbusineadverssignictainablbanks term. Maffecteserviceticularprotstive toactivityable, arisk.

Ackno

WeI. Hasae this article in press as: Chaffai, M., Dietsch, M., Modelling and measuStability (2014), http://dx.doi.org/10.1016/j.jfs.2014.08.004

well. In fact, the crisis has shown that the specicationrisk sources varies across banks activities and business

er proposes a new approach to modelling and measur-s risk, and it uses this approach to compute businesss running a retail banking business model. This newy is based on the efciency frontier framework and,cally, on the directional distance function. It relieslity property between the distance function and the

Stability of in the Shadmeeting, Wcomments.

Appendix A

See TablWithout costs adjustment g(0, 1)

Shocks to lendingservices

Shocks to liquidityservices

Shocks to otherservices

16.9 14.1 8.9110.9 90.7 53.3

ion to provide a measure of business risk as lost prots. directional distance function facilitates an estimationcreases induced by adverse shocks to banking outputs.s replicate the worst nancial situations banks haved in the 19932011 time period covered by the study.

the methodology allows simultaneous changes in out-puts to be taken into account. This approach is well

the theoretical foundations provided by the microe-f production, cost and prot. Moreover, the approachient because only data on input and output amountsd. The approach can also serve as a new methodologynt stress testing in banking. The innovation here is toank as if it could fall in the last 10 percentiles of the less

nes, and to compute the distance between non-shockedd shocked frontiers, while neutralizing prot decreasesfrom non-macroeconomic or systemic shocks, such asg due to inefcient management.udy, the methodology is applied to a quasi exhaustiveench banks mainly composed of cooperative regionalch have all been identied as running a retail bank busi-. We measure business risk in two situations: a situationl banks facing shocks to output volumes have the capac-e their costs, and a situation where bank costs are rigidt term. We also model shocks in a consistent mannerr shocks of different intensities.ings conrm the strong resiliency of the retail bankodel. Indeed, business risk prot declines caused bytput shocks is low in the case of moderate but shocks to outputs, and this risk appears to be sus-the case of strong shocks to output volumes, even ifnable to adjust costs to output changes in the shortover, results show that retail banks prots are more

shocks to lending services and to liquidity provisionn by shocks to households portfolio services. In par-ks to lending services provoke the highest decrease inch means that retail banks prots are the most sensi-upply of this kind of services. In other words, lendingikely to be the most strategic, if not the most prot-y for retail banks, protecting banks against business

gements

ld like to thank two anonymous reviewers, O. de Bandt,. Koehler, and the participants at the Conference Thering business risk and the resiliency of retail banks. J.

the European Financial System and the Real Economyow of the Crisis, Dresden, 2013, the EUROBANKINGien, June 2011, the NAPW workshop, 2012, for helpfulAll remaining errors are ours.

.

e A1.

Please citFinancial


Table A1Maximum likelihood parameter estimates of the directional distance function.Stochastic frontier parameter estimates, normal/half normal distribution.

Variable Direction (gy = 1, gx = 1)

Est. t-Statistic

tr 0.0027 2.52trs 0.0004 3.8sy2py1t 0.0045 9.09sy3py1t 0.0331 29.88sx1my1t 0.0057 7.7sx2my1t 0.0046 6.98sx3my1t 0.0007 1.46sy2py1 0.0240 4.31sy3py1 0.2333 14.65sx1my1 0.0095 0.98sx2my1 0.0851 8.76sx3my1 0.0099 1.42sy22 0.0055 4.11sy33 0.0404 15.97sy23 0.0345 12.36sx11 0.0014 0.46sx22 0.0011 2.28sx33 0.0096 9.71sx12 0.0032 2.23sx13 0.0063 4.68sx23 0.0086 7.46sy2 1 0.0037 2.51sy2 2 0.0005 1.01sy2 3 0.0031 2.3sy3 1 0.0077 4.65sy3 2 0.0049 3.32sy3 3 0.0345 22.59Intercept 0.7809 126.88u 5.8428 108.76v 5.2247 59.94Aic 8138.8326Log likelihoo

References

Bcker, K., 200Accord T

Chaffai, M., Dietsch, M., 2009. The effect of the environment on bank branches protefciency. In: Productivity in the Financial Services Sector. SUERF BanqueCentrale du Luxembourg, SUERF studies 2009/4.

Chambers, R.G., Chung, Y., Fre, R., 1998. Prot, directional distance functions, andNerlovian efciency. J. Optim. Theory Appl. 98, 351364.

Chung, Y.H., Fre, R., Grosskopf, S., 1997. Productivity and undesirable outputs: adirectional distance function approach. J. Environ. Manage. 51, 229240.

Drehmann, M., 2008. Stress tests: objectives, challenges and modelling choices.Sveriges Riksbank Econ. Rev. 2, 6092.

Fre, R., Grosskopf, S., Noh, D.W., Weber, W., 2005. Characteristics of a pollutingtechnology: theory and practice. J. Econometr. 126, 469492.

Fre, R., Grosskopf, S., Weber, W., 2004. The effect of risk-based capital requirementson prot efciency in banking. Appl. Econ. 36, 17311743.

Hughes, J., Mester, L., 2010. Efciency in banking, theory, practice and evidence.In: Berger, A., Molyneux, P., Wilson, J. (Eds.), The Oxford Handbook of Banking.Oxford University Press.

IFRI Foundation/CRO, 2007. Insights from the Joint IFRI/CRO Forum Survey on Eco-nomic Capital Practice and Applications.

Jondrow, J., Lovell, C.A.K., Materov, I.S., Schmidt, P., 1982. On the estimation oftechnical inefciency in the stochastic frontier production function model. J.Econometr. 19 (2/3), 233238.

Kumbhakar, S.C., Lovell, C.A.K., 2000. Stochastic Frontier Analysis. Cambridge Uni-versity Press.

Park, K.H., Weber, W., 2006. A note on efciency and productivity growth in Koreanbanking industry. J. Bank. Finance 30, 23712386.

Wang, J.C., Basu, S., 2006. Risk bearing, implicit nancial services, and specialisationin the nancial industry. In: Public Policy Discussion Paper, Federal Reserve Bankof Boston, No. 06-3.

Wang, J.C., Basu, S., Fernald, J., 2008. A general-equilibrium asset-pricing approachto the measurement of nominal and real bank output. In: NBER Working PaperNo. 14616.d (#obs) 4099.4163 (3264)e this article in press as: Chaffai, M., Dietsch, M., Modelling and measuStability (2014), http://dx.doi.org/10.1016/j.jfs.2014.08.004

8. Modelling and measuring business risk. In: Pillar II in the New Baselhe Challenge of Economic Capital., pp. 179200.ring business risk and the resiliency of retail banks. J.

Modelling and measuring business risk and the resiliency of retail banks1 Introduction: business risk concept and measurement2 Survey of current methods used to measure business risk2.1 Earnings-at-risk methodologies2.2 A structural approach to business risk

3 Methodology: the directional distance function3.1 The parametric directional distance function3.2 Using the parametric directional distance function to measure retail banks business risk

4 Data and specification of the profit function5 The results5.1 Profit inefficiency measurement the benchmark frontier5.2 Business risk measurement simulation results

6 ConclusionAcknowledgementsReferencesReferences

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Modelling and Measuring Business Risk and the Resiliency of Retailbanks

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