the financial model for albania: a panel data approach
TRANSCRIPT
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The Financial ModelFor albania:
a panel daTa approach
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elona dushkuVasilika Kota*
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* Elona Dushku, Research Department, Bank of Albania,e-mail: [email protected]
*Vasilika Kota, Financial Stability Department, Bank of Albania,e-mail: [email protected]
*We would like to thank Mr. Klodion Shehu, Head of the Financial Stability Department and Mr. Altin Tanku, Head of the Research Department at the Bank of Albania, for their helpful insight into constructing a macro-financial model, build on the current needs of the Bank of Albania. We are also grateful to the contribution of Elsida Orhan, Enald Koshi and Adela Bode, Financial Stability Department, in constructing a large and detailed database of banks’ balance sheet, which is the core of our panel approach.
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conTenTs
Abstract 5
1. Introduction 6
2. Macroeconomic and financial development in Albania 9
3. Structure of the Model 16
4. Simulations Results 33
5. Policy Applications and Conclusions 40
References 42
Appendix 45
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absTracT
The recent global crisis underscores the important role of banks and other financial institutions in transmitting and amplifying economic and financial shock. This paper introduces the Financial Model for Albania, a medium-sized model, which is still at the development stage. It is based on the detailed balance sheets of banks in Albania to see how the main macro and financial shocks affect the most important variables. This paper uses panel data methods to estimate the core behavioural equations, which are: lending volume, net interest income, credit risk asset, nonperforming loans (household and corporate sector) and credit cost equation. Analyses of the simulation results permit us to see how these shocks are transmitted to financial system and real economy.
Keywords; Model construction; financial simulation
JEL Classification; C33, C51, E44, G17
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1. inTroducTion
The recent financial crisis has highlighted the important role of the financial system in the overall economic performance and the individual sectors of the economy. The importance of the financial channels has been argued even before the onset of the crisis, by bernanke et al., (1999) who have considered them both as amplifiers and sources of business-cycle fluctuations. on the other hand, other authors, such as Meier and Mueller (2006), find that these channels are more important in times of deep financial stress than in normal times. This discussion has become crucial during the latest decade, which has experienced a fast growth of the financial markets and especially of the banking sector in the emerging economies. as such, regardless of whether the financial markets are in some form of distress or are operating in normal times, the economic policy making is relying on incorporating their development into the overall picture of the economy. in this context, financial stability has become an important objective for the welfare of the economy.
The recent years have also proved a deepening and strengthening of the public focus on central banks in the pursuit of financial stability (haldane et al, bank of england) even though this objective/mandate is the hardest to be defined. also, as cechetti (2009) discusses in his work on integrating financial stability into the framework of the macroeconomic policies, the financial stress occurrence has become very frequent and with a high cost to welfare and as such, it plays an important role in the forward-looking stabilization policy.
bardsen et al. (2006), who discuss a set of tools that central banks use to assess financial stability, conclude that these institutions use mainly two complementary approaches. in the first approach of assessing financial stability, the risks that originate from within the financial sector and contagion are mainly monitored in the form of systemically important financial institutions and common exposure across the financial institutions. in the second approach of assessing financial stability, the risks that originate and develop outside the financial system are mainly linked to other macroeconomic
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developments. both approaches require the introduction of properly developed tools to monitor and assess the impact of the risks on financial stability. The first approach relies mainly on the financial soundness indicators, systemic risk indicators as well as indicators of the common development in alternative markets and institutions. The second approach relies mainly on some form of modelling the macroeconomic and financial linkages in a given country.
in terms of financial stability objectives, the bank of albania has adopted a macro-prudential approach, which is also coherent to the micro prudential approach developed by its supervisory mission. The main concern of the macro prudential policy is to analyse the interrelation between financial institutions and markets, common exposures to economic variables, and pro-cyclical behaviours that can create risk in an attempt to strengthen the resilience of the financial system (G 30, 2010). The bank of albania carries out this task by analysing the potential risks from the financial sector to the real economy, analysing the systemic risk arising from within or stemming from outside the financial sector, and taking the necessary policy actions to deal with these risks. Given the relatively new nature of financial stability issues, the bank of albania is working to create tools that mitigate these analyses. The model we present in this paper is the first attempt to link the development in the macroeconomic area to the financial system, which in our case consists mainly in the banking sector.
The introduction of macro-financial linkages, which prior to the crisis were discussed theoretically, have currently drawn more attention for their introduction into practical and concrete policy decisions, and are therefore at the centre of many central bank researches. Woodford (2012), in his recent work, has stated that “neither standard macroeconomic models that abstract from financial intermediation nor traditional models of the “bank lending channel” are adequate as a basis for understanding the recent crisis”. The results of the detailed work indicate that these models did not capture the full potential of the financial crisis, mainly due to the weak role imposed on the financial sector rather than the direct missing of all the transmission channels.
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The aim of this model is to provide a framework of the way macro and financial shocks are transmitted to the banking sector, and possibly their projection for the future. The model is designed to analyse how different macroeconomic shocks affect risks to financial stability, deriving from outside the financial system. bank of albania’s current policy decision making is focused on the impact of the macroeconomic shock to the real economy, while the impact to the financial sector is mainly evaluated through the stress test framework. This first version of the macro-financial model uses as input the main macroeconomic variables obtained from MeaM model (dushku, Kota and binaj, (2006), Kota and dushku, (2007)) in order to evaluate their impact on the banking sector; therefore the results will be obtained in terms of profitability and capitalization of the individual banks.
The paper is organized as follows. The second section presents a broad view of the main macroeconomic and financial developments in albania. The third section presents the structure of the model and provides a detailed description of the main equations, as well as the interlinkages including the different identities. The fourth section summarizes the main shocks results, while the last section concludes and suggests some future researches in this area.
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2. MacroeconoMic and Financial deVelopMenT in albania
albania is assessed to have been characterised by a steady economic growth over the period 2000-2008, in average terms about 6.1%, with a good performance until the onset of the 2008 financial crisis (7.5%, in real terms). The impact of this crisis was mainly reflected during the following years, when the economy grew by only 3.5% (2009). The economic activity slowed down further in 2011, by 2.9% on average, while during 2012 economic growth stood at 1.6% on average.
Following these developments, the structure of the economy has also changed. Thus, a blooming of construction is noted during 2000-2008, providing a contribution by 13.8% of added value in 2008, while the services sector contributed by 51.1% on average during 2000-2008. contribution of the agriculture sector slowed down to an average of 20.3%, while the industry sector continued to provide a low contribution (8.1%). after the onset of the financial crisis, the construction sector has suffered the most, and hence lowering its contribution to the real economic growth, while the services sector has continued to expand steadily.
Chart 1: Contribution of economic sectors to real GDP growth
Source: Instat
Agriculture Industry Construction Services Real economic growth
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since 2000, the inflation rate has fluctuated within the 2-4% band, i.e., in line with the bank of albania’s targeted band of 3% (+/- 1 %). after the financial crisis, the average inflation rate rose to about 3.5% in 2010 and 2011, from 2.2% in 2009, mainly due to higher foreign prices. however, the slow economic growth during 2012, mainly due to low consumption and investments, decreased the average inflation rate to 2%.
exchange rate developments, which we discuss in terms of neer and reer, performed in line with other macroeconomic development during the crisis or normal times. The exchange rate developments stabilized after 2001, when the albanian currency slowly became stronger, until the onset of the financial crisis. during 2009-2010, the neer and reer depreciated by about 5.99% and 5.07%, respectively, as the markets stabilized toward a new equilibrium of currency exchange during the years under development.
The developments in the albanian banking financial sector are in line with the main macroeconomic landscape. The two-tier banking structure was implemented only in 1992, after the collapse of the communism and the passing of the law “on the bank of albania” and the law “on the banking system in the republic of albania”. by the end of 1996, the banking sector was still underdeveloped, with a few banks operating in the country, suffering from deficiencies in
Chart 2: Inflation rate and exchange rate performance during 2000- 2012
Source: Instat.
Annual inflation rate Average annual change
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the legal and regulatory framework, even though the economy was growing rapidly. only after the privatization of the former savings bank (after 2004), different structural reforms took place even due to the entry of major foreign commercial banks into the market, where the financial deepening through the banking sector became relevant.
The following table provides a summarised view of specific sectors’ share of financial system in albania as a percentage to Gdp. it is noted that banking system dominates the albanian financial system, sharing 81% of the system, while the share of other segments is low. currently, in albania operate 16 banks in total, 92.2% of foreign capital origin, where banks of european union origin continue to have the highest shares in the structure of banking system’s capital.
Table 1: Financial system segments as a percentage of GDP over the years, in % 2007 2008 2009 2010 2011 2012banking system 75.9 76.7 77.5 80.9 84.7 87.9non-bank institutions 1.5 1.7 2.2 2.7 2.5 2.6slas and their unions 0.6 0.7 0.8 0.8 0.7 0.78insurance companies 1.4 1.4 1.5 1.4 1.5 1.5pension funds 0.01 0.01 0.01 0.02investment funds 1.1
source: bank of albania, albanian Financial supervisory authority.
Financial system’s performance appears sustainable, where banking sector is well-capitalised and profitable. Thus, capital adequacy ratio for the entire system stood averagely at 24 % during the period 200-2008, about 2 times higher than the minimum regulatory capital set forth by the bank of albania (12 %). after the global financial crisis, a decrease in capital adequacy ratio is noted, at 15 %, showing a well-capitalisation of the banking sector. in terms of profitability, banks have been characterised by positive profits throughout the period 2000-2012. it is noted a fall in profits deriving from both their assets and funds after the ultimate financial crisis.
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assets and liabilities in banking system have been increased since 2001 to 2012, albeit from 2008 to the end of 2012 they increased at slower paces.
“customer transactions” contributed to the lower increase in assets. This item represents banking sector lending to residents and non-residents, excluding accrued interests. deposits remain the main financial contributors of liabilities that, notwithstanding the financial crisis, have continued to provide a positive contribution. deposits in the system account for averagely 83% of total liabilities.
Chart 3: Capital adequacy ratio and profitability of banks
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Capital adequacy ratio, in %
Source: Bank of Albania.
Chart 4: Decomposition of total assets and liabilities of banking system during the period 2001-2012
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Source: Bank of Albania.
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lending in albania started to grow at fast paces mainly after 2004. as at end-2012 lending accounted for 42% of Gdp, from 6.4% of total loan to Gdp. after 2004 banks expanded their credit to the economy with annual growth rate peaking at about 78% for household credit and 72% for corporate credit in 2005. Following these developments, in 2008, corporate credit reached 22.5% of Gdp, while household credit reached 12.9% of Gdp. The onset of the financial crisis had a major impact on credit growth. The annual credit growth rates slowed down significantly by 1.5% for corporate credit and by 0.43% for household credit, as at end of 2012.
regarding the structure of corporate credit, trade, construction and manufacturing are the major sectors that have been credited during this period. starting from mid-2009, credit to the construction sector slowed down significantly with a growth rate of only 6.6% at the end of the year compared to 43.2% at the end of 2008. its share in total corporate credit slowed down to 15.7% at end 2012, from 21% at end-2008. on the other hand, trade and manufacturing sectors have continuously increased their share of corporate credit, with the electricity and agriculture sectors being more attractive in terms of credit activity.
The impact of the financial crisis on non-performing loans is significant. starting with a low level of 6.6% at end-2008, credit
Chart 5: Credit performance in Albania
Source: Bank of Albania.
Share to GDP in %
Credit to corporatesCredit to household
Structure of credit to corporates, mln Other community, social and personal service activitiesHealth and social workEducationPublic administrationReal estate, renting, etc.Financial intermediation
Transport, storage andtelecommunicationsHotels and restaurantsTrade, repair of motor vehicles and personaland household goodsConstruction
Electricity, gas and water supply
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quality deteriorated rapidly, reaching 23% at end-2012. This deterioration reflects mainly the rapidly increasing stock of nonperforming corporate loans. on the other hand, household credit quality deteriorated, though at a slower rate, coupled with negative developments in their borrowing activity.
in terms of the main sectors of the economy, the construction sector displays the fastest pace of increase in nonperforming loans, reaching 29.6% by June 2012 from end-2008. The trade sector also indicates high rates of nonperforming loans ratio, while the electricity, gas and water supply sector, which has started to be credited lately, shows a better performance.
Finally, an important component of the financial development is the costs of financial intermediation. even though it is difficult to evaluate these costs, the bank interest rate margins are generally used as a good indicator. chart 7 shows that despite the downward trend of interest rates on deposits and loans, their margins do not appear to narrow significantly. by end-2000, the interest rate margins amounted to 5.94% and widened during 2004-2005, mainly due to lower deposit interest rates. after 2008, the interest rate margins expanded due to higher credit cost, which slowed down only during the first half of 2012. however, over this period, the interest rate margin remained around 5.1% by the end of 2012.
Chart 6: Non-performing loan ratio by different agents (left)and sectors (right) in the economy
Source: Bank of Albania.
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Chart 7: Interest rate performance in Albania
Source: Bank of Albania.
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3. sTrucTure oF The Model
The section below presents the main structure of the model. our version of the macro financial model is a small and medium-sized model that includes the financial sector, especially the banking sector in albania, and some macroeconomic variables. The model focuses on banks’ soundness in the albanian financial system. For these banks, the model provides a quantitative framework for assessing how different shocks are transmitted to banks’ balance sheets, taking into account the macro-credit risk, the interaction between banks and the feedback effect arising on both the asset and liability side of the balance sheet. This model works also as a satellite model of the macroeconomic model of albanian economy, MeaM1 to accomplish a full transmission mechanism from real to financial sector and then to real sector. as cecchetti, G et al (2009) has mentioned, the main lesson of the financial crisis is that “our models need to find a more meaningful role for finance”. Modelling the behaviour of banks is not an easy task, but we should know how they respond to our measures and how this shock is transmitted to the real economy.
a-Model oVerVieW
chart 8 shows in detail, the main interlinkages between the most important variables and the main channels incorporated in the model.
The transmission mechanism of our model consists in to main channels: banks credit channel and banks capital channel. The exchange rate channel is included through MeaM model, while assets price channel (approximated through houses prices) is exogenous and it is thought to be modelled later.
based on transmission channels of banks’ balance-sheets, negative shocks on financial institutions may drive to a contraction in lending and then of economy. These shocks amplify higher if two
1 a detailed description of MeaM is given at dushku, Kota and binaj, 2006 “a Macro econometric albania Model”, round Table, 2 , also Kota, V and dushku, e, 2007, ” a Macro econometric albania Model-a follow up”.
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conditions are met: first, banks are not able to isolate the negative impact of lending fall; and second, borrowers depend considerably on banking lending.
by banking lending channel we expect that one shock which affects banks’ balance-sheet, affects accordingly the cost and availability of loan, that goes beyond the traditional monetary policy channel, through the interest rates channel.
on the other hand, based on the bank capital channel, a reduction in bank capital increases the banks’ cost of funds, which also affects the borrowers’ cost of funds. another reason why bank capital can affect lending is the regulatory capital requirement that banks should meet. This criterion is put as an upper bound on bank assets and thus affects the banks’ lending. requirements for additional capital to risk-weighted assets have the potential to further exacerbate the effect of bank capital on lending. The worse economic conditions deteriorate the actual bank capital ratio not only due to increase in loan losses, but also due to increase in risk-weighted assets. bank capital affects lending even when the regulatory constraint is not momentarily required, which implies that shocks to bank profits, such as loan defaults, can have a persistent impact on lending.
after the financial crises, the literature highlighted the role of liquidity channel, as a determinant of banks’ ability to extend credit, and in turn to affect the real economic variables, either by influencing the strength of the traditional bank lending channel, or by creating additional transmission channels. The net worth of financial intermediaries is sensitive to fluctuations in asset prices, hence banks adjust their balance sheets in such a way that leverage is high during booms and low during busts. in our case, however, the model focuses on both traditional transmission channels which are relevant for albania, the banks’ lending channel and banks’ capital channel.
other element of this model is the feedback channel, which considers the second-round impacts from financial sector to the real one, through the effect this indicator has on lending. in this first version of the model, this impact is simply modified by directly
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affecting nominal Gdp rate, without specifying or dividing the impact that lending has on consumption and investments.
at the core of our financial model is a detailed database of the banks’ balance sheets ranging from 2002 Q1 – 2012 Q4 for all the 16 banks operating in albania. The balance sheets data of each individual bank is disaggregated into two main classes of assets and liabilities, where the link between macro economy and the main (credit) risk on banks’ balance sheet is crucial to our financial model for analysis purposes.
chart 8: albania Macro-Financial Model Mechanism2
2 Where endogenous (or behaviour) variables are presented by oval forms, endogenous variables that are defined as identity are presented by rectangles with rounded corners, and the exogenous variables by rectangles. arrows and their direction show the dependence of variables in the model, whereas the arrow at interrupted lines shows the feed-back channel between lending and Gdp in the model.
Figura 8: Mekanizmi i modelit makrofinanciar në Shqipëri
BANK LIABILITIES
DEPOSITS
OTHER LIABILITIES
BANK NET ASSETS
Shareholder’s equity
Other net assets
BANK ASSETS
LENDING VOLUME
CORPORATELENDING
HOUSEHOLD LENDING
Other lending
Other Assets
Treasury bills
Bank securities
BANK CAPITAL
RWA
CAPITAL ADEQUACY
RATIO
NOMINAL GDP
REAL GDP
CAR GAP
UNHOUSEPRICENEER REPO
LENDING INTEREST
RATELENDING
GAP NET INTEREST INCOME
OPERATING PROFIT OF CORE
BUSINESS
NET OPERATING
PROFIT
NONPERFORMING LOANS
CORPORATE HOUSEHOLD
CREDITCOST
WoF
PROVISION
RWF
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b. speciFicaTion
our financial macro model has on total 35 equations, where out of eight are behavioural equations and the rest are identities. The estimated equations are: household and corporate lending volume equations, lending interest rate equation, net interest income equation, credit cost equation, credit risk equation and portfolio risk (or non-performing loans) equations for households and business sector. all the dates are quarterly, from 2001 Q1 until 2012 Q4. The appendix shows a detailed description of the definitions and measuring for each variable.
c. MeThodoloGy
our model includes 49 financial and macroeconomic variables (see Table 2 in the appendix). We have used the quarterly annualized growth rates as the main variables and we have paid attention to enter all variables as stationary variables in all behavioural equations. estimation of the equations is based on regressions with fixed effects, to account for the dynamic relationship at individual bank level. obviously, modelling and estimating the behaviour and dynamic of financial variables is a very critical process and we have chosen as simple as possible model to see how these main financial variables interact with each other and with the main macroeconomic variables.
in panel data, banks are observed at several points in time, and we have analysed the linear relationship between endogenous variables and explanatory variables, which are taken to be exogenous. a general approximation of a multiple linear regression for banks i= 1, 2, 3…N, who is observed at several time periods t=1, 2, 3…, n is given as below:
yit= α+x’itβ+ci+ uit (1)
Where, yit is the dependent variable, x’it is a K-dimensional row vector of explanatory variables excluding the constant, α is the intercept, β is a K dimensional column vector of parameters, ci is an individual-specific effect and uit is an idiosyncratic error term.
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regardless the evolution and the changes that have occurred to the banking sector in albania, we have estimated the linear regression based on the so-called balanced panel, where we have the observations for each bank i, in all time periods t.
The T observations for individual i can be summarized as follows:
yi = [ yi1 yityiT
]Tx1
Xi = [ x’i1 x’itx’iT
]TxK
ui = [ ui1 uituiT
]Tx1
(2)
and nT observations for all banks and time periods are presented as:
Y = [ yi yiyN
]nTx1
X = [ X1 XiXN
]TxK
ui = [ u1 uiuN
]nTx1
(3)
Where data generation process (dGp) is described by linearity, independence and idiosyncratic error term uit is assumed uncorrelated with the explanatory variables of the same individual. We have chosen to estimate fixed versus random effect equations, to see how the main relationships or variables vary across individuals at the same point in time, and possibly over time for all individual banks taken together3. a detailed description of the main behavioural equations and the factors that affect the core variables is given in the following section.
our financial model performs well according to common diagnostics. The equations show a good performance in the sample, having an adjusting coefficient r2 that ranges from 40 to 85% on average, excluding the credit cost equation that has a lower fit. another diagnostic feature is the forecasts performance that is reasonable, for the major part of the equations in the sample. regardless the feature presented above, the most important feature is the performance of the whole model, measured by the impulse responses of the main shock running into the model. detailed descriptions of the main simulations are presented in the last section.
3 due to the lower (cross-section) banks number than the number of the period we use in the model, we have been oriented towards fixed-effects regressions, by not considering GMM models.
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d. Main behaVioural eQuaTions
a detailed description of the behaviour equations and the factors that affect the core variables is given as follows, while all the identities are presented in the appendix 1.
1. corporaTe lendinG VoluMe
Log(lendv_c_g)= 0.12 + 0.64*log(lendv_c_g(-1)- 0.011*lend_ir_real(-1) p-value (0.05) (0.00) (0.046) + 0.41 * d(car_gap(3)) + 1.78 * gdp_g(1) (0.0) (0.00)
r2-adj=0.8 (equation 1)
Wherelendv_c_g= year on year growth rate of lending volume to corporate, lend_ir_real= lending interest rates in real terms,car_gap= gap of the capital adequacy ratio to the 12% minimum required level,Gdp_g= Gdp annual growth rate in real terms,
credit to the private sector is an important component of banks’ activity. as at end 2011, this indicator accounted for 73% of the total lending activity, and approximately 30.5% of Gdp. Thus the performance of this indicator is important in analysing the current and future developments of the economy and the banking sector. The determinants of the credit to the private sector are mainly divided into the demand and supply side, even though, it is difficult to fully separate these channels as many variables may explain developments from both sides. Generally, most studies include an economic activity variable (for example Gdp) and financing costs (for example bank lending interest rates) as the main drives of crediting (calza et.al.2001). authors such as Kashyap et.al (1993) argue that higher economic growth allows the agents to borrow more in order to finance their consumption and investments; therefore there should be a positive relationship between Gdp and the lending activity. on the other hand, the relationship between the demand
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for loans and their cost in the form of bank lending interest rates, is mainly found to be negative (calza et.al.2001) and with a strong impact on the demand for loans (Kakes, 2000). however, as these authors argue, a third component of borrowing cost is comparing the bank lending interest rates with the cost of alternative internal or external sources of financing for the corporate. in the case of albania, the private sector relies largely on the banking sector to finance its needs, as other forms of external financing (such as bonds) are missing, while borrowing from informal markets, though present, is difficult to be quantified in the form of historical data.
other authors, such as Focarelli and rossi (1998), include private investment as an important determinant of lending to the private sector, while taking into consideration other explanatory variables such as profitability. in our case, we suffer from lack of data, given that private investment data are published in annual terms with at least two years lag, while measures of profitability of businesses are not available.
regarding the supply-side determinants of lending to the private sector, we rely on the capital adequacy ratio of banks. The idea is that banks are able to lend money to a given demand from the businesses, if their regulatory capital requirements are fulfilled (Teglio 2011). Following the ultimate financial crisis, the long-term impact of the capital requirement on the macroeconomic development (including lending to the private sector) has been largely studied, as Teglio (2011) summarized in his work. The main findings are that not only higher capital requirement reduces the probability of the banking crisis, but it also causes lower output growth as it dampens the output volatility. in the case of private lending, it plays an important role in the banks’ credit availability and willingness to meet the demand for loans.
The estimation results in our case indicate that the impact of the cost of borrowing on lending to the private sector is rather low, mainly because bank lending is the main source of financing and as such, the demand for loans may be considered as rather inelastic to the interest rates fluctuations. expectations on future development of Gdp growth rather than past developments are found to be
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relevant, probably because from the demand side, businesses expecting better economic development in the future are more likely to ask for loans, while from the supply side, banks expecting higher economic growth are more willing to provide loans. The same implies for the impact of capital requirement: banks expecting to have better capitalization in the next year are keener to provide lending. discussing the magnitude of the coefficient, it appears that lending to the private sector is persistent with a coefficient of 0.64, while banks and the private agents incorporate their expectations for the economic growth and banking capitalization for the current development of lending.
2. household lendinG VoluMe
When considering the determinants of household lending, the macroeconomic impacts are generally in line with lending to the private sector, following the demand channel. We expect a positive impact of the economic activity on boosting household lending, while the cost of borrowing is expected to have a negative impact. concerning the capitalization of banks, this indicator is generally not expected to have impact on household lending. in the case of emerging markets, barrel and Gottschalk (2006) find that consumer crediting is not sensitive to changes in the solvency ratio, mainly due to the lower share of consumer borrowing to Gdp and banks assets.
another important factor affecting household lending from the demand and supply side is the real estate wealth, proxied by the development in the house price index. Kiss, nagy and Vonnák (2006) indicate that an increase in the property prices can affect the demand for credit through the wealth effect following the permanent income concept. Goodhart and hofmann (2008) also argue that the house prices imply a collateral effect, given that houses are used as collateral for loans. in this context, higher housing price increase the borrowing capacity of households, and thus the demand for loans. on the other hand, this collateral affects the balance sheet of the banks, capturing the supply side of the lending. nieto (2001) in his work about this channel indicated that as the housing price increases, so does the collateral value of
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the house and the borrower’s creditworthiness. Therefore, banks are entitled to provide more lending to households, indicating the importance of this variable to the overall consumer lending.
Log(lendv_h_g)= 0.04 + 0.75 * log(lendv_h_g(-1)- 0.011 * lend_ir_real(-1) p-value (0.47) (0.00 (003) + 2.76 * gdp_g(1) + 0.12*hpi_g(-2)
(0.00) (0.08)
r2-adj=0.85 (equation 2)
Wherelendv_c_g= year on year growth rate of lending volume to households, lend_ir_real= lending interest rates in real terms,gdp_g= Gdp annual growth rate in real terms,hpi_g= house price index growth rate in annual terms,
The results of the estimation indicate that the persistence of household lending is higher compared to the persistence of corporate lending with a coefficient of 0.74. The impact of the cost of borrowing on consumer lending is low, in line with the findings for the corporate, which may be attributed to the limited access of consumers to other sources of financing their borrowing. expectations on future economic growth are found to be significant, following both the supply and the demand side of determining consumers’ loans. Finally, developments in the housing prices have impacted the households’ loans, with a positive coefficient of 0.12.
3. neT inTeresT incoMe
The developments of the net interest income are very important to the banking sector in albania, as they comprise a large part of total revenues. however, estimating the determinants of this variable is difficult, as the impact of the macroeconomic conditions on their development can be ambiguous. in an early work by Kashyap et.al (1993), the results indicate that uncertainty and worsening of the macroeconomic conditions actually have a positive impact on interest rate margins. These results are later confirmed by the work of schweiger and liebeg (2009) and Kansman et. al (2010) for the
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countries of central eastern europe. however, when studying the relationship between economic growth and interest rate margins in the Western europe, claeys and Vander Venett (2004) conclude that this relationship is positive, mainly due to better lending activity and loan quality. in the case of the eastern europe, the coefficient linking these two variables is not statistically significant.
another important determinant of interest rate income, is credit risk (schweiger and liebeg, 2009; saad and el Moussawi, 2012), which is found to have an important impact on banks’ interest margins. in this context, banks generally charge higher interest rates in order to compensate for their credit risk, which may come out to be anticipated or unanticipated. Kasman et.al (2010) confirms that this theory holds for banks of the european union, while athanasoglow et al (2006) confirms these findings for the countries of south eastern europe.
net_in_inc_g=0.23+0.005*net_in_inc_g(-1)+0.17*lendv_g-0.36* dummy_08p-value (0.00) (0.00) (0.04)
+ 0.029*lend_repo(-4)+3.8*d(nplt_ratio(-3)) (0.01) (0.09) r2-adj=0.42 (equation 3)
Wherenet_in_inc_g=net interest income annual growth rate,lendv_g= Growth of lending activity,dummy_08= dummy variable for 2008 Q3,lend_repo= difference between lending rate and repo rate,nplt_ratio= ratio of non-performing loans to total portfolio of lending.
in our case, the net interest incomes show a small persistence with the growth of lending activity and its quality being the main determinants. The dummy variable for the financial crisis of 2008 is also relevant, while the interest rate margins between the average lending rate and repo rate have a small positive impact on the development of net interest income.
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4. lendinG inTeresT raTe eQuaTion
as we mentioned at the beginning, the interest rate channel occurs when the policy rate determined by the central banks’ actions affects deposit and lending rates, thus impacting both the quantity of credit and the deposit demand. on the other hand, the credit channel refers to the differing impacts of monetary policy on banks, depending on their specific characteristics. Thus, variations in the quantity of credit, when the central bank changes the monetary policy, affect the supply of loans and the demand for deposits by changing the external finance premium (the discrepancy between the cost to raise funds externally, mainly through loans, and the opportunity cost of internal funds or deposits). There is a lot of theory that explain how bank capital could influence the propagation of economic shock, where markets are imperfect and banks are not able to raise funds in order to increase lending opportunities. in our model, when a monetary tightening (easing) occurs, this determines an increase (reduction) of market interest rate. in this case, bank capital may influence the impact of monetary policy changes on lending through the traditional bank lending channel (bernanke and blinder, 1988, stein, 1998; Kishan and opiela, 2000) and through the bank capital channel (Thakor, 1996; bolton and Freixas, 2001; Van den heuvel, 2001a).
based on the bank lending and bank capital channels, the main factors that determine the lending interest rate are: the previous rate of lending interest rate, the annual growth rate of the monetary policy rate, the annual change of the total volume of credit gap, as well as the gap of capital adequacy ratio. We have used capital adequacy ratio gap because, as mentioned by literature, the effect of bank capital on the “bank lending channel” cannot be captured by capital-to-asset ratio. This measure is used to analyse the distribution effect of bank capitalization on lending and does not take into account the riskiness of a bank portfolio. The capital adequacy ratio gap measures the excess capital that banks hold in excess compared to the minimum required level to meet the prudential regulation standards. Thus, this indicator represents a risk-adjusted measure of banks’ capitalization that gives more indication of the probability of a bank default, and moreover it may serve as a measure to evaluate the bank credit availability.
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We expect a positive relationship between the lending interest rate, changes in the policy rate and changes in the lending volume, but a negative relationship with the capital adequacy ratio gap. an increase in the policy rate is followed by higher funding cost that the bank passes to a higher lending interest rate. as the lending interest rate increases or decreases, the lending volume gap reflects the tighter or easer supply-demand balance. a bank with a low capital adequacy ratio raises its interest rate, since it can make a profit and boost its capital, if the interest rate elasticity of the lending volume is low. otherwise, the bank has to decrease its lending volume.
lend_ir = 0.32* lend_ir (-1) + 0.02* g_repo(-4) + 0.0008* g_lendv_gap(-1) p-value (0.00) (0.00) (0.00)
- 0.03*car_gap + 0.07 (0.03)
r2 –adjusted =0.6 (equation 4)
Where:lend_ir = average weighting of lending interest rate in nominal term,g_repo= year on year change of monetary policy rate,g_lendv_gap=year on year change of lending volume gap, car_gap=capital adequacy ratio gap,
Furthermore, when credit cost increases, a bank raises its lending interest rate to compensate a reduction in capital. We have found a negative relationship between lending interest rate and capital adequacy ratio gap. This is in line with models presented by Kim and santomero, (1988; rochet), who argue that well-capitalized banks are less risk averse, but these models are not able to explain why banks typically detain excess capital with respect to the minimum requirements imposed by the supervisory authority (for example, see van den heuvel (2003). as we will see, this is a crucial point in studying heterogeneity in the behaviour of banks due to capitalization.
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5. crediT cosT eQuaTion
credit cost in our model is defined as a sum of loan loss provisions, write offs minus recoveries of write off, while credit cost ratio is defined as a ratio of credit cost to total lending. This measure represents the cost that banks face when extending loans. The main determinants of change in credit cost growth are the growth rate of nominal Gdp and the change in lending volume. We expect a negative relationship between credit cost and economic growth, while the lending volume is expected to have positive effect on credit cost. an increase in lending volume is accompanied with an increase in loan loss provisions, which affects positively also credit cost. credit costs also have a significant impact on banks’ profits, so a higher level of credit cost is accompanied with lower net interest income. The credit cost affects the level of risk-weighted assets, which endogenously affect the level of capital adequacy ratio and the gap of capital adequacy ratio.
D(ccost_g) = - 0.65*growth_n(-1) + 0.18*D(lendv_g(-1)) + 4.13p-value (0.05) (0.00)
r2 –adjusted =0.1 (equation 5)
Whereccost_g= is the growth rate of credit cost volume4
growth_n=quarterly annualized nominal Gdp growth, lendv_g=year on year change of lending volume,
based on the main results, we found out that the change in credit cost is mostly affected by economic condition, presented through the nominal Gdp growth, while the lending volume growth affects negatively the level of credit cost growth. This equation has the poorest explanation in terms of adjusted r2 and our aim is to estimate or calibrate them in the future.
4 credit cost is measured as a sum of loan loss provision and write off minus recovered write off.
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6. risK WeiGhTed asseTs eQuaTion
risk-weighted assets (rWas) are an important part of both micro and macro prudential toolkit (le lesle, V and avramova, s, (2012) for three main reasons; first rWas can provide a common measure for a bank’s risk; second rWas ensure that capital allocated to asset is equivalent with the risks; and third, this indicator can potentially emphasize where destabilizing asset class bubbles are arising. The ranking of banks’ capital adequacy varies significantly, depending on whether capital ratios are risk-weighted or not. le lesle, V and avramova, s, (2012) have compared the level of capital by regions (europe, north america and asia pacific) based on three different indicators and the main conclusion is that: the better performance of banks under certain capital ratios is a function of four variables: regulatory environment, accounting framework, economic cycle and probability of default: banks business models, composition of rWas and methodology to determine them.
The characteristic of banks’ portfolios heavily influence the calculation of rWas. collateral quality and quantity is an important driver of loss given default (lGd), especially as risk weights move in line with lGd. Given that lGds tend to have a greater impact on rWas than pds do, banks have an important incentive to extend secured (versus unsecured) loans. The portfolio maturity is another component that influences rWas. due to greater uncertainty, the banks with longer-term assets draw higher rWas than short-term assets. so banks, whose portfolios have longer average maturity, have a higher ratio of their rWas compared to other banks.
based on basel iii, risk-weighted assets include assets for the credit risk, market risk, operational risk and other risks. in our financial model, we have estimated only the rWa for the credit risk component, as by definition and regulations issued by the bank of albania. The albanian banking system operates under the basel i standard. The new regulation “on capital adequacy”, adopted by the bank of albania, shall enter into force on the 31st december of 2014. currently, the credit risk is the only component of rWas, representing on average 60 of total bank assets. For estimating the rWas (credit risk), we have taken into consideration three
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main variables: lending volume, credit cost, total volume of total securities and treasury bills held by banks. certainly, bearing in mind that this is the first attempt to make an empirical quantification of these issues, the results should be interpreted cautiously.
risk_g = 0.19 + 0.24* lendv_g + 0.01* ccost_g+ 0.03* bills_bond_g
p-value (0.00) (0.00) (0.043)r2 –adjusted =0.6 (equation 6)
Where:risk_g is the growth rate of credit risk, in annual term, lendv_g= year on year change of lending volume,ccost_g=year on year growth rate of credit cost volume,bills_bond_g=quarterly changes of total bonds and treasury bills held by banks, in annual term.
an increase in lending volume, and increase in bonds and treasury bills held by banks raise the banks’ exposure to credit risk and immediately increase the credit risk assets. Therefore, we added the credit cost growth to the equation. When a rise in the credit cost ratio is induced by the downgrading of self-assessment due to deteriorating credit quality, there is a rise in rWas.
7. nonperForMinG loans eQuaTion
The empirical literature of the determinants of non-performing loans (npl) and the interactions between macroeconomic performances include both theoretical models and empirical models. in general, the theoretical business cycle model emphasizes an explicit role in financial intermediation. The financial accelerator theory, (bernanke and Gertler, (1989), bernanke and Gilchrist, (1999), Kiyotaki and Moore (1997)), focuses on the importance of the macroeconomic linkages to npl performance. in this framework, the determinants of npl are both institutional/structural and macroeconomic. institutional or structural indicators are based on financial regulation and supervisions of national authority, as these indicators are more important in explaining the cross-country difference of npls. Macroeconomic variables include indicators
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that influence borrowers’ balance sheets and their debt servicing capability.
literature focusing on determinants of npls has identified that the main macroeconomic determinants of npls are Gdp growth, unemployment and interest rate. These variables provide additional information regarding the impact of macroeconomic conditions on households and firms, justified also by the theoretical life-cycle model. in particular, an increase in the unemployment rate should negatively influence the cash flow level of household and increase their debt burden. an increase in unemployment gives signals to the firms for a decline in demand, which implies a fall in their production and shrinkage in the firm’s revenues, accompanied by a fragile debt condition. The interest rate is expected to influence positively the number of npls because it affects the difficulty in servicing debt. We have included also the exchange rate variable, because a large share of credit portfolios is lending in foreign currency and the impact of exchange rate is mixed. on the one hand, an appreciation weakens the competitiveness of export-oriented firms, and adversely influences their ability to service their debt (Fofack, 2005). While on the other hand, it can improve the debt-servicing capacity of borrowers who borrow in foreign currency.
because of the nature, compositions and specifics of npls in albania, we have estimated two different equations for npl: one for household and one for corporate. The main factors included are: real Gdp growth, quarterly lending growth, real lending interest rate and nominal effective exchange rate developments. in a more detailed description, the estimated equations are given below:
7.1 households npl equation
Log(nplh)=0.69*log_nplh(-1)-4.55*gdp_g(-4)-4.59*lendvh_ratio + 0.03* lend_ir_cpi(-4) + p-value (0.00) (0.00) (0.00) (0.00)
0.30* d(un(3)) + 4.08* d(log_neer(-1)) - 0.78 (0.04) (0.05)
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r2 –adjusted=0.81 (equation 7)
Wherenplh is log transformation5 of nplhs ratio, lendvh_ratio = quarterly change of household lending, lend_ir_cpi = real lending interest rate,un = unemployment rate,neer = nominal effective exchange rate,
7.2 corporate npl equation
Log(nplc) = 0.78*log_nplc(-1) - 3.30*gdp_g(-4)-0.11*lendvc_ratio(-1) + 0.02* lend_ir_cpi(-1) + p-value (0.00) (0.03) (0.00) (0.06) 4.06* d(log_neer) - 0.64 (0.07)
r2 –adjusted 0.73 (equation 8)
Wherenplc is log transformation of nplc ratio, gdp_g = real Gdp growth,lendvc_ratio =quarterly change of corporate lending, lend_ir_cpi = real lending interest rate,neer = nominal effective exchange rate,
all the variables have the expected sign: the behaviour of npl for households is more influenced by lending growth, economic conditions and exchange rate developments, while real interest rate and unemployment have almost the same impact. The same implies for the corporate npls. also the magnitude of coefficient is comparable with other research studies of bank of albania that have estimated the main determinants of npl in albania.(shijaku and ceca, (2010), Kalluci and Kodra (2010), dushku and Vika, (2011)).
5 i.e. log(nplhs/(1-nplhs) where nplhs is nplhs ratio). This transformation ensures the dependent variables spans over the interval ]-∞, +∞[ ( as opposed to between 0 and 1) and is distributed symmetrically.
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4. siMulaTions resulTs
as previously discussed, the shock and simulations analyses are the best way to evaluate the performance of the whole model, in terms of reaction of all endogenous variables. We carry out this task by running different shock and simulations in the form of monetary policy shock, macroeconomic simulation, exchange rate shock and capital increase shock. Generation of our baseline6 is a function of our endogenous variables predetermined as in section 4, with assumptions that exogenous variables have a determined value and behaviour and all other exogenous shocks are equal to zero. This means that the economy will not be subject to additional potential shocks. The shock simulations results for the entire banking system are given as the discrepancies between the simulation results and the baseline, expressed in percentage or in base points. as shocks results are taken within the sample, the deviations of scenarios from the model baseline bear also their current behaviour during the period of assessing the equations in the model.
1. MoneTary policy shocK
a tight monetary policy directly weakens borrowers’ balance sheets in at least two ways. First, a rise in interest rate increases the interest expenses, reduces net cash and weakens borrowers’ financial position. second, a rise in interest rate is typically associated with declining asset prices, which inter alia, shrink the value of borrowers’ collateral.
in our financial model, the monetary policy rate enters as an exogenous variable7. Thus, the monetary policy actions affect only financial variables, which in turn, affect the macroeconomic variables. We have assumed a permanent increase in the policy rate by 0.5 pp for eight quarters.
6 current level of endogenous variables, determined according to the assessed equations and the connections provided in the model.7 The behavior of core interest rate is determined by MeaM sample, according to a rule for the monetary policy.
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The results for the main variables are presented in chart 9. based on the model transmission channel, we figure out that a permanent increase in the policy rate increases the lending interest rate that is reflected in higher npl ratios for households and for the business sector. higher levels of npl increase the level of banks’ loan loss provisions, leading to lower net interest income and consequently, lower capital and capital adequacy ratio. increase in lending interest rate causes deleveraging for households and the business sector that is reflected in a higher level of credit cost and credit cost ratio. The credit cost ratio influences the risk-weighted assets and the bank capital adequacy ratio.
The final impact on bank capital adequacy ratio depends on the size of monetary shock, which influences the bank capital and risk-weighted assets.
Chart 9: Results of the first scenario(increase of key interest rate by 0.5 pp
-0,1
0,1
0,3
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1 2 3 4 5 6 7 8
pp
Lending interest rate
Lending Volume
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Credit cost ratio
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%
Total loan
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-0,101 2 3 4 5 6 7 8
%
Credit Risk Asset
-0,5-0,4-0,3-0,2-0,1
00,1
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Capital adequacy ratio
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NPl ratio
quarters quarters
quartersquarters
quarters quarters
Corporates Household
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in this version of the model, the feedback effect from the financial sector to the real sector of the economy passes through the lending volume. Therefore, the nominal change of Gdp is a function of the lag value of nominal Gdp and the changes of the lending volume. so the monetary policy tightening increases the lending interest rate, which decreases the lending volumes and affects the economic performance through a lower level of Gdp growth. due to a tightening in monetary policy, the nominal Gdp growth shrinks in the first two year with around 0.03 pp.
2. doWnWard siMulaTion on noMinal Gdp
in the second version of the simulations, we assume a negative shock of 1% to nominal Gdp over two years. its impact is present on credit cost, lending volume, non-performing loans and capital adequacy ratio, as shown in chart 9. according to the chart, a slower economic growth has a major impact on household and corporate lending volume, which decreases over the following 8 quarters of the shock. as a result of the assumed lower Gdp growth accompanied by lower credit expansion, the credit cost ratio increases by up to 4 percentage points and the gap of the lending volumes expands, while the lending interest rates increase by 0.4 percentage points at the end of 8 quarters.
Chart 9/a: Results of the first scenario(increase of key interest rate by 0.5pp)
-0,05
-0,04
-0,03
-0,02
-0,01
0
1 2 3 4 5 6 7 8
pp
Nominal GDP Growth
quarters
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The non-performing loans deteriorate as the economic activity slows down and the lending interest rates increase. as a result, the risk-weighted assets also decrease due to higher credit cost ratio and lower lending volume over the 8 quarters. This development is reflected in the capital adequacy ratio, which first increases by 0.14 percentage points until the third quarter after the shock, and then it slowly declines below the baseline over the next 5 quarters. The latest progress reflects the strong impact that lower economic growth has on net interest income, which offsets the impact of an increase in the lending interest rates.
quarters
quarters
quarters
quarters
quarters
quarters
Chart 10: Results of the second scenario(Decrease of nominal GDP by 1%)
-2
-1,5
-1
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0
0,5
1 2 3 4 5 6 7 8
pp
Capital Adequancy Ratio
-0,2
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1 2 3 4 5 6 7 8
pp
Credit Cost Ratio
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pp
Lending Interest Rate
-0,50
0,51
1,52
2,53
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Total NPL ratio
-2,5
-2
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01 2 3 4 5 6 7 8
%
Credit Risk Asset
-10-9-8-7-6-5-4-3-2-10
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%
Total lending volume
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quartersquarters
quartersquarters
Chart 11: Result of the third scenario (Exchange rate depreciation)
0
0,1
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pp
Lending Interest Rate
0
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Credit Cost Ratio
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NPl ratio
Corporates Household Total
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pp
Capital adequacy ratio
3. exchanGe raTe depreciaTion
The third version of the shock assumes a 10% depreciation of the nominal exchange rate over two years (chart 10). The first impact is on non-performing loans ratio because the exchange rate depreciation has a strong impact on the debt serving capacity for unhedged borrowers. The impact is stronger for businesses, as the npl ratio deteriorates by up to 6 pp at the end of the fourth quarter of the shock, then it slows down to 0.8 pp by the end of the second year. The impact of the exchange rate depreciation is lower on the npl ratio for households, as it increases by 1pp at the end of the first year, slowing down to 0.4pp by the end of the second year. as the non-performing loans deteriorate, so does the credit cost ratio, by up to 2.5 pp at the end of the first year and it slows down to 0.3 pp at the end of the second. This development incorporates also the impact of the higher lending interest rate. The latter development is due to a higher lending gap volume because lending decreases as its quality deteriorates
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and the capital adequacy gap also expands. This is in line with the lower capital adequacy ratio due to higher provisions following the increase in non-performing loans. The impact of the shock on Gdp growth in the first year is not considerable.
4. banK capiTal shocK
one of the first authors who highlight the importance of bank-capital channel in the context of the monetary policy transmission mechanism was Van den heuvel (2007a). The main approach is that shocks to aggregate demand and supply, as well as the real-estate market conditions may influence loan losses (or loan values) and affect the level of bank capital, if these losses are not buffered by profit. These adverse (or favourable) shocks to the balance sheets of bank can require contraction (or expansions) in credit, which can amplify the effect of such shock on output and inflation. here we present a negative bank capital shock and its impact on the main variables. in our model, asset price is proxies by house price entered as exogenous variables that influence only the household lending volume. Thus, deterioration of bank capital decreases bank lending through increasing lending interest rate and tightening loan standards.
chart 12 shows some of the main results caused by 10 % decrease in the capital of all banks in the system, by reflecting also a fall of 10% in the system’s total capital. as a consequence, a sudden deterioration of bank capital causes a decline in banks’ capital-asset ratios by around 3 pp. however, the capital adequacy ratio gap is lower, but still positive. bank capital-asset ratio endogenously affects bank lending volume by 1 % in the second year. decreases in the lending volume directly affect the nominal Gdp by 0.2 % at the end of the second year. The impact on Gpd seems lower. in our opinion, this indicates that banks in albania are well capitalized and have absorbed better this adverse shock.
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Bank Capital
quartersquarters
quartersquarters
quarters
Lending Volume
Capital Adequancy Ratio Nominal GDP growth
Chart 12: Results of the fourth scenario (Negative bank capital shock)
- 3.5
-3
- 2.5
-2
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-1
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0
1 2 3 4 5 6 7 8
pp
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- 14- 12- 10
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%
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%
Corporate Houshold Total lending
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Lending interest rate
-40-
5. policy applicaTions and conclusions
The main goal of albanian financial model is to provide a quantitative analytical framework, not only for credit risk analysis, but also for other potential risks that come from macro and financial sector, and to provide a complete tool to analyse the banking system. in more details, some policy applications of our model are given below.
producing aggregate (for banking system) and individual (for banks in particular) simulations for the main financial variables takes into account the relationship between the financial and real sector. This type of model is very useful for telling story and for policy analysis, and can be used to analyse separately different parts of bank balance sheet, such as net interest income, credit risk, bank asset, capitalization level etc. analysing credit risks under stress scenarios. This framework is more appropriate than the actual tool because the main financial variables are determined endogenously and simultaneously, so the final results in credit risk and other final variables are obtained from our transmission mechanism. also, this model includes a wide range of channels that come from bank balance sheets and also the feedback effect by offering a sustainable and a more appropriate tool to testing the stability of the banking system.
producing forecast for the main financial variables. in the future, after testing the performance of our model, our aim is to produce out of sample the forecasts for all behavioural variables that we have estimated. This is important for the financial and macroeconomic stability to observe the future path of main financial variable in order to manage and prevent different types of shocks.
as already stated, this is the first version of the macro-financial model, and as such, more work is needed in terms of calibrating the model and discussing the main simulation results before incorporating it into bank of albania’s decision-making process. First, the model needs to be enhanced with a better detailed presentation of the feedback between the financial system and the real economy. in our
-41-
case, we rely on the relationship between lending and economic growth, which can be further determined in terms of consumption, investments etc. second, here we present the results in aggregate terms, while a more detailed representation in terms of individual banks will be helpful for the internal discussion. Third, the model should be tested in terms of out of sample forecasting properties for a sufficient time, in order to evaluate its performance.
-42-
reFerences
Aikman, D, Allessandri, P et al. “Funding liquidity risk in a quantitative model of systemic stability”, Bank of England, WP 372.
Athanasoglou, B. P., Delis, M. D. and Staikouras, C. K., 2006. Determinants of bank profitability in the South and Eastern European region, Bank of Greece Working Paper No. 47, September.
Atsushi I; Koichiro, K; Yoshiyuki K; Kentaro, N; and Yuki, T “Introduction to the Financial Macro-Econometric Model”, Bank of Japan, January 2012.
Barrell, R and Gottschalk, S. «The Impacts of Capital Adequacy Requirements on Emerging Markets,» NIESR Discussion Papers 269, National Institute of Economic and Social Research, 2006.
Bernanke, B., Gertler, M., and Gilchrist, S., “The financial accelerator in a quantitative business cycle framework”, in. Taylor, J. B., Woodford, M. (Eds.), Handbook of Macroeconomics. Elsevier, pp. 1341-1393
Bernanke, B.S and Gertler, “Inside the Black Box; The credit channel of monetary policy transmission”, Journal of economic perspectives, Vol.9, No.4, 1995, pp.27-48.
Brock, Philip and Liliana Rojas-Suárez. 2000. “Interest rate spreads in Latin America: Facts, Theories and Policy Recommendations”. Inter-American Development Bank, 2000.
Calza, A., Gartner C. and Sousa J. (2001), “Modelling the Demand for Loans to the Private Sector in the Euro Area”, ECB, Working Paper, No. 55.
Central and Eastern Europe, Oestereichische Nationalbank, Financial Stability Report No. 14.
Claeys, S. and Vander Vennet, R., 2008, ” Determinant of Bank Interest Margins in Central and Eastern Europe: A comparison with the West”, Economic System, Journal, Volume 32, Issues 2, pg 197-216,
Dushku, E, and Vika, I “VAR analysis of Macroeconomic effects on Bank Loan Quality in Albania” Bank of Albania, 2011.
-43-
Espinoza, R and Prasad, A, “Nonperforming loans in the GCC Banking System and their Macroeconomic Effect”IMf WP/10/224, 2010.
Focarelli , D. and P. Rossi (1998) ‘‘ La domanda di finanziamente bancari in Italia e nelle diverse aree del Paese (1984-1996)”, Banka D`Italia, Temi di discussione, Numero 333.
Gambacorta, L and Mistrulli, P; “Bank capital and lending behavior: Empirical evidence for Italy”, Research Department, Bank of Italy, 2003
Goodhart C., Hofmann B., (2008), “House prices, money, credit and the macroeconomy” ECB Working Paper Series no 888.
Gunnar B, Kjersti-Gro L and Dimitrios P. Tsomocos, (2006) “Evaluation of macroeconomic models for financial stability analysis “, Working Paper, Norges Bank.
Haldane, A., Hoggarth, G and Saporta, V (2001). «Assessing Financial Stability, Efficiency and Structure at the Bank of England», published in Marrying the MacroPrudential Dimensions of Financial Stability, BIS Papers.
Kakes, J. (2000) “Identifying the Mechanism: Is There a Bank Lending Channel of Monetary Transmission in the Netherlands?”, Applied Economics, Vol. 7, pp. 63-7.
Kalluci, I and Kodra, O, “Macroeconomic Determinants of Credit Risk: The case of Albania”, Bank of Albania, 2010.
Kashyap, A., J. Stein and D.Wilcox (1993) Monetary policy and credit conditions: evidence from the composition of external finance,American Economic Review, 83, pp. 8-98.
Kasman, A., Tunc, G., Vardar, G and Okan, B., 2010. Consolidation and commercial bank net interest margins: Evidence from the old European Union members and candidate countries, Economic Modeling 27, 648-655.
Kiss G., Nagy M, Vonnák B., “Credit Growth in Central and Eastern Europe: Trend, Cycle or Boom?”, National Bank of Hungary, 2006.
Kota, V and Dushku, E., “A Macro Econometric Albania Model-A follow up”, Bank of Albania 2007.
-44-
Le Lesle, V and Avramova, S; “Revisiting Risk-Weighted Asset”, IMF WP 2012”.
Louzis, D.P, Vouldis, A.T and Metaxas, V.L “Macroeconomic and bank –specific determinants of non-performing loans in Greece; A comparative study of mortgage, business and consumer loan portfolios”, Bank of Greece, 2010.
Meier A and Müller G, (2005). «Fleshing out the monetary transmission mechanism - output composition and the role of financial frictions,» Working Paper Series 500, European Central Bank.
Nieto F., A. Del Roi and T. Sastre, 2001, “La evolución reciente del crédito al sector privado en Spain”, Central bank of Spain.
Saad, W. and El-Moussawi, C., 2010. The Determinants of Net Interest Margins of Commercial Banks in Lebanon, Journal of Money, Investment and Banking, Issue 23.
Schwaiger, M.S. and Liebig, D., 2009. Determinants of the Interest Rate Margins in Central and Eastern Europe, Oestereichische Nationalbank, Financial Stability Report No. 14.
Shijaku, H and Ceca, K “A model for the credit risk in Albania using banks’ panel data”, Bank of Albania, 2010.
Stephen G Cecchetti, Piti Disyatat and Marion Kohler, (2009), “Integrating financial stability: new models for a new challenge”.
Teglio, A., Raberto, M., and Cincotti, S. (2011). Do capital requirements affect long-run output trends? In: Emergent results of artificial economics, vol. 652, pp. 41-52, Springer Berlin Heidelberg transmission in the Netherlands, Applied Economics Letters, 7, pp. 63-67.
-45-
appendix
idenTiTies eQuaTions
1. Equation “Total lending volume”Lendv = Lendv_C + Lendv_H + Lendv_Jor
2. Equation “Capital Adequacy Ratio”Car = Cap / Riska
3. Equation “Bank Liabilities”Bl = Bdep + Boliab
4. Equation “Bank Assets”Bas = Bl + Bnas + Bnas_Oth
5. Equation “Bank other Assets”Boas = Bas - Lendv - Bsec
6. Equation “Capital adequacy Ratio Gap”Car_gapv = Car - 0.12
7. Equation “Lending Gap”Lendv_gap = (lendv / lendv_hp - 1)
8. Equation “Other net interest income”Netinc_oth = inc_oth - exp_oth
9. Equation “Total gross interest income activity”Bruto_inc_act = netintinc + netinc_oth - exp_prov
10. Equation “Total net interest income activity”Net_Inc_Act = Bruto_Inc_Act - Exp_Act
11. Equation “Total net interest income“Net_Inc = Net_Inc_Act + Net_Exd_Inc - Inc_Tax - Oth_Tax
12. Equation “Loan loss provisions”Llp=NPLT*0.5
13. Equation ”Bank capital”Cap=cap (-1) + Net_inc
14. Feedback equationDlog(YN) =0.53dlog(yn(-1))+0.042Dlog(lendv(-1))
-46-
Tabl
e 2
lisT
oF
Vari
able
s
nr.
cod
eVa
riabl
eVa
riabl
e de
scrip
tion
uni
tso
urce
1ba
sM
oney
sup
ply
rate
a
ny it
em o
f ec
onom
ic v
alue
ow
ned
or c
ontro
lled
by a
ban
k,
whi
ch c
ould
be
conv
erte
d ea
sily
to c
ash
mill
al
boa
2bi
lls_
bon
d_G
obl
igat
ions
and
trea
sury
bi
lls h
eld
by b
anks
%bo
a
3bl
bank
liab
ilitie
s
bank
lia
bilit
ies
reco
rded
on
the
bala
nce
shee
t of
the
ban
k (ri
ght s
ide)
and
are
vita
l to
the
bank
. M
ain
item
s of
liab
ilitie
s ar
e de
posi
ts,
equi
ty,
treas
ury
and
inte
rban
k lia
bilit
ies
from
in
vest
men
ts in
sec
uriti
es a
nd o
ther
obl
igat
ions
mill
al
boa
4bn
aba
nk n
et a
sset
sTh
e va
lue
of b
ank’
s to
tal
asse
ts l
ess
its t
otal
lia
bilit
ies
and
inta
ngib
le
The
valu
e of
ba
nk’s
to
tal
asse
ts
less
its
to
tal
liabi
litie
s an
d in
tang
ible
ass
ets
mill
all
boa
5bo
as
bank
oth
er a
sset
oth
er a
sset
s of
the
bank
con
sist
in a
ny o
ther
ass
et e
xclu
ding
le
ndin
g ac
tivity
and
sec
uriti
es in
vest
men
ts.
mill
all
boa
6bo
nd
obl
igat
ion
held
by
bank
The
issu
er o
wns
to th
e bo
nd h
olde
rs a
deb
t and
dep
endi
ng o
n ob
ligat
ion
term
s w
ill p
ay to
them
an
inte
rest
and
or
pay
back
th
e pr
inci
pal b
y a
spec
ific
date
, cal
led
mat
urity
. m
ill a
llbo
a
7bo
no
Trea
sury
bill
s he
ld b
y ba
nkse
curit
ies
held
by
bank
s (a
s in
vest
men
ts),
issu
ed b
y th
e ce
ntra
l go
vern
men
t at z
ero
risk,
hav
ing
a m
atur
ity o
f one
yea
r or
less
mill
all
boa
8br
uTo
_in
c_a
cT
Gro
ss
inco
me
from
co
re
busi
ness
pre-
tax
gros
s in
com
e re
aliz
ed fr
om th
e ba
nk’s
cor
e bu
sine
ss
mill
all
boa
9bs
ecba
nk’s
sec
uriti
esba
nk’s
sec
urity
inv
estm
ents
as
asse
t ite
m a
nd i
nclu
de f
ix
inco
me
secu
ritie
s, c
hang
eabl
e, p
urch
ased
/sol
d ac
cord
ingl
y to
the
repu
rcha
sing
agr
eem
ents
m
ill a
llbo
a
10c
ar
cap
ital a
dequ
acy
ratio
it is
the
ratio
bet
wee
n re
gula
tory
cap
ital a
nd ri
sk-w
eigh
ted
asse
ts
in p
erce
ntag
e. T
his
ratio
sho
uld
not b
e be
low
the
levl
el o
f 12%
.m
ill a
llbo
a
11c
ap
cap
ital
it is
the
indi
cato
r enc
ompa
ssin
g th
e co
re c
apita
l and
add
ition
al
capi
tal a
s w
ell a
s a
set
of o
ther
cal
cula
tions
of
the
bala
nce-
shee
t ite
ms.
it is
use
d to
mea
sure
the
capi
tal a
dequ
acy.
m
ill a
llbo
a
12c
ar_
Ga
pc
apita
l ade
quac
y ra
tio g
apit
show
s th
e ga
p be
twee
n ca
pita
l ade
quac
y ra
tio a
t a c
erta
in
perio
d w
ith th
e su
perv
isor
y re
gula
tory
rat
e of
12%
.%
boa
13c
co
sTc
redi
t cos
tc
redi
t co
st is
mea
sure
d as
a s
um o
f lo
an lo
ss p
rovi
sion
and
w
rite
off m
inus
rec
over
ed w
rite
off .
mill
all
boa
-47-
14c
co
sT_G
cre
dit c
ost g
row
thye
ar o
n ye
ar c
hang
e of
cre
dit c
ost
%bo
a, o
ur c
alcu
latio
ns15
cc
osT
raTi
oc
redi
t cos
t rat
ioc
redi
t cos
t as
ratio
n of
tota
l cre
dit
%bo
a
16c
pic
onsu
mer
pric
e in
dex
a c
onsu
mer
pric
e in
dex
(cpi
) m
easu
res
chan
ges
in t
he p
rice
leve
l of
a m
arke
t ba
sket
of
cons
umer
goo
ds a
nd s
ervi
ces
purc
hase
d by
hou
seho
lds.
in
de
x,
2007
=10
0a
lban
ian
inst
itute
of
st
atis
tic (i
nsta
t)
17G
_rep
o
Gro
wth
rat
e of
pol
icy
rate
Gro
wth
rat
e of
pol
icy
rate
(rep
o)%
18G
dp_
Gre
al G
dp
grow
th
it is
the
rate
of c
hang
e th
at a
cou
ntry
’ s G
dp
expe
rienc
esfro
m
one
year
to a
n ot
her.
%in
stat
, our
exp
ecta
tion
for
Gd
p gr
owth
19h
pih
ouse
pric
esh
ouse
pric
e in
dex
is a
pro
xy fo
r re
al e
stat
e m
arke
tin
dex
inst
at20
hpi
_Gre
al h
ouse
pric
e h
ouse
pric
e in
dex
defla
ted
with
infla
tion
%
21in
c_T
ax
inco
me
tax
it re
pres
ents
inco
me
stem
min
g fro
m ta
x.m
ill a
llbo
a22
len
d_i
rle
ndin
g in
tere
at r
ate
ave
rage
con
tract
inte
rest
rat
es o
n lo
ans
%bo
a23
len
d_i
r_c
pire
al le
ndin
g in
tere
st r
ate
nom
inal
lend
ing
inte
rest
rat
e m
inus
infla
tion
%bo
a, o
ur c
alcu
latio
n24
len
dV
lend
ing
volu
me
Tota
l loa
n vo
lum
e fro
m b
anki
ng s
ecto
rm
ill a
llbo
a25
len
dV_
cc
orpo
rate
lend
ing
volu
me
lend
ing
volu
me
to c
orpo
rate
sec
tor
mill
all
boa
26le
nd
V_G
lend
ing
volu
me
grow
th
year
on
year
cha
nge
of le
ndin
g vo
lum
e%
boa,
our
cal
cula
tions
27le
nd
V_G
ap
corp
orat
e le
ndin
g vo
lum
e to
cor
pora
te s
ecto
rco
rpor
ate
lend
ing
volu
me/
pote
ntia
l cor
pora
te le
ndin
g vo
lum
e *1
00-1
00m
ill a
llbo
a
28le
nd
V_h
hou
seho
ld le
ndin
g vo
lum
e To
tal l
oan
volu
me
to h
ouse
hold
sec
tor
mill
all
boa
29ll
plo
ans
loss
pro
visi
on
an e
xpen
se s
et a
side
as a
res
erve
to
be u
sed
for
bad
loan
s (in
the
even
t the
cus
tom
er fa
ils to
mee
t the
res
pect
ive
finan
cial
ob
ligat
ions
or w
hen
the
term
s of a
loan
hav
e to
be
rene
gotia
ted)
. m
ill a
llbo
a
30n
eer
nom
inal
effe
ctiv
e ex
chan
ge
rate
a n
omin
al e
ffect
ive
exch
ange
rate
is th
e ex
chan
ge ra
te o
f the
do
mes
tic c
urre
ncy
vis-
a-vi
s ot
her
curr
enci
es..
bo
a
31n
eT_e
xd_i
nc
net
ext
radi
onar
y in
com
eit
repr
esen
ts
the
spre
ad
betw
een
gain
s de
rivin
g fro
m
extra
ordi
nary
act
iviti
es to
ext
raor
dina
ry e
xpen
ses.
m
ill a
llbo
a
32n
eT_i
nc
net
inco
me
afte
r ta
xes
net_
inc_
act +
net
_exd
_inc
- in
c_ta
x -
oth_
tax
mill
all
boa
33n
eT_i
nc
_ac
Tn
et in
tere
st in
com
ein
tere
st in
com
e-in
tere
st e
xpen
ese
mill
all
boa
34n
eTin
c_o
Thne
t inc
ome
from
oth
er
activ
ities
oth
er a
ctiv
ities
inco
me-
oth
er a
ctiv
ities
exp
ense
m
ill a
llbo
a
35n
eTin
Tin
cn
et in
tere
st in
com
eit
repr
esen
t th
e sp
read
be
twee
n to
tal
inte
rest
in
com
e to
in
tere
st e
xpen
se
mill
all
boa
-48-
36n
plc
non
-per
form
ing
loan
s of
co
rpor
ate
sect
or
mill
all
boa
37n
plc
_ra
Tio
non
perfo
rmin
g lo
ans
of
corp
orat
e se
ctor
ratio
of n
on-p
erfo
rmin
g lo
ans
of c
orpo
rate
sec
tor
to le
ndin
g vo
lum
e of
cor
pora
te s
ecto
r.%
boa
38n
plh
_ra
Tio
non
perfo
rmin
g lo
ans
of
hous
ehol
d se
ctor
ratio
of n
on-p
erfo
rmin
g lo
ans
of h
ouse
hold
sec
tor
to le
ndin
g vo
lum
e of
hou
seho
ld s
ecto
r.%
boa
39n
plh
non
perfo
rmin
g lo
ans
of
hous
ehol
d se
ctor
m
ill a
llbo
a
40n
plT
non
perfo
rmin
g lo
ans
Tota
l cr
edit,
prin
cipa
l an
d in
tere
st,
clas
sifie
d in
to t
he t
hree
la
st c
ateg
orie
s of
loa
n cl
assi
ficat
ion
( su
b-st
anda
rd l
oans
, do
ubtfu
l loa
ns a
nd lo
ss lo
ans)
.m
ill a
llbo
a
41o
Th_T
ax
oth
er ta
xes
item
incl
uded
in th
e in
com
e (p
rofit
and
loss
) sta
tem
ent o
f the
ban
k m
ill a
llbo
a
42re
pore
po r
ate
(repo
)
it is
the
rat
e th
e ba
nk a
pplie
s to
rep
urch
ase
gove
rnm
ent
inst
rum
ents
from
com
mer
cial
ban
ks a
nd w
hich
is s
et o
ut b
y th
e le
vel o
f mon
ey s
uppl
y th
at s
houl
d be
mai
ntai
ned
in e
cono
my
in th
e fra
mew
ork
of m
onet
ary
polic
y im
plem
enta
tion.
%bo
a
43ri
sKa
cre
dit r
isk
asse
t
in
com
plia
nce
with
ca
pita
l re
quire
men
ts,
bank
s sh
ould
ge
nera
te a
cap
ital r
epor
t bas
ed o
n th
eir a
sset
s, w
eigh
ted
by a
ris
k fa
ctor
atta
ched
to
them
. a
diff
eren
t ris
k fa
ctor
is a
pplie
d on
diff
eren
t ass
ets
cate
gorie
s.
mill
all
boa
44rW
oF
reco
verie
s of
writ
e of
fTh
is it
em r
epre
sent
s an
ext
raor
dina
ry in
com
e m
ill a
llbo
a
45sh
eQsh
areh
olde
rs’ e
quity
c
apita
l ca
lcul
ated
for
ban
king
sup
ervi
sion
pur
pose
s. T
here
ar
e in
clud
ed v
ario
us c
apita
l an
d re
serv
es c
ateg
orie
s, a
nd
othe
r el
emen
ts s
et o
ut b
y th
e ba
nk o
f alb
ania
by
by-la
ws.
mill
all
boa
46u
nu
nem
ploy
men
t rat
eTh
e pe
rcen
tage
of t
otal
forc
e ab
le to
wor
k th
at is
une
mpl
oyed
, bu
t tha
t act
ivel
y re
ques
ts to
be
empl
oyed
and
is re
ady
to w
ork.
.%
inst
at
47W
oF
Writ
e of
fit
impl
ies
cred
it w
rite
off b
alan
ce-s
heet
. any
inco
me
arisi
ng fr
om
thes
e w
ritte
n-of
f loa
ns is
con
sider
ed a
s ex
traor
dina
ry in
com
e.m
ill a
llbo
a
48y_
rea
l re
al G
dp
an i
nfla
tion-
adju
sted
mea
sure
tha
t re
flect
s th
e va
lue
of a
ll go
ods
and
serv
ices
pro
duce
d in
a g
iven
yea
r, e
xpre
ssed
in
base
-yea
r pr
ices
. m
ill a
llin
stat
, our
ca
lcul
atio
ns
49yn
n
omin
al G
dp
a g
ross
dom
estic
pro
duct
(G
dp)
fig
ure
that
has
not
bee
n ad
just
ed fo
r in
flatio
n.m
ill a
llin
stat
-49-
-50-
-51-
-52-
cip Katalogimi në botim bK Tiranë
elona dushku, Vasilika KotaThe Financial Model for albania:a panel data approach- // dushku elona, Kota Vasilika - Tiranë:banka e shqipërisë, 2013
-52 f; 15.3 x 23 cm.
bibliogr.isbn: 978-99956-42-95-7.
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