D O C U M E N T O D E T R A B A J O

Instituto de EconomíaTESIS d

e MA

GÍSTER

I N S T I T U T O D E E C O N O M Í A

w w w . e c o n o m i a . p u c . c l

The Pass-Through of Interest Rates:The Case of the Dominican Republic

Rafael Rivas.

2011

1

PONTIFICIA UNIVERSIDAD CATOLICA DE CHILE I N S T I T U T O D E E C O N O M I A MAGISTER EN ECONOMIA

TESIS DE GRADO

MAGISTER EN ECONOMIA

Rivas, Cueto, Rafael Andrés

Diciembre, 2011

2

PONTIFICIA UNIVERSIDAD CATOLICA DE CHILE I N S T I T U T O D E E C O N O M I A MAGISTER EN ECONOMIA

The Pass-Through of Interest Rates: The Case of the Dominican Republic

Rafael Andrés Rivas Cueto

Comisión

Rodrigo Fuentes

Miguel Fuentes

Juan Urquiza

Santiago, Diciembre de 2011

3

Contents

Abstract ............................................................................................................................ 4

1 Introduction .................................................................................................................. 5

2 Studies of the Pass-Through ......................................................................................... 7

3 Monetary Policy in the Dominican Republic ............................................................... 9

4 The Behavior of the Banking Firm ..............................................................................11

4.1 Short-Run Behavior of Banks in the Loan Market ....................................................13

4.2 Short-Run Behavior of Banks in the Deposit Market ...............................................14

5 Data and Methodology ................................................................................................. 15

5.1 Symmetric Error Correction Model .........................................................................15

5.2 Asymmetric Error Correction Model ........................................................................17

6 Results ..........................................................................................................................18

7 Concluding Remarks ...................................................................................................24

References .......................................................................................................................26

Appendix A: Data ...........................................................................................................30

Appendix B: Unit Root and Cointegration Tests ...........................................................36

Appendix C: Freixas and Rochet (1998) Model of Bank Behavior ...............................40

4

The Pass-Through of Interest Rates: The Case of the Dominican Republic

Rafael Andrés Rivas Cueto1

Pontificia Universidad Católica de Chile

December, 2011

(this version: March 2012)

Abstract

We examine the pass-through of changes in the money market or interbank interest rate to

lending interest rates and deposit interest rates in the Dominican Republic within both a

single equation symmetric error correction framework and single equation asymmetric error

correction framework. Given the banking crisis and major institutional changes that

occurred in the Dominican Republic during the time period 2003-2004, we divide our

analysis into a pre-crisis and institutional changes period, and a post-crisis and institutional

changes period. We find evidence in support of an increase in efficiency of the interest rate

channel of monetary policy transmission. For the post-crisis period, we find a long-run

linear relationship between banks’ interest rates and the interbank interest rate characterized

by a complete pass-through. Nonetheless, we find the pass-through to be incomplete in the

short-run. Combining a marginal cost pricing model and a model of imperfect information

in the banking sector, our research paper is able to explain these findings.

1 Email address: rarivas@uc.cl. I wish to thank my thesis advisors Professors Rodrigo Fuentes, Miguel

Fuentes, and Juan Urquiza for their helpful comments and guidance; José Manuel Mota, Juan Carlos López,

Marianne Rodríguez, and Omar Rodríguez for comments. Finally, I wish to thank my Dominican family, my

Chilean family, and Carmen Garcés for their continuous support. All remaining errors are my responsibility.

5

1. Introduction

The purpose of this research is to empirically investigate the pass-through of the money

market or interbank interest rate to lending and deposit rates in the Dominican Republic.

Currently, monetary policy in the Dominican Republic is conducted in such a way that

movements in the policy rate aim to have an impact on market interest rates, such as bank

lending rates and bank deposit rates, which in turn affect consumption and investment

opportunity costs in the economy. A quicker pass-through of interest rates strengthens

monetary policy transmission.

Specific questions we address are the following:

i. Do lending and deposit rates respond sluggishly to changes in the interbank interest

rate?

ii. Do banks adjust asymmetrically to changes in the money market interest rate? This

is, is the speed of adjustment faster (slower) when the money market rate is

increasing than when it is decreasing for lending (deposit) interest rates?

These are worthwhile questions because from 2003 to 2005 a mix of institutional changes

along with a banking crisis affected the way monetary policy was conducted in the

Dominican Republic. With the new Monetary and Financial Law of December 2002, new

permanent liquidity facilities were created at the Central Bank, such as the Overnight

Deposits Window and the Lombard Window, which entered the money market during the

year 2004. These facilities formally created a corridor for the interbank market interest rate,

with the rate on Overnight Deposits at the Central Bank serving as the floor of the corridor,

and the Lombard Window rate serving as the cap. The design of this corridor is such that

banks prefer to borrow short-term funds from other banks (other than the Central Bank) at

the prevailing interbank interest rate rather than to pay a relatively high interest rate at the

6

Lombard Window, and prefer to lend excess liquidity at the interbank rate rather than to

receive a relatively lower rate at the Overnight Deposits Window. Through this corridor the

Central Bank signals the market about its monetary policy intentions, thus influencing

expected inflation beliefs in the economy, which is of vital importance for the effectiveness

of monetary policy. Furthermore, in developing economies such as the Dominican

economy, the effectiveness of the interest rate channel of monetary policy transmission is

fundamental, given that a significant portion of construction, industrial, retail and other

services investments are financed by commercial banks loans.

Using aggregate monthly interest rate data divided into two different samples (before and

after institutional changes), this research will attempt to achieve its purpose by employing

single equation error correction models in order to capture the long-run relationship and

short-run dynamics of the pass-through of interest rates. In addition, a small modification to

these single equation error correction models will allow us to test for the presence of

asymmetric adjustment in the bank lending and deposit rates in response to movements in

the money market or interbank interest rate.

Our main finding suggests the existence of an underdeveloped and highly inefficient

interest rate channel of the monetary policy transmission mechanism during the pre-crisis

and institutional changes period. This is not the case for the 2005-2011time period; during

this period, we are able to establish long-run linear relationships between banks’ interest

rates and the interbank interest rate characterized by a complete pass-through of changes in

the interbank rate to bank rates. Nonetheless, we find this pass-through to be incomplete in

the short-run.

The paper proceeds as follows: Section 2 offers a short literature review; Section 3 provides

a brief description on the evolution of monetary policy in the Dominican Republic; Section

4 contains a model of the behavior of banks; Section 5 describes the methodology; Section

6 presents the estimation results; afterwards, Section 7 summarizes and concludes the work.

7

2. Studies of the Pass-Through

The sluggishness in the pass-through of market interest rates to bank rates can be explained

by a marginal cost pricing model (Winker, 1999), where the marginal costs of banks when

making decisions about their lending and deposit rates is determined by the money market

rate; only in a world with perfectly competitive markets and full information would a

change in the market rate lead to an immediate adjustment of the lending and deposit rates.

Stiglitz and Weiss (1981) provided a theoretical explanation of the sluggishness of interest

rates based on asymmetric information and its consequences on the pricing behavior of

banks. In their view, banks may not be able to increase lending rates in the presence of

higher marginal costs (this is, higher money market rate) given that this would attract

riskier investors, and therefore, increase the risk of the bank’s loan portfolio.

There is abundant empirical literature that studies the degree and speed of adjustment of

bank interest rates to changes in money market interest rates. In general, the literature finds

that bank interest rates respond sluggishly to changes in policy or money market rates, this

is, a change in the policy or money market interest rate is not transmitted immediately to

bank rates. Several studies try to explain this phenomenon by including characteristics of

the banking structure, such as competition and concentration measures, into the analysis as

exogenous variables. For example, in their cross-country analysis, Cottarelli and Kourelis

(1994) identified that the absence of constraints on bank competition (particularly, barriers

to entry) is one of the structural features singled out as being particularly relevant in

increasing lending rate flexibility. Berstein and Fuentes (2003) found that there is some

rigidity for deposit interest rates in Chile, and that it is closely related to market

concentration in the banking industry.

Studies in the empirical literature perform either a cross-country analysis of the pass-

through of interest rates or a time series analysis of the pass-through focusing just on one

country; our study performs the latter. Breding et al. (2002) performed a time series

8

analysis of the pass-through for the case of Ireland; they employed single equation error

correction models to measure the long-run and short-run pass-through of movements in a

money market interest rate to different lending rates varying depending on borrower type

and maturity of the loan. Their results showed that the long-term coefficient ranges from

0.54 to 0.92, while the speed of adjustment coefficient ranges from 0.06 to 0.56. In a

similar fashion, after establishing a long-run linear relationship between a lending (and a

deposit) interest rate and a money market interest rate, Winker (1999) employed an error

correction model to combine the identified long-run relationship with the short-run

adjustment process using monthly interest rate data from Germany. Winker’s results show

that for both the lending rate and the deposit rate, the pass-through is complete in the long-

run (long-term coefficients tend to 1), but that in the short-term only 14.5 percent of a

change in the money market rate is passed through the lending rate within a one-month

period, while for the deposit rate the short-run pass-through is of about 37.3 percent.

The literature identifies concentration in the banking industry as one of the main causes of

asymmetric adjustments in bank deposit and lending rates, (Berger and Hannan, 1989;

Hannan and Berger, 1991). Deposit rates seem to be more rigid upwards while lending rates

seem to be more rigid downwards due to the fear of banks of disrupting collusive

arrangements. Espinosa-Vega and Rebucci (2003) allowed for pass-through asymmetries in

the case of Chile by introducing a dummy variable in their error correction model but found

little evidence in favor of asymmetries in the adjustment of rates. Similarly, Duran-Viquez

and Esquivel-Monge (2008) employed a non-linear asymmetric vector error correction

model, but found no evidence in favor of asymmetries in the reaction of retail rates to

movements of the policy rate in Costa Rica. Enders and Siklos (2001) estimated their M-

TAR model to analyze the term structure of U.S. interest rates using monthly values of the

federal funds rate and the 10-year yield on federal government securities. They found that

discrepancies from long-term equilibrium resulting from increases in the federal funds rate

display a large amount of persistency.

9

There are no previous studies of the pass-through of interest rates in the Dominican

Republic. This will serve as the scope of our work.

3. Monetary Policy in the Dominican Republic.

The purpose of this section is to briefly describe the evolution of monetary policy in the

Dominican Republic from the year 1990, when the fixed official exchange rate regime was

abandoned, to the present time when the Central Bank is moving towards formally adopting

inflation targeting.

During the early 1990s, the Dominican Republic’s monetary and financial sectors

experienced several economic reforms, such as the abandonment of the fixed official

exchange rate regime, liberalization of exchange rates and interest rates, and the

development of the interbank market. According to Sánchez-Fung (2005), during this

period, monetary policymakers responded to an output gap, an inflation gap, and an

exchange rate gap. Furthermore, monetary policy was oriented towards the management of

monetary aggregates, and was implemented via the buying and selling of Central Bank’s

debt instruments. Nonetheless, it should be noted that, since the late 1990s, a transition to a

monetary policy that operates via the interest rate channel of monetary transmission has

taken place.

Regarding the Dominican Republic’s exchange rate regime as of June of the year 1999,

Hausmann et al. (2000) classified the regime as a managed float with no preannounced path

for exchange rate. Using a sample of 30 countries, the authors focused on three aspects of

exchange rate management: the stock of reserves with which a country floats, the extent to

which a country uses its reserves to stabilize the exchange rate, and the extent to which a

country uses interest rates to stabilize the exchange rate. According to the indicators

analyzed in Hausmann et al. (2000), almost nine years after the liberalization of exchange

10

rates, the Dominican Republic ranked high as one of the LAC countries closer to a pure

flotation regime. For other LAC countries, much has changed since then, for example

Chile, which at the time of the study was classified under countries with an exchange rate

regime with crawling or horizontal bands, is now well into its second decade of a floating

regime; the Dominican Republic, although gradually moving towards a pure float since

2005 in order to effectively adopt inflation targeting, is still considered to have a managed

float regime with no preannounced path for exchange rate.

Towards the end of 2002, the establishment of the new Monetary and Financial law

allowed for the creation of new policy instruments, such as the Central Bank's notes and

bonds, and permanent liquidity facilities, such as the Overnight Deposits Window and the

Lombard Window. In addition, the new law strengthened the institutional framework for

the conduct of monetary policy by prohibiting the financing of the government by the

Central Bank, and establishing price stability as the main Central Bank’s mandate. In mid-

2003, the Dominican economy suffered a major banking crisis when three major

commercial banks went bust2; the economy shrank for the first time since 1990, inflation

jumped to 42.7 percent, and the Dominican peso/U.S. dollar exchange rate depreciated

going from 17.5 pesos per U.S. dollar to 35 pesos per U.S. dollar. The Central Bank

assumed the role of lender-of-last-resort and flooded the market with liquidity increasing

the monetary base from 39 billion pesos in April 2003 to 78 billion pesos in December of

the same year.

From early 2004 onward, the Dominican Republic’s monetary policy is formally based on

monetary targets, and implemented using the aforementioned instruments and facilities;

that is, taking excess liquidity from commercial banks in the form of short-term deposits

through the Overnight Deposits Window while providing liquidity to commercial banks in

the form of short-term collateralized loans through the Lombard Window so that these

banks could meet the liquidity needs resulting from day - to - day operations. The Central

2 Commercial banks Baninter, Bancredito, and Banco Mercantil.

11

Bank conducts open market operations in order to affect the monetary base looking to have

an impact on its main concern: inflation. As previously mentioned, since the late 1990s, a

transition to a monetary policy that operates via interest rates has taken place. By moving

the overnight deposits rate, the Central Bank looks to manage liquidity in the interbank

market and to signal the economy of its policy intentions so that agents can properly adjust

their inflation expectations.

At the present time, inflation is policy makers’ primary concern; as a consequence,

monetary policy is gradually moving towards formally adopting inflation targeting.3 Since

2006, the Central Bank’s Open Market Operation Committee (COMA, for its acronym in

Spanish) started to formally announce the level of interest rates decided on its meetings on

the Overnight Deposits Window and the Lombard Window.

4. The Behavior of the Banking Firm

Next, following Freixas and Rochet (1998), we study the behavior of commercial banks in

a partial equilibrium context under imperfect competition. As a consequence of the

significant barriers to entry that exists in the banking industry, a model of imperfect

competition seems appropriate to model banks’ behavior.4

We assume there are N banks, all having the following linear cost function:

3 According to Banco Central de la República Dominicana (BCRD) (2010), Inflation targeting will be formally adopted on February 2012. 4 A complete derivation of Freixas and Rochet (1998) model for both the cases of pefect and imperfect

competition is derived in Appendix C.

12

Assuming constant marginal cost of intermediation, ,

, each of the n

banks, taking as given the volume of deposits and loans of other banks, choose the

pair

that solves:

where is a compulsory share of deposits that must be kept as reserves, is the

interest rate on bank loans, is the interest rate on bank deposits, and is the interbank

interest rate. Bank faces the following inverse demand function for loans

and the following inverse supply function of deposits

The resulting first-

order conditions from the above profit maximization imply:

where the elasticity of demand for loans and the elasticity of supply of deposits are as

follows:

.

13

Notice that (2) and (3) can be interpreted as a long-run equilibrium for banks. Moreover,

the long-run pass-through of changes in the interbank rate to bank lending rates will be

larger the smaller the elasticity of demand for loans is. This latter relationship works the

opposite way for the pass-through to bank deposit rates and the elasticity of supply of

deposits. In addition, notice that as the intensity of competition grows (proxied by N, the

number of firms) becomes less responsive to changes in , while

becomes more

responsive. This can be seen from the following derivatives:

.

The model described above can be interpreted as a long-run equilibrium for banks. The

model gives the microfundations for arguing that there exists a long-run linear relationship

between and as well as between and . However, this model does not explain why

interest rates would show sluggish adjustment to changes in the interbank rate in the short-

run.

4.1 Short-Run Behavior of Banks in the Loan Market.

As Stiglitz and Weiss pointed out in their 1981 paper, asymmetric information, and moral

hazard can serve as a possible explanation for the sluggish adjustment of retail rates in the

short-run. Next, following Winker (1999), we combine the model of bank behavior

presented in the previous sections, and Stiglitz and Weiss (1981) model in order to develop

an appropriate model for the explanation of the sluggish adjustment of bank retail rates.

Consider there is an increase in bank marginal costs , according to the relationship

established in equation (2), the interest rate charged on loans should increase; in the short-

run, not necessarily. A hike in the interest rate charged on loans will lower the expected

return on investment for debtors with low risk projects; therefore, they will be crowded out

of the loan market leaving only the risky investors. Given the latter, banks will find

14

themselves facing adverse selection costs, and, as a consequence, banks will respond by

adjusting loan rates sluggishly. Now, suppose banks have previous knowledge of the

probability distribution of the risks associated to each possible project a debtor can pursue

(which was not the case before), but banks can’t observe the project chosen by each debtor

nor force the debtor to choose a particular project. In this case an increase in the lending

rate will give debtors an incentive to take on riskier projects to compensate for the increase

in borrowing cost; this is, moral hazard could take place forcing banks to adjust rates

sluggishly. The cost of a bank not reacting to an increase in marginal costs and the

asymmetric information costs of reacting via the interest rate charged on loans can be

represented by the following quadratic loss function:

,

this dynamic loss function can be minimized by choosing a path for . The term

indicates that a bank will suffer a loss from keeping its lending rate apart from the desired

level, , plus a fixed mark up (the optimal long-run level); the term penalizes any fast

movements of the lending rate, while the last term, , indicates that a bank will suffer

lower losses if it moves the lending rate in the same direction of the interbank rate.

In the short-run, due to the presence of adjustment costs as a consequence of informational

asymmetries, we expect a slow adjustment of bank lending rates in response to changes in

the interbank interest rate. However, in the long-run, we expect banks to complete this

adjustment.

4.2 Short-Run Behavior of Banks in the Deposit Market.

Regarding the rate on deposits, although equation (3) above implies a long-run relationship

between the interbank rate and the rate on deposits, there are no asymmetric information

effects in its short-run adjustment as for the lending rate. However, it could be the case that

banks face menu costs when deciding to adjust the rate on deposits given a change in the

15

interbank rate; as a consequence, we would expect a short-run sluggish response of rates on

bank deposits to changes in the interbank interest rate.

From this section we can point out the following testable hypotheses regarding the interest

rate channel of monetary policy in the Dominican Republic:

1. There exist a long-run linear relationship between bank retail rates and the

interbank interest rate.

2. In the short-run, bank retail rates do not fully adjust to changes in the interbank

interest rates. Furthermore, this adjustment process is not the same for both the

lending rate and the rate on deposits.

5. Data and methodology

The data set consists of monthly annualized interest rates series of the interbank, lending,

and deposit interest rates of different maturities for the period 1996:01-2011:08. The data

has been obtained from the Central Bank of the Dominican Republic. Table A1 in

Appendix A offers a brief description of the interest rates for the time period

aforementioned, while Table A2 in the same appendix presents some descriptive statistics

of each interest rate series. We construct a banking industry concentration measure using

balance sheet data obtained from Dominican Republic’s Superintendency of Banks.

5.1 Symmetric Error Correction Model

Using ordinary least squares, we estimate the following long-run linear relationship

between the bank interest rate and the money market (interbank) interest rate:

16

where stands for a bank rate, stands for the money market rate, is a constant

cost markup, and is the long-run coefficient. The idea behind (7) is that the money

market rate reflects the marginal funding costs faced by banks; therefore, we would expect

changes in the money market rate to cause changes in bank lending and deposit rates.

indicates that the pass-through is complete in the long-run, this is, an increase in

banks’ marginal costs (the money market or interbank interest rate) is fully transmitted to

bank rates in the long-run; indicates an incomplete long-run pass-through; while

indicates the pass-through is more than complete in the long-run, which, in the case

of lending rates, means that banks increase their lending rates by an amount greater than

the increase in the money market (interbank) rate in order to compensate for additional

risks (de Bondt, 2002). This long-run relationship is affected by the elasticity of demand for

loans and the elasticity of supply of deposits. Furthermore, these elasticities are affected by

the degree of competition in the banking sector, as the intensity of competition grows (more

elastic demand), bank lending rates are less able to respond to changes in the money market

rate, while the opposite is true for bank deposit rates and the elasticity of supply of deposits.

We employ single equation error correction models (ECM) in order to capture the long-run

relationship and short-run dynamics of the pass-through of interest rates. After finding

first-difference stationarity in the interest rates time series and establishing long-run

relationships via the Engle and Granger cointegration test, an error correction model is

specified the following way: 5

where represents the speed of adjustment, measured as the percentage of disequilibrium

that is corrected monthly, represents the long-run pass-through coefficient, stands

5 Unit root tests are performed using the augmented Dickey and Fuller test (ADF), the Phillips and Perron tests (PP), and the Kwiatkowski, Phillips, Schmidt, and Shin test (KPSS). Appendix B presents these results along with cointegration analysis.

17

for a bank retail rate, stands for the money market (interbank) rate, and is the first

difference operator. The speed of adjustment coefficient is expected to be less than one

given the presence of adjustment costs due to informational asymmetries. In the short-run,

banks will compare these adjustment costs with the costs associated with keeping bank

rates apart from their long-run equilibrium level. We apply this error correction model to

two different data sets: first, to monthly interest rate data for the period 1996-2002, second,

to monthly interest rate data for the period 2005-2011.

5.2 Asymmetric Error Correction Model

In order to allow for asymmetric adjustment in bank lending and deposit rates, we follow

Borenstein et al. (1997) and estimate the following modification of the error correction

model presented above:

(9)

where and

, therefore and

indicate the contemporaneous responses of bank rates to money market rate increases and

decreases, respectively; and

; and

indicate positive and negative deviations from long-run equilibrium, respectively,

therefore and are asymmetric speed of adjustment coefficients. Our asymmetric error

correction equation differs from the one estimated by Borenstein et al. (1997), in that they

did not allow for the presence of asymmetries in the speed of adjustment coefficients.6

Given that our evidence suggests that the interest rate channel of monetary policy

transmission was almost nonexistent during the pre-crisis and institutional changes period,

the analysis of asymmetric adjustment is only made for the period 2005-2011.

6 Other works on the pass-through of interest rates, such as Scholnick (1996) and Sarno and Thorton (2003),

also allow for this kind of asymmetry.

18

Notice that we can get a positive deviation from the long-run equilibrium as a

consequence of an increase in the bank interest rate, a decrease in the interbank interest

rate, or both. Similarly, we get a negative deviation when the bank rate decreases, the

interbank rate increases, or both. In both, the estimation of the symmetric error correction

equation and the asymmetric error equation, we follow Espinosa-Vega and Rebucci (2003)

and assume that the interbank interest rate, which serves as the proxy for the policy rate, is

exogenous; this is, all the adjustment is made by the bank rates within a one-month period.

The motivation behind the estimation of equation (9) comes from the fact that during the

period under consideration, the four largest commercial banks held on average 77 percent

of total assets of the banking sector. Provided that market concentration is a good proxy for

the degree of competition, we would expect bank lending rates to exhibit a faster

adjustment in response to an increase in the money market, and bank deposit rates to

exhibit a slower adjustment in response to the same increase in the money market rate

rather than to a decrease. This is, although an increase in the money market rate would

cause an increase in both loan and deposit rates in the long-run, in the short we would

expect lending rates to adjust faster than deposit rates effectively causing an increase in

banks’ margins.

6. Results

We estimated equation (7) using ordinary least squares. Based on equation (8) and

employing the Akaike information criterion (AIC) to choose the appropriate number of

lags, we estimated a symmetric single equation error correction model for bank rates for the

periods 1996-2002 and 2005-2011. Results are shown in Table 1 below.

19

1996-2002 Time Period

A long-run linear relationship could only be established between the 180 days bank lending

rate (LR180) and the interbank interest rate.7 The long-run pass-through coefficient, ,

practically indicates a complete pass-through, =1, meaning that, in the long-run, a change

in the interbank interest rate is fully transmitted to the 180 days bank lending rate. In the

short-run, the speed of adjustment coefficient, , indicates that about 42 percent of any

disequilibrium in the long-run relationship is corrected within a one-month period. These

results are consistent with the description of the evolution of monetary policy in the

Dominican Republic presented in Section 3. During this period, monetary policy was

oriented towards the management of monetary aggregates, and implemented via the buying

and selling of Central Bank’s debt instruments. Although there was an interbank market for

short term funds already in place, it was not until 2004 that policy makers started to

explicitly manage liquidity in this market in order to stabilize the interbank interest rate

around the policy rate. The absence of a long-run linear relationship between three of the

four bank interest rates analyzed and the interbank interest rate is a clear sign of an

underdeveloped and almost nonexistent interest rate channel of the monetary policy

transmission mechanism

7Given the low power of the Engle-Grangle tests in the presence of an asymmetric adjustment process, we performed Enders and Siklos (2001) asymmetric threshold cointegrations tests on the three remaining bank interest rate series and still failed to reject the null hypothesis of no cointegration .

20

Table 1. Symmetric Error

Correction Model Estimation

Results

1996-

2002

2005-2011

LR180

LR180 LR360 DR180 DR360

Long-run pass-through

0.90*

[0.06]

1.42*

[0.13]

1.35*

[0.09]

1.00*

[0.04]

1.00*

[0.05]

Speed of adjustment

-0.42*

[0.13]

-0.61*

[0.08]

-0.53*

[0.14]

-0.35*

[0.09]

-0.21**

[0.08]

Adj.

0.30

0.60 0.52 0.67 0.58

DW 1.94

2.12 2.01 1.94 2.01

Serial correlation LM-test (0.62)

(0.27) (0.70) (0.18) (0.23) Note: LR180 is the 180 days bank lending rate while DR360 is the 360 days bank deposit rate. *Statistical significance at the 1 percent level. **Statistical significance at the 5 percent level. Standard errors in brackets. P-values in parenthesis.

2005-2011 Time Period

A long-run linear relationship was established between our four bank interest rates and the

interbank interest rate. For both lending rates, the long-run pass-through coefficient is well

above 1, meaning the pass-through of changes in the interbank rate to these lending rates is

more than complete in the long-run. Instead of rationing credit in the short-run, banks are

increasing lending rates to compensate for, perhaps, additional risks (de Bondt, 2002).

However, in the short-run, the pass-through is incomplete; for the 180 days bank lending

rate the speed of adjustment coefficient states that about 61 percent of any disequilibrium in

the long-run relationship is corrected within a one-month period; for the 360 days bank

lending rate (LR360), about 53 percent of any disequilibrium in the long-run relationship is

corrected within a one-month period. Less than one speed of adjustment coefficients found

in our results can be attributed to the presence of adjustment costs due to informational

asymmetries in the loan market. For both deposit rates, the 180 days bank deposit rate

21

(DR180) and the 360 days bank deposit rate (DR360), the long-run pass-through coefficient

indicates that a change in banks’ marginal costs (this is, the interbank interest rate) is fully

transmitted to deposit rates in the long-run. Nevertheless, as indicated by the speed of

adjustment coefficients, the pass-though is incomplete in the short-run. This can be

attributed to the presence of menu costs when banks decide whether to adjust the rate on

deposits given a change in the interbank interest rate.

In contrast with the pre-crisis period, the new Monetary and Financial Law of December

2002 gave policymakers a single objective, the achievement of price stability- a low and

stable inflation rate, which has directed the Dominican Republic’s Central Bank towards

the adoption of an inflation targeting regime. Under inflation targeting, the literature as well

as central banks around the world identify the interest rate channel as one of the key

monetary policy transmission channels, and the short-term interest rate as the key monetary

policy instrument. Our finding of the existence of a long-run relationship between the

policy rate and banks’ retail rates indicates that the Dominican Republic is on the right path

to such regime. Furthermore, comparing the 180 days lending rate between the two time

periods, we can see that the speed of adjustment coefficient experienced an increase of

about 19 percentage points, suggesting an improvement in the effectiveness of the interest

rate channel of monetary policy transmission. This was expected, since it was not until

January of 2004 that Central Bank’s instruments, such as the zero coupon bonds, and

permanent liquidity facilities, such as the Overnight Window and the Lombard Window,

entered the money market.

Moreover, these results lend support to equations (2) and (3) in Section 4, which suggest

that as the degree of competition in the banking sector falls, lending rates become more

sensitive than deposit rates to changes in the money market (interbank) rate. As shown in

Appendix A at the end of this document, the four firm concentration ratio, which is used as

a proxy for competition, indicates that in the pre-crisis period about 60 percent of total

assets of the banking industry were held by the four major banks in the industry; in the

period after the banking crisis, this number suffered a 20 percentage points increase; thus,

suggesting a less competitive banking sector.

22

Following Duran-Viquez and Esquivel-Monge (2008), we computed the average number of

months it takes for the pass-through to be complete which is given by

.8 For both

lending rates, the adjustment takes, on average, no more than 1.4 months to be complete.

For both deposit rates, this number turned out to be 1.6.

Regarding the presence of asymmetries in the speed of adjustment coefficients, our results

from the estimation of (9) for the period 2005-2011, shown in Table 2 below, were not

expected. Wald tests applied to the asymmetric contemporaneous responses of bank rates

were not able to reject the null hypothesis of no asymmetry in the adjustment of three of

our four bank interest rates. Furthermore, we were not able to reject the null hypothesis of

no asymmetry in the speed of adjustment of our four bank interest rates, this is, bank rates

adjust with the same speed to both increases and decreases of the interbank interest rate.

We only found evidence of asymmetric contemporaneous response in the 180 days bank

deposit rate (DR180). The results indicate that a contemporaneous response of this rate to

money market rate changes is greater for increases in the money market rate than for

decreases (

. This result is usually present in competitive markets where banks

may believe that there could be an unfavorable response of consumers given a decrease in

bank deposit rates; and, therefore are reluctant to lower this rate. However, these results

should be interpreted with caution given the ordinary least squares estimates of the speed of

adjustments coefficients’ small sample properties.9 Given that our four firm market

concentration ratio suggests the banking sector in the Dominican Republic is closer to an

oligopoly than to perfect competition, we would have expected lending rates to exhibit

downward rigidity, and deposit rates to exhibit upward rigidity. This behavior would have

been consistent with banks trying to increase their margins by adjusting asymmetrically to

changes in the interbank interest rate. From this we can conclude that concentration ratios

are not necessarily good indicators of the presence of collusive arrangements in the banking

8 2005-2011 Time period.

9 In small samples, tests for asymmetric speed of adjustment have low power in rejecting the null of symmetric adjustment. Furthermore, we re-estimated the asymmetric error correction model by imposing symmetric contemporaneous responses but did not get different results regarding the speed of adjustment coefficients.

23

industry. The four-firm concentration ratio only accounts for the market share of four of the

firms in an industry that could hold tens and does not account for significant swings in

market share amongst the top four companies. Given that concentration ratios do not

provide a lot of detail concerning competitiveness in an industry, they could only indicate a

vague degree of competition.

Table 2. Asymmetric Error Correction

Model Results

2005-2011

LR180 LR360 DR180 DR360

Contemporaneous

response given increase in

the interbank rate (

0.48

[0.51]

0.14

[0.47]

0.84*

[0.16]

0.76*

[0.21]

Contemporaneous

response given increase in

the interbank rate (

-0.27

[0.75]

0.25

[0.31]

0.43*

[0.11]

1.00*

[0.27]

Speed of adjustment +

-0.57*

[0.16]

-0.78**

[0.32]

-0.51**

[0.24]

-0.14

[0.09]

Speed of adjustment -

-0.41*

[0.17]

-0.43**

[0.19]

-0.10

[0.15]

-0.11

[0.20]

Adj.

0.55 0.51 0.71 0.62

Wald test:

(0.41) (0.85) (0.04) (0.53)

Wald test:

(0.57) (0.35) (0.22) (0.90 )

DW

2.25 1.94 1.84 1.82

Serial correlation LM-test

(0.06) (0.34) (0.31) (0.19) *Statistical significance at the 1 percent level. **Statistical significance at

the 5 percent level. Standard errors in brackets. P-values in parenthesis.

24

7. Concluding remarks

The purpose of this research was to empirically investigate the pass-through of interest

rates in the Dominican Republic. From 2003 to 2005 a mix of institutional changes, such as

the promulgation of the new Monetary and Financial Law, along with a banking crisis

affected the way monetary policy was conducted in the Dominican Republic. Given the

latter, we divided our aggregate monthly interest rates data into two different samples (

before and after institutional changes), and employed Engle-Granger cointegration test and

single equation error correction models in order to capture the long-run relationship and

short-run dynamics of the pass-through of rates before and after institutional changes. Next,

we summarize our main results:

In the pre-crisis and institutional changes period, a long-run relationship could only be

established between the 180 days bank lending rate and the interbank interest rate. The

long-run pass-through coefficient indicates a complete pass-through, while the short-run

speed of adjustment coefficient indicates that about 42 percent of any disequilibrium in the

long-run is corrected within a one-month period.

In the post-crisis and institutional changes period, a long-run relationship was established

between our four bank interest rates and the interbank interest rate. The long-run pass-

through coefficient indicates a more than complete pass-through of changes in the interbank

rate to lending rates and a complete pass-through regarding the rates on deposits, while the

speed of adjustment coefficient indicates an incomplete pass-through in the short-run for

both lending and deposit rates. In particular, 61 percent of any disequilibrium between the

180 days lending rate and the interbank interest rate is corrected within a one-month period.

Comparing the 180 days lending rate between the two time periods, we can see that the

speed of adjustment coefficient experienced an increase of about 19 percentage points.

25

Our estimates suggested that, going from the pre-crisis and institutional changes period to

the post-crisis and institutional changes period, the effectiveness of monetary policy

transmission via the interest rate channel increased in the Dominican Republic. This result

was not unexpected to us, given that the new Monetary and Financial Law of December

2002 not only enforced the Central Bank’s autonomy, but also gave policymakers the

appropriate money market instruments for the implementation of monetary policy within

the interest rate channel.

The evidence presented lent support of the presence of downward rigidity in the adjustment

of the 180 days post-crisis bank deposit rate. No evidence of asymmetric adjustment to

changes in the interbank interest rate was found on our three remaining bank rates.

In this paper, we studied how changes in the interbank interest rate, which served as a

proxy for the policy rate, are transmitted to the interest rates of the banking sector. Further

research should include a second stage, namely, how the changes in these interest rates are

translated into changes in output, prices, and employment in the short-run.

26

Reference s

Banco Central de la República Dominicana, 2010. “Estrategia Para La Implementación De

Un Esquema De Metas De Inflación En La República Dominicana”. Available on

www.bancentral.gov.do

Scholnick, Barry, 1996. “Asymmetric adjustment of commercial bank interest rates:

evidence from Malaysia and Singapore,” Journal of International Money and Finance, vol.

15, issue e, pages 485-496.

Berger, Allen N & Hannan, Timothy Hannan, 1989. "The Price-Concentration Relationship

in Banking," The Review of Economics and Statistics, MIT Press, vol. 71(2), pages 291-99,

May

Bernanke, S. B., and M. Gertler, 1995, “Inside the Black Box: The Credit Channel of

Monetary Policy Transmission,” The Journal of Economic Perspectives, Vol. 9, No. 44, pp.

27–48.

Berstein, Solange & Fuentes, 2004. "Concentration and Price Rigidity: Evidence for the

deposit Market in Chile," Econometric Society 2004 Latin American Meetings 67,

Econometric Society.

_________________________ 2004. "Is There Lendign Rate Stickiness in the Chilean

Banking Industry?," Central Banking, Analysis, and Economic Policies Book Series, in:

Luis Antonio Ahumada & J. Rodrigo Fuentes & Norman Loayza (Series Editor) & Klaus

27

Schmidt-Hebbel (Se (ed.), Banking Market Structure and Monetary Policy, edition 1,

volume 7, chapter 6, pages 183-210 Central Bank of Chile

Betancourt, Rocio; Vargas, Hernando y Rodriguez, Norberto. “Interest Rate Pass-Through

in Colombia: a Micro-Banking Perspective”. Cuad. econ. 2008, vol.45, n.131, pp. 28-58.

Borenstein, S., Cameron A. C., and R. Gilbert, 1997. “Do gasoline prices respond

asymmetrically to crude oil price changes? The Quaterly Journal of Economics 112, 305-

339.

Chan, K. 1993, “Consistency and limiting distribution of the least squares estimator of a

threshold autoregressive model,” Annals of Statistics, 21, 520-33.

Cottarelli, C., and A. Kourelis, 1994, “Financial Structure, Bank Lending Rates and the

Transmission Mechanism of Monetary Policy,” IMF Working Paper WP/94/39

(Washington:International Monetary Fund).

De Bondt, Gabe, 2002, “Retail Bank Interest Rate Pass-Through: New Evidence at the Euro

Areal Level,” European Central Bank. Working Paper Series. Working Paper No. 136.

Don Breding & Trevor Fitzpatrick & Gerard O Reilly, 2002. “Retail Interest Rate Pass-

Through- The Irish Experience,” The Economic and Social Review, Economic and Social

Studies, vol. 33(2), pages 223-246.

28

Duran-Viquez and Esquivel-Monge, “Policy Rate Pass-Through: Evidence from the Costa

Rican economy”, Banco Central de Costa Rica. Research Paper. DIE-05-2008-DI, June,

2008.

Enders, Walter & Siklos, Pierre L, 2001. "Cointegration and Threshold

Adjustment," Journal of Business & Economic Statistics, American Statistical Association,

vol. 19(2), pages 166-76, April.

______________ & Granger, Clive W J, 1998. “Unit Root Tests and Asymmetric

Adjustment with an Example Using the Term Structure of Interest Rates,” Journal of

Business & Economic Statistics, American Statistical Association, vol. 16(3), pages 304-11,

July.

Engle, Robert F. and Granger, Clive W. J., 1987, “Co-Integration and Error Correction:

Representation, Estimation, and Testing,” Econometrica, 55, issue 2, p. 251-76.

Espinosa-Vega, Marco A. & Rebucci, Alessandro 2004. "Retail Bank Interest Rate Pass-

through: Is Chile Atypical?," Central Banking, Analysis, and Economic Policies Book

Series, in: Luis Antonio Ahumada & J. Rodrigo Fuentes & Norman Loayza (Series Editor)

& Klaus Schmidt-Hebbel (Se (ed.), Banking Market Structure and Monetary Policy, edition

1, volume 7, chapter 5, pages 147-182 Central Bank of Chile.

Freixas, Xavier and Rochet, Jean-Charles 1998. Microeconomics of Banking. The MIT

Press. London, England.

29

Hannan, Timothy H & Berger, Allen N, 1991. "The Rigidity of Prices: Evidence from the

Banking Industry," American Economic Review, American Economic Association, vol.

81(4), pages 938-45, September.

Hausmann, Ricardo, Panizza, Ugo G. and Stein, Ernesto Hugo, “Why Do Countries Float

The Way They Float?,” (April 2000). Available at SSRN: http://ssrn.com/abstract=223650

or doi: 10.2139/ssrn.223650.

MacKinnon, J. G., 1996, “Numerical distribution functions for unit root and cointegration

tests,” Journal of Applied Econometrics, 11, 601-618.

Sanchez-Fung, Jose R. 2005. “Exchange Rates, Monetary Policy, and Interest Rates in the

Dominican Republic during the 1990s Boom and New Millennium Crisis,” Journal of Latin

American Studies, 37(04), pp. 727-738. ISSN (print) 0022-216X

Stiglitz, J. and A. Weiss. 1981. “Credit Rationing in Markets with Imperfect Information.”

American Economic Review 71(3):393–410.

Winker, P. 1999. “Sluggish Adjustment of Interest Rates and Credit Rationing: An

Application of Unit Root Testing and Error Correction Modeling.” Applied Economics

31(3): 267–77.

30

Appendix A: Data

Table A1. Variables Descriptions and Abbreviations

Variable Abbreviation Description Source

Overnight interbank

rate

INTERBANK Overnight interbank

lending rate. (proxy

for the policy rate)

Central Bank of

The Dominican

Republic

(CBDR)

Lending rates LR180 180 days nominal

bank lending rate.

CBDR

LR360 360 days nominal

bank lending rate.

Deposit rates

Concentration ratio

DR180

DR360

CR4

180 days nominal

bank deposit rate.

360 days nominal

bank deposit rate.

Four-firm

concentration ratio

CBDR

Balance sheet

data: Dominican

Republic’s

Superintendency

of Banks

Table A2. Interest Rates 1996-2002

Variable Mean Std. deviation Max. Min Bank rates/

Interbank

rate

correlation

coefficient.

Interbank 15.15 2.60 19.46 8.93 1.00

LR180 24.84 2.81 29.77 18.02 0.83

LR360 24.57 2.91 30.38 19.06 0.81

DR180 15.05 2.65 21.37 10.89 0.73

DR360 15.46 3.02 24.47 10.24 0.65

31

Table A3. Interest Rates 2005-2011

Variable Mean Std. deviation Max. Min Bank rates/

Interbank

rate

correlation

coefficient.

Interbank 9.11 2.55 15.51 5.33 1.00

LR180 15.70 4.35 24.47 8.31 0.83

LR360 16.95 4.07 25.20 9.10 0.85

DR180 8.45 2.71 14.55 4.79 0.95

DR360 8.79 2.76 14.73 5.30 0.92

8

10

12

14

16

18

20

1996 1997 1998 1999 2000 2001 2002

Interbank interest rate

16

18

20

22

24

26

28

30

1996 1997 1998 1999 2000 2001 2002

180 days bank lending rate

Chart A1. Monthly Interest Rates for the Time Period 1996-2002 Source: Central Bank of the Dominican Republic

32

18

20

22

24

26

28

30

32

1996 1997 1998 1999 2000 2001 2002

360 days bank lending rate

10

12

14

16

18

20

22

1996 1997 1998 1999 2000 2001 2002

180 days bank deposit rate

10

12

14

16

18

20

22

24

26

1996 1997 1998 1999 2000 2001 2002

360 days bank deposit rate

33

Chart A2. Monthly Interest Rates for the Time Period 2005-2011 Source: Central Bank of the Dominican Republic

4

6

8

10

12

14

16

2005 2006 2007 2008 2009 2010 2011

Interbank interest rate

8

12

16

20

24

28

2005 2006 2007 2008 2009 2010 2011

180 days bank lending rate

8

12

16

20

24

28

2005 2006 2007 2008 2009 2010 2011

360 days bank lending rate

34

4

6

8

10

12

14

16

2005 2006 2007 2008 2009 2010 2011

180 days bank deposit rate

4

6

8

10

12

14

16

2005 2006 2007 2008 2009 2010 2011

360 days bank deposit rate

Chart A3. Four-Firm Concentration Ratio 2000-2011 Source: Author’s calculation using balance sheet data from the Dominican Republic’s Superintendency

of Banks

.50

.55

.60

.65

.70

.75

.80

.85

00 01 02 03 04 05 06 07 08 09 10 11

C4

35

The four firm concentration ratio (CR4), is the proportion of total assets in the banking

sector that is held by the four major banks; it is commonly used to indicate the degree to

which an industry is oligopolistic. The CR4 is calculated as follows:

where is the value of the assets held by an individual bank, and is the value of total

assets in the banking sector. The closer the value of the ratio to one, the higher the degree

of concentration in the banking industry. The CR4 has a couple of drawbacks; first, it does

not take into account the total distribution of banks in the industry, and second, the choice

of the number of banks to include in the numerator is arbitrary.

Looking at Chart A3 above, we can clearly note that before the banking crisis (year 2003)

the CR4 ranged between 0.55 and 0.60, meaning that a maximum of about 60% of total

assets in the banking industry was held by the four major banks in the industry. After the

crisis, this changed, and now we have about 80% of total assets in the banking industry

held by the four major banks, therefore, suggesting a significant increase in the degree of

concentration.

36

Appendix B: Unit root and Cointegration Tests

Unit Root Tests

• ADF: includes enough lagged dependent variables to rid the residuals of

serial correlation. Under the null hypothesis, the series in question has a unit root.

• PP: alternative to the ADF test, modifies the test statistic so that no additional lags

of the dependent variable are needed in the presence of serially-correlated errors .

• KPSS: the Kwiatkowski, Phillips, Schmidt, and Shin test differ from the other unit

root tests in that the series in question is assumed to be stationary under the null.

37

Table B1. Unit Root Tests

Interest Rate ADF PP KPSS

1996-2002

Interbank -6.25* -6.33* 0.06*

LR180 -3.27*** -11.55* 0.08*

LR360 -9.17* -9.18* 0.08*

DR180 -5.50* -12.27* 0.05*

DR360 -10.19* -10.32* 0.15*

2005-2011

Interbank -4.47* -4.34* 0.06*

LR180 -11.89* -12.01* 0.07*

LR360 -1.93 -10.25* 0.09*

DR180 -2.51 -4.38* 0.09*

DR360 -4.05* -5.46* 0.11*

*Nonstationarity rejected at 1% ; **Nonstationarity rejected at 10 % Lags chosen using the

AIC. is the first difference operator.

Cointegration Tests

To test for the existence of a long-run equilibrium relationship among our interest rates

series, we employ the Engle and Granger (1987) two-step methodology; this methodology

assumes linearity and symmetric adjustment. First, we use ordinary least squares (OLS) to

estimate the long-run equilibrium relationship in the form:

where stands for a bank rate, either a lending or a deposit rate, and stands for a

money market rate, which in our case is the interbank interest rate. If the variables are

cointegrated, the OLS regression yields “super-consistent” estimators of the cointegrating

parameters and .

38

Second, to determine if the variables are cointegrated, we focus on the OLS estimate of

in the regression equation:

where denotes the series of the estimated residuals from equation (B1). The lagged

changes in the sequence ensure that the errors approximate a white-noise process. If

, equations (B1) and (B2) jointly imply the existence of an error-correction model.

Under the null hypothesis, the series are not cointegrated, and critical values correspond to

Mackinnon (1996). What follow are tables (B1) and (B2), which present the results:

Table B2. Engle-Granger Cointegration Test, 1996-2002

Engle-Granger Cointegration Test

Interbank

Rate-Lending

Rates

Interest Rate

Tau- statistic

Mackinnon

(1996) p-values

LR180 -5.22* 0.00

LR360 -2.77 0.18

* The two interest-rates series are cointegrated.

Lags chosen using the AIC.

Interbank Rate-

Deposit Rates

Interest Rate

Tau- statistic

Mackinnon

(1996) p-values

DR180 -2.62 0.24

DR360 -0.46 0.98

39

Table B3. Engle-Ganger Cointegration Test, 2005-2011

Engle-Granger Cointegration Test

Interbank

Rate-Lending

Rates

Interest Rate

Tau- statistic

Mackinnon

(1996) p-values

LR180 -3.59* 0.03

LR360 -3.55* 0.04

* The two interest-rates series are cointegrated.

Lags chosen using the AIC.

Interbank Rate-

Deposit Rates

Interest Rate

Tau- statistic

Mackinnon

(1996) p-values

DR180 -4.71* 0.00

DR360 -3.74* 0.02

40

Appendix C: Freixas and Rochet (1998) Model of Bank Behavior

First, we start by assuming that there are N identical banks that are price-taker, this is,

banks take the interest rate on loans ,the interest rate on deposits and the interbank

interest rate all as given. Each of these N banks faces an identical cost function,

, interpreted as the cost of managing a volume of deposits, and a volume of

loans. Taking into account the management costs, the profit of a bank is given by

(subscripts omitted):

where , the net position of a bank on the interbank market, is given by:

where is a compulsory share of deposits that must be kept as reserves. Using

(C2) to re-write (C1) we get:

Therefore, the profit of each bank is the sum of the intermediation margins on loans and

deposits net of management costs. Assuming concavity in the profit function and

decreasing returns to scale in the cost function, we differentiate (C3) with respect to and

to get the profit-maximizing first-order conditions:

41

.

Hence, competitive banks will adjust their demand for deposits and\or their supply of loans

in such a way that intermediation margins equal management costs.

C.1 Equilibrium of the Banking Sector under Perfect Competition.

The competitive equilibrium is characterized by the following equations:

where is the net supply of government bonds, the investment demand by firms,

) the savings function of households, is the loan supply of bank , and

is the deposit demand of bank . Note that since we assume that the Central

Bank can control (by injecting or draining cash from the interbank market) becomes

exogenous and equation (C7) disappears.

Assuming constant marginal cost of intermediation, ,

, and using the first-

order conditions derived above, we can characterize the equilibrium in the following way:

42

note that,

> 0 and

> 0.

C.2 Equilibrium of the Banking Sector under Imperfect Competition.

As a consequence of the significant barriers to entry that exists in the banking industry, a

model of imperfect competition seems more appropriate to model banks’ behavior.

As before, we assume there are N banks all having the following linear cost function:

Each of the N banks, taking as given the volume of deposits and loans of other banks,

choose the pair

that solves:

where bank faces the following inverse demand function for loans

and the following inverse supply function of deposits

The first-order

conditions are:

43

These first-order-conditions can also be written as:

where the elasticity of demand for loans and the elasticity of supply of deposits are as

follow:

.

Note that from (C11a) and (C11b) the sensitivity of and

to changes in interbank rate

depends on N, the number of firms, which may be interpreted as a proxy for the intensity

of competition (N=1 being a monopoly). As the intensity of competition grows (larger N),

becomes less responsive to changes in , while

become more responsive. This can be

seen from the following derivatives:

.

44

The model described above can be interpreted as a long-run equilibrium for banks. The

model gives the microfundations for arguing that there exists a long-run linear relationship

between and as well as between and .