The effects of monetary policy changes on market interest rates in greece: An event study approach

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The operational procedures of the Bank of Greece underwent major changes during the 1990s. These shifts in operational strategy made interest rates the main tool of monetary policy for the first time in Greece. This paper examines the effects of changes in the bank’s operational interest rates on market interest rates at eight maturities and for different operational regimes. A major feature of our study is the application of the event study methodology used in finance, which has not been employed in any previous study on this subject. We find that changes in official interest rates had a significant influence on short-term and intermediate-term rates and that this relationship was affected by the changes in the bank’s operational procedure.

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International Review of Economics and Finance 15 (2006) 487 – 504<br /><br /> <br /> <br /> <br /> <br /> The effects of monetary policy changes on market interest<br /> rates in Greece: An event study approach<br /> Asimakis Kaketsis a, Nicholas Sarantis b,*<br /> a<br /> Deutsche Asset Management, UK<br /> b<br /> Department of Economics, Finance and International Business, London Metropolitan University,<br /> 84 Moorgate, London EC2M 6SQ, UK<br /> Received 20 October 2003; received in revised form 15 September 2004; accepted 29 September 2004<br /> Available online 22 December 2004<br /> <br /> <br /> <br /> Abstract<br /> <br /> The operational procedures of the Bank of Greece underwent major changes during the 1990s. These shifts in<br /> operational strategy made interest rates the main tool of monetary policy for the first time in Greece. This paper<br /> examines the effects of changes in the bank’s operational interest rates on market interest rates at eight maturities<br /> and for different operational regimes. A major feature of our study is the application of the event study<br /> methodology used in finance, which has not been employed in any previous study on this subject. We find that<br /> changes in official interest rates had a significant influence on short-term and intermediate-term rates and that this<br /> relationship was affected by the changes in the bank’s operational procedure.<br /> D 2004 Elsevier Inc. All rights reserved.<br /> <br /> JEL classification: E52; E58; C52<br /> Keywords: Central Bank operational procedure; Monetary policy; Market interest rates; Event study<br /> <br /> <br /> <br /> <br /> 1. Introduction<br /> <br /> Since the late 1980s we have witnessed substantial liberalisation of Greece’s financial markets.<br /> Controls on cross-border capital flows have been lifted and restrictions affecting competition and price<br /> <br /> <br /> * Corresponding author. Tel.: +44 20 7320 1464; fax: +44 20 7320 1414.<br /> E-mail address: (N. Sarantis).<br /> <br /> 1059-0560/$ - see front matter D 2004 Elsevier Inc. All rights reserved.<br /> doi:10.1016/j.iref.2004.09.003<br /> 488 A. Kaketsis, N. Sarantis / International Review of Economics and Finance 15 (2006) 487–504<br /> <br /> <br /> flexibility in domestic financial markets have almost been completely removed. The experience of other<br /> countries indicates that changes in financial structure can have important implications for the conduct of<br /> monetary policy and a number of them have substantially revised their operating procedures during the<br /> past decade as financial market changes altered the relationship between policy tools and objectives. In<br /> Greece, changes in the monetary framework and financial reform have coincided during the transition,<br /> with the Bank of Greece (Greece’s Central Bank) in effect playing a dual role. It altered its operating<br /> strategy in response to the evolving financial environment, as well as instigating and guiding these<br /> changes. The complex system of controls, which had been in existence since the end of the Second<br /> World War, supported an operating strategy designed to influence the supply of credit, rather than the<br /> price of credit. However with the gradual relaxation of the complex system of controls in the late 1980s,<br /> the Bank of Greece shifted its strategy away from the control of monetary and credit aggregates towards<br /> the use of interest rates as the main tool in the transmission of monetary policy.<br /> This paper examines the effects of the Bank of Greece’s official interest rate on market interest rates at<br /> various maturities over the period 1994–2000, using daily data. The reaction of short-term and long-term<br /> market interest rates to changes in the bank’s official rate provides important information about the<br /> transmission of monetary policy into the money market. But although this relationship has been<br /> investigated in a number of advanced industrial countries, it has not been examined in emerging market<br /> economies undergoing financial liberalisation, like the Greek economy. In addition, Greece is now<br /> member of the European Monetary Union, where the transmission of monetary policy has been the<br /> subject of considerable debate.<br /> To measure the effect of central bank rates on market rates we employ the event study methodology.<br /> Event studies can circumvent many of the problems associated with the time series approach by focusing<br /> on the response of market rates in the days immediately surrounding changes in the intervention rates.<br /> Given rationality in the market place, the effect of an event, such as a change in the operational rate of<br /> the Central Bank, will be reflected immediately in market rates. Thus the impact of a change in a Central<br /> Bank’s intervention rate can be measured using changes in market rates observed over a relatively short<br /> time period. In this way, we can measure the immediate impact that a change has. This information is<br /> important in the conduct of monetary policy.<br /> The pioneering study on the channel between central bank interest rates and market rates using a<br /> similar methodology is that of Cook and Hahn (1989). The authors examine the effect of changes in the<br /> Federal Funds rate on market rates in the United States at various maturities around and on the day of<br /> changes in the Federal Funds rate. Thornton (1998) has also studied the market’s reaction to federal<br /> funds rate changes, but only on the day of the change in the Federal Funds rate. Like Cook and Hahn, he<br /> obtains successively lower values as the maturity increases. Hence, for the short rates, the direct liquidity<br /> effect is the predominant influence, while in the case of longer rates, expectations are more important.<br /> On the other hand, Garfinkel and Thornton (1995) present evidence suggesting that the Federal Funds<br /> rate is a no better indicator of monetary policy than other short term interest rates. Other studies for the<br /> United States include Cook and Hahn (1988), Thornton (1986, 1994), Dueker (1992), Rudebusch (1995)<br /> and Kuttner (2000). Paquet and Pe´rez (1995) carried out a study for Canada and show that changes in the<br /> overnight mostcall rate induce a significant effect on the rates of assets with up to 6 months maturity.<br /> Work has also been done for European countries. Pedersen (1997) reports that changes in the Danish<br /> discount rate are found to have significant effects on market rates and the effect declines with maturity. A<br /> study by the Deutsche Bundesbank (1996) reports similar results for Germany. Neumann and Weidmann<br /> (1998) also investigate the effect of the German discount rate on the overnight rate and find that, post-<br /> A. Kaketsis, N. Sarantis / International Review of Economics and Finance 15 (2006) 487–504 489<br /> <br /> <br /> unification, the size of this effect not only is reduced, but becomes insignificant. In contrast, Hardy<br /> (1998) finds that German market interest rates responded significantly to changes in the official rates<br /> during the 1990s, and these responses become even stronger when the changes in official rates are<br /> decomposed into anticipated and unanticipated changes. But in line with the evidence for the United<br /> States, Hardy also obtains successively smaller effects as the maturity of assets increases. Buttiglione,<br /> Del Giovane, and Tristani (1997) analyse the impact effects of changes in central bank rates on the term<br /> structure of interest rates in nine industrial countries. They find that central bank rates have a substantial<br /> impact both on short as well as on long rates, with short rates responding similarly across countries,<br /> while the reaction of long rates differs markedly between countries. Dale (1993) examines the impact of<br /> changes in the Bank of England’s bank 1 stop rate on market interest rates at seven different maturities in<br /> the days surrounding these policy changes. Dale’s results suggest that changes in the stop rate lead to<br /> significant responses in market interest rates for maturities of 1 month to 5 years, and that both<br /> anticipatory and learning effects are significant.<br /> An important contribution of our paper is the application of a more sophisticated method to that used<br /> in the above studies. We employ the more established and uniform event study methodology that is<br /> widely used in the field of financial economics (see MacKinlay, 1997). In this literature, most papers<br /> tend to focus on the impact of various events on security returns. However, this methodology has not<br /> been used in previous event studies of money market rates. What has been drawn from this literature is<br /> the methodological framework and the considerations raised from its empirical application to money and<br /> bond market.<br /> The remainder of the paper is organised as follows: in Section 2 we discuss the operational procedures<br /> of the Bank of Greece during the 1990s. In Section 3 we explain the event study methodology employed<br /> in the paper. Section 4 discusses the data. In Section 5 we present an analysis of the empirical results.<br /> Section 6 draws up the conclusions.<br /> <br /> <br /> 2. Operational procedures of the Bank of Greece<br /> <br /> During the 1990s, the Bank of Greece underwent two major regime changes (see Annual Report of<br /> the Governor of the Bank of Greece). The first regime describes the 1994–1997 period. Following the<br /> process of gradual financial liberalisation towards the end of the 1980s and early 1990s, combined<br /> with the efforts of achieving the Maastricht convergence criteria, the bank abandoned monetary<br /> aggregates and switched to the operational use of interest rates. The interventions in the interbank<br /> money markets that had begun in 1993 continued in 1994. In March 1994 the Athens Interbank Offer/<br /> Bid Rates were initiated (ATHIBOR/ATHIBID). This step allowed the Central Bank to utilise the<br /> interbank market in order to modernise its operating procedures. Interest rates became the main policy<br /> tool. Throughout this period, the final objective of the bank had been the deceleration of inflation,<br /> with the emphasis of monetary policy shifted from intermediate monetary targets to the protection of<br /> the exchange rate parity. The exchange rate was included for the first time as an intermediate target in<br /> 1993, although not on equal footing as the monetary target. In 1994, the exchange rate gained<br /> predominance over monetary aggregate targeting. However, until 1997, both targets were officially<br /> regarded as equally important. In practice, with the establishment of the money market rates with<br /> maturities longer than 1 day at the end of March 1994, the bank obtained a new intermediate target<br /> (exchange rate stability) and a new tool to achieve this target (the interest rate channel).<br /> 490 A. Kaketsis, N. Sarantis / International Review of Economics and Finance 15 (2006) 487–504<br /> <br /> <br /> From January 1993 until March 1997, the main interest rate used by the bank to conduct the required<br /> adjustments in liquidity was the bid rate in the overnight maturity. However, there were two rates at this<br /> maturity. The bid rate was used to withdraw liquidity and the offer rate was used to inject liquidity to the<br /> market. Interventions using these rates were activated at the bank’s discretion. In this respect the bank’s<br /> system was symmetric.1 The bid rate acted as the bank’s signal of its monetary policy stance. The offer<br /> rate was primarily used to inject liquidity during times of downward pressures on the exchange rate. As<br /> such, use of this rate was seen as temporary.2 Other discretionary interventions in the 14-day and 1-<br /> month maturities in the interbank markets were also used during this period. However, they were used on<br /> a much smaller scale. Their role was confined to acting as supplementary tools to help the bank<br /> withdraw the structural excess liquidity in the Greek money markets.<br /> The second regime lasted from April 1997 until the end of 2000. With the enactment of Law 2548/97<br /> regarding bProvisions Relating to the Bank of GreeceQ and the corresponding amendment to its statute,<br /> the Bank of Greece bacquired a modern institutional framework, compatible with the Treaty on<br /> European Union and the Statute of the European System of Central Banks.Q3 Thus, the Bank of Greece<br /> was granted independence and the operational procedures changed accordingly, with price stability<br /> becoming the primary objective.<br /> On March 27 1997, a two-tranche overnight deposit facility was introduced.4 Additionally, the bank<br /> changed its main monetary policy tool to a weekly repo with a 14-day maturity. This was not used<br /> systematically though, until January 1998. The change occurred in response to a document published by<br /> the European Monetary Institute (1997), where the proposed operating procedures for the new European<br /> Central Bank (ECB) were outlined. In effect the Bank of Greece started implemented the ECB’s<br /> operational procedure after March 27, 1997. Direct interventions in the overnight rate were suspended.<br /> A two-tranche overnight deposit facility was introduced, which represented the floor. The Lombard rate,<br /> which was already in place since 1993, represented the ceiling for the overnight rate.5 Both of these<br /> rates were nondiscretionary. Until January 1998, there were no regular liquidity operations. In January<br /> 1998, the 14-day repo, which had been in use as an irregular discretionary instrument, became the new<br /> operational rate of the bank. Operations were conducted weekly. A step closer to compliance with the<br /> ECB system was made during 1998, when a nonregular 91-day operation was introduced. As a result<br /> the bank harmonised its intervention procedures with those in other EU countries and the European<br /> Central Bank, as well as allowing market forces to have a larger influence on money market rates.6<br /> Hence the bank has followed world and European trends in conducting its liquidity management<br /> operation through market operations, with standing facilities being used as a bsafety valve.Q7<br /> 1<br /> Symmetric in the sense that facilities existed both to withdraw from and inject liquidity to the markets. This is contrasted to<br /> asymmetric facilities, where the central bank can only do one of the two (see Borio, 1997 for a discussion of this feature of<br /> central banking procedures).<br /> 2<br /> This is confirmed by reading the daily reports on the money market in the daily financial newspaper Nafteboriki. (See also<br /> Filippides, Kyriakopoulos, and Moschos (1995).<br /> 3<br /> Monetary Policy 1997–1998, April 1998, statement by the governor, Lucas Papademos.<br /> 4<br /> The limit for the first of the overnight deposit facility is set at 300 billion drachmas. This amount is shared out among<br /> domestic credit institutions dependent on their market share. The second tranche (with a lower interest rate) has no quota.<br /> 5<br /> The quota on the facility was gradually raised from 150 billion in 1994 to 480 billion in 1999.<br /> 6<br /> See for instance the Report on Monetary Policy by the Bank of Greece 1997–1998.<br /> 7<br /> The main difference is that whereas many countries have moved away from standing facilities towards market operations,<br /> Greece has never had an experience of conducting policy using standing facilities, since these were introduced alongside the<br /> discretionary liquidity control measures.<br /> A. Kaketsis, N. Sarantis / International Review of Economics and Finance 15 (2006) 487–504 491<br /> <br /> <br /> 3. Modelling methodology<br /> <br /> The methodology employed in our event study draws upon the work of MacKinlay (1997). This<br /> involves three steps.<br /> <br /> 3.1. Defining the event window<br /> <br /> In our case this is the change in the operational rate of the Bank of Greece. In practice, the event<br /> window includes not only the day of the actual change in the operational rate, but is expanded to include<br /> days surrounding the adjustment day.8 The reason for expanding the event window is to capture the<br /> dynamics of the market rate responses in the days surrounding the official interest rate changes. Two<br /> kinds of dynamics are of interest. Firstly, the extent to which the market anticipates changes, and<br /> secondly the degree to which the market responds with a lag (i.e., delayed or learning effects).9 If the<br /> markets anticipate the timing of policy changes it may lead to systematic movements in market rates in<br /> the days leading up to the change. Delayed effects occur when markets take time to digest information.<br /> Dale (1993) points out that significant movements in the interest rates in the day following the change<br /> may indicate a learning process. Such a learning process may be expected to be more pronounced when<br /> the Central Authority does not announce an explicit target level for its policy objective and hence the<br /> markets have to learn about changes in the target. This has direct implications for Greece, where such a<br /> learning process seems quite likely, especially in the period immediately after the change in the<br /> operational procedure and the introduction of more indirect methods of intervention. Prior to the<br /> independence of the bank, the governor was not in the habit of announcing clear and transparent policy<br /> aims as well as targets, certainly not in comparison to more transparent institutions such as the Bank of<br /> England. In view of the above, we follow previous studies in setting an event window of 5 days (2 days<br /> before and 2 days after the policy change).<br /> <br /> 3.2. Measuring the effects of the intervention rate<br /> <br /> Two methods have been used in the literature for measuring these effects. Both methods implicitly<br /> assume that there is an association between the magnitude of changes in the intervention rate and the<br /> response of market rates. The first is to run a regression of the type illustrated below:<br /> <br /> DRt ¼ a þ bDðINTERVÞt ð1Þ<br /> <br /> where DR t is the change in the market rate on a particular maturity at time t, and D(INTERV)t is the<br /> respective change in the intervention rate.10 Only days in the event window are considered. Days outside<br /> the event window are not included in the regression. But Dale (1993) notes that the main problem with<br /> Eq. (1) is the implicit assumption that the coefficient of interest, b, is constant. In practice though, the<br /> reaction of market rates to policy change is likely to depend on a whole array of factors, such as current<br /> 8<br /> See Cook and Hahn (1989) and Dale (1993).<br /> 9<br /> Roley and Sellon (1995) suggest that delayed effects may be the result of revisions in expectations.<br /> 10<br /> Dale (1993) and Thornton (1998) suggest that the value of coefficient b in Eq. (1) may vary, depending on whether changes<br /> in the official interest rate are large or small. In our empirical experimentation we tested for this effect, but the results were not<br /> significant.<br /> 492 A. Kaketsis, N. Sarantis / International Review of Economics and Finance 15 (2006) 487–504<br /> <br /> <br /> market sentiment, the extent to which the policy was anticipated, the information content of the policy<br /> change, external factors,11 and so on. In order for the coefficient b to be unbiased, these missing<br /> variables should be included. However, this is often not possible because the missing explanatory factors<br /> may be qualitative and thus difficult, if not impossible, to identify and measure. As a consequence, b<br /> becomes event specific. In these circumstances, Dale suggests that, in terms of the bivariate regression<br /> (Eq. (1)), the most efficient form of estimation would be to estimate it allowing b to be event-varying.<br /> Unfortunately, in analysing policy rate changes, estimation of this form is limited by the fact that these<br /> types of analysis, typically, have a limited number of observations in their samples. Therefore, although<br /> theoretically robust, in practice, estimation of the bivariate regression is limited to simple OLS on Eq.<br /> (1). This observation leads Dale to propose looking at mean responses in the two interest rates instead.<br /> The response to the change in the market rate is calculated as a percentage of the change in the official<br /> rate. The same method is adopted in our paper.<br /> <br /> 3.3. Determining the significance of results<br /> <br /> The method proposed by Dale (1993) is to compare the reaction of rates immediately surrounding<br /> official interest rate changes with those observed across the entire sample. Dale uses a standard t statistic<br /> test for the equality of two means. Such a test though makes strong parametric assumptions. This does<br /> not seem to be entirely adequate for the period under analysis. We introduce the more sophisticated<br /> method outlined in MacKinlay (1997), which has not been used in previous event studies of this type.<br /> The method can be viewed as having the following three steps.12<br /> <br /> 3.3.1. Measuring the abnormal effect<br /> To assess the event’s impact we require a measure of the abnormal effect. The abnormal effect for<br /> market rate i, (AC)i , is the actual ex post change of the market rate (DR)i minus the normal change<br /> (NC)i over the event window. The normal change is defined as the change in market rates that<br /> would be expected to occur even if the change in the operational rate did not take place. Ideally, the<br /> abnormal changes in market rates should represent only the effect of the operational rates for the<br /> particular date t. In the general event study literature there are two broad methods for measuring<br /> normal performance. One is to assume away any information and simply use a constant change<br /> model. This assumes that mean change in a given interest rate is constant through time. The second<br /> method is to estimate an econometric model, using suitable conditioning variables for capturing<br /> other influences on market interest rates. It is reasonable to assume that at least two factors apart<br /> from the intervention rate could influence changes in money market rates. One is developments in<br /> the foreign exchange market and the other is domestic liquidity conditions.13 During our experi-<br /> mentation stage, we tried to estimate a regression of changes in market rates on various proxies for these<br /> factors. But although we used many combinations of these variables, we could not find a significant<br /> relationship.<br /> As a result, we used the constant-change model to evaluate the normal change. The advantage of this<br /> method is that it is simple and in certain cases has good large sample properties. Brown and Warner<br /> <br /> 11<br /> Such as pressures on the exchange rate target.<br /> 12<br /> A good analysis of this testing procedure can also be found in Campbell, Lo, and MacKinlay (1997), Chap. 4.<br /> 13<br /> See, for example, Cable and Holland (1999).<br /> A. Kaketsis, N. Sarantis / International Review of Economics and Finance 15 (2006) 487–504 493<br /> <br /> <br /> (1980, 1985) find that it often yields results similar to those of more sophisticated models. In any case, in<br /> the absence of any suitable conditioning information, one may have to use this method to ascertain the<br /> impact of events. The normal change model is given by:<br /> <br /> DRiv ¼ li þ giv ð2Þ<br /> <br /> where l i is the normal change that is estimated using the estimation window, and v=1,. . .,V measures the<br /> number of daily observations in the estimation window. Generally the event window itself is not<br /> included in the estimation period to prevent the event from influencing the estimates of the normal<br /> change model. The estimation window that will be used in this study includes the days surrounding the<br /> 5-day event window.<br /> The sample variance of the abnormal changes estimated using the estimation window (the period<br /> outside the event window) is obtained by:<br /> <br /> 1 XV<br /> r2giv ¼ ðDRiv  li Þ2 ð3Þ<br /> V  1 v¼1<br /> <br /> The abnormal change for market rate i on the event day s, (AC)is , is calculated as the difference<br /> between the actual change and the normal change on the event day (day of change in the operational<br /> rate):<br /> <br /> ACis ¼ DRis  li ð4Þ<br /> <br /> 3.3.2. Aggregation of abnormal changes<br /> In order to draw overall inferences for the event of interest, the abnormal change observations must be<br /> aggregated across the days within the event window. Let s2, s1, s+1, and s+2 represent the 4 days<br /> surrounding the day, (s), of the operational rate change. We then define ACin as the cumulative abnormal<br /> change for market rate i on the nth operational change, given by:<br /> <br /> X<br /> sþ2<br /> ACin ¼ ACis ð5Þ<br /> s2<br /> <br /> Similarly, the average abnormal change on the nth event window is:<br /> <br /> PP 1 X<br /> sþ2<br /> ACin ¼ ACis ð6Þ<br /> 5 s2<br /> <br /> Since we are interested in evaluating the significance of the effect of policy rates on market rates as a<br /> whole, we also need to aggregate across event windows. Assuming N events, we calculate the<br /> cumulative abnormal change for market interest rate i across all policy changes:<br /> <br /> X<br /> N<br /> CACi ¼ ACin ð7Þ<br /> n¼1<br /> 494 A. Kaketsis, N. Sarantis / International Review of Economics and Finance 15 (2006) 487–504<br /> <br /> <br /> The average cumulative abnormal change for each market interest rate i across all operational rate<br /> changes is given by:<br /> PPP 1 XN<br /> PP<br /> CAC i ¼ ACin ð8Þ<br /> N n¼1<br /> 3.3.3. Hypothesis testing<br /> Under the null hypothesis, H0, that the event has no impact on market rates (i.e., the abnormal change<br /> is zero), the distribution properties of the abnormal changes can be used to draw inferences. Following<br /> MacKinlay (1997), we assume that the abnormal changes are distributed normally with a mean of zero. It<br /> is also assumed that they are independently and identically distributed. Under these assumptions, the<br /> abnormal changes can be aggregated over the days within each event window and across event windows<br /> to yield distributional assumptions for the average cumulative abnormal change.<br /> Hence the null hypothesis H0 can be tested using the statistic:<br /> PPP<br /> CACi a<br /> ffi f N ð0; 1Þ<br /> H ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð9Þ<br /> PPP<br /> varðCACi Þ<br />  PPP <br /> The traditional method used for measuring var CACi is the sum of the estimation period residual<br /> variances of the abnormal changes (Eq. (3)) (see Brown & Warner, 1980). But as Boehmer, Musumeci,<br /> and Poulsen (1991) argue, this method ignores the event-induced variance. Changes in policy can have<br /> in principle two kinds of influences on money market interest rates. One is an effect on the mean of the<br /> abnormal changes. The other is on the variance of the abnormal changes. Dale (1993) tests only for a<br /> mean effect. In null hypothesis H0, either a mean or a variance effect will reject the null of no significant<br /> impact of policy changes on market rates. However, when analysing the policy impact, we are interested<br /> in testing for a mean effect on the abnormal changes. Thus we must expand the null hypothesis to allow<br /> for a variance effect by the event. If changes in the operational rates of the Bank of Greece cause a<br /> variance effect, then a measure based on r g2 iv(Eq. (3)) is not a consistent estimator of the variance of the<br /> event window. Boehmer et al. suggest to eliminate the reliance of the null hypothesis on the estimates<br /> obtained from the estimation window and to rely instead on an estimate of the variance of cumulative<br /> abnormal changes. Based on simulations presented in their paper, the authors argue that the proposed<br /> adjustment results in equally powerful tests when the null is false and appropriate rejection rates when it<br /> is true. Therefore, to take account of the potential variance effect, we use the following estimator for the<br /> variance of the average cumulative abnormal changes:<br /> PPP 1 XN<br /> PP<br /> varðCACi Þ ¼ ½ACin  ACin 2 ð10Þ<br /> N ðN  1Þ n¼1<br /> <br /> Moreover, as mentioned in Boehmer et al. (1991), the above methodology works best when the constant-<br /> change model is used, as in the present paper.<br /> <br /> 3.4. Measuring daily significance<br /> <br /> A problem with the above method is that it may create a bias in the determination of the response of<br /> market rates. The reason is that the above method assesses the significance of the event window as a<br /> A. Kaketsis, N. Sarantis / International Review of Economics and Finance 15 (2006) 487–504 495<br /> <br /> <br /> whole. However, it does not scrutinise the individual responses in specific days within the event window.<br /> In particular, although the change in the policy rate may have significant effects across the 5-day event<br /> window, the influence may not be statistically significant on all the days in the event window. For<br /> instance, the response 2 days before the operational rate change may be significant, but the response 1<br /> day after may not. The above methodology can be easily adjusted to test whether the coefficients on<br /> particular days within the event window are significant. In order to do this, we aggregate only across<br /> event windows, not within event windows. Thus Eq. (4) is replaced with:<br /> ACiqn ¼ DRiq  li ð11Þ<br /> where q=s2, s1, s; s+1, and s+2.<br /> The average cumulative abnormal change in market rate i on the individual days within the event<br /> window is given by:<br /> PPP 1 XN<br /> CACiq ¼ ACiqn ð12Þ<br /> N n¼1<br /> Using the results from Eq. (12), we can then calculate the significance levels for individual days using<br /> the variance estimator (Eq. (10)), which takes into account the variance effect that policy changes have<br /> on market rate changes.<br /> <br /> <br /> 4. Estimation period and data<br /> <br /> As explained in Section 2, the Bank of Greece implemented two distinct operational procedures during<br /> our sample period. Hence our study will cover two estimation periods corresponding to the different<br /> operational procedures. The first procedure spans the March 1994–March 1997 period. As is to be<br /> expected, the interbank market did not function properly in the first year. This is because the dere-<br /> gulation of the financial sector was not yet complete.14 Thus, the study will examine the effects of the<br /> intervention rate over the period May 1995–March 1997. The second sample investigates the reaction of<br /> money market rates to the official interest rate changes over the April 1997–April 2000 period.<br /> We use daily data obtained from the Bank of Greece. For the money market rates we use the overnight<br /> and 1-, 2-, 3-, 6-, 9-, and 12-month Athibor rates. These consist of averages of the money market rates<br /> quoted by banks. For the second part of the sample (April 1997–April 2000), a long-term rate has also<br /> been included. Unlike the earlier period, by 1998, the government bond rates were set by market forces.<br /> The rates were no longer administratively set and banks were not legislatively obliged to hold<br /> government paper.<br /> As has been explained in Section 2, the bid rate will be taken as the operational rate of the Bank of<br /> Greece during the May 1995–March 1997 period. With the exception of two observations, all the 19<br /> changes in the bid rate that occurred in this period are used. On November 21, 1995 there were serious<br /> problems with the prime minister’s health and this caused a disproportionately large response that is an<br /> <br /> <br /> 14<br /> In particular, the rate on government bonds was still administratively set. Furthermore, in March 1994, full liberalisation in<br /> capital movements had not yet occurred.<br /> 496 A. Kaketsis, N. Sarantis / International Review of Economics and Finance 15 (2006) 487–504<br /> <br /> <br /> outlier. The second observation to be excluded is the market change on December 13. The reason is that<br /> it stands between two operational rate changes, on December 11 and 15. Hence using it will be double<br /> counting, since it would represent both a reaction 2 days after the change on December 11, and an<br /> anticipation 2 days before the change on December 15.<br /> Between April and October 1997, the operational rate was the two-trance system. During this period,<br /> it was changed three times. Between November 1997 and January 1998, the Asian crisis intervened. The<br /> drachma came under strong pressure, owing to substantial capital outflows at the end of October 1997,<br /> after the outbreak of monetary turmoil in the Asia markets. To support its exchange rate policy, the Bank<br /> of Greece used direct measures. Namely, it imposed a surcharge of 0.4% per day on the increase in the<br /> debit balances of commercial banks’ current accounts with the Central Bank. This is equivalent to an<br /> annual interest rate of 170%. Liquidity was also provided via the 14-day maturity. After the main impact<br /> of the crisis subsided, the bank initiated the 14-day reverse repo as the main operational tool.15 The first<br /> two open market operations were conducted on January 5 and 14, respectively. Both represented changes<br /> using 14 days as the operational rate. However, during this period, there was substantial exchange rate<br /> instability. This was due to two reasons: firstly, the rekindling of the Asian crisis and, secondly,<br /> uncertainty over the EMS entry of Greece (both concerning the date and the central parity of the drachma<br /> in entry). Therefore, these dates are not considered indicative of the effects of monetary policy and,<br /> consequently, are not included in the estimation. The sample includes the other 24 changes that occurred<br /> in this period.<br /> <br /> <br /> 5. Empirical results<br /> <br /> 5.1. Mean responses<br /> <br /> In Greece, market interest rate responses to operational rate changes display huge variability. A look<br /> at the plots of the proportional series for the seven money market interest rates across the 43 policy<br /> changes, in Figs. 1 and 2, suggests that the responses are event specific. The plots of the proportional<br /> series highlight the sharp fluctuations in the reaction of market interest rates across the different policy<br /> changes. This would make regression analysis using Eq. (1) unwise. Therefore, we will follow Dale<br /> (1993) in considering instead mean changes in the two interest rates. In the examination of the impact of<br /> changes in the Bank of Greece’s intervention rate, we shall calculate the change in the market rate as a<br /> percentage of the change in the intervention rate. Hence, a resulting figure that is greater than 100%<br /> implies that markets overreact. Conversely, a figure less than 100, but still positive, implies a partial<br /> reaction by the market. Finally, a figure less than zero implies a reaction by the market in the opposite<br /> direction to the change in the intervention rate.<br /> Plotting each proportional official rate change individually gives an idea of the variance in the<br /> market’s reaction and the extent that such reactions are event specific. The results for Greece support<br /> much variation in market reactions. Negative reactions or large positive reactions are not uncommon.<br /> Negative reactions may occur when the change was already anticipated by the market, but the actual<br /> change was smaller than what the market had anticipated. This would lead to a market correction on the<br /> actual day of change that will appear as a negative reaction empirically. Large positive reactions could<br /> 15<br /> Interventions in the repo definition.<br /> A. Kaketsis, N. Sarantis / International Review of Economics and Finance 15 (2006) 487–504 497<br /> <br /> <br /> D1M(t) D2M(t)<br /> 2.5 D3M(t) D6M(t)<br /> D9M(t) D12M(t)<br /> <br /> 2<br /> <br /> <br /> 1.5<br /> <br /> <br /> 1<br /> <br /> <br /> .5<br /> <br /> <br /> 0<br /> <br /> <br /> -.5<br /> <br /> <br /> -1<br /> <br /> <br /> <br /> 0 5 10 15 20 25 30 35 40<br /> Policy Changes<br /> <br /> Fig. 1. Proportional responses: event day responses. D1M—change in the 1-month athibor; D2M—change in the 2-month<br /> athibor; D3M—change in the 3-month athibor; D6M—change in the 6-month athibor; D9M—change in the 9-month athibor;<br /> D12M—change in the 12-month athibor.<br /> <br /> <br /> <br /> occur, for instance, if the interest rate change generates expectations of further changes in the future.<br /> Taking mean changes at each maturity provides information of how the markets react in general to<br /> interest rate changes.<br /> The mean responses for the two sample periods are reported in Tables 1–4. For both periods we notice<br /> large anticipation responses. Learning responses are also substantial for the first period, but not for the<br /> second sample period. Looking at cumulative responses, there seems to be an overreaction in the first<br /> period, but underreaction in the second period.<br /> <br /> 5.2. Overall significance levels<br /> <br /> Having obtained estimates of the response of money market rates to changes in Central Bank official<br /> interest rate changes, one is naturally interested whether these are significant. Following the method<br /> outlined in Section 3.3, we evaluated the significance of the results across the event windows. As Tables<br /> 5 and 6 show, the responses across the event windows are significant.<br /> In the first period of analysis, the h statistics strongly reject the null hypothesis that official interest<br /> rate changes had no effect on market rates for all maturities (except for the 12-month rate). In the<br /> second sample period, the variances are considerably larger, although the responses of market rates to<br /> official interest rate changes are still significant (although not as significant as for the first period) for<br /> all maturities except for the overnight and the 10-year rates. There are two potential explanations for<br /> this result in the second sample period. Firstly, the bank’s presence in the market was reduced. The<br /> bank’s interventions were instead through the regular weekly 14-day repo rates. As a result, the<br /> 498 A. Kaketsis, N. Sarantis / International Review of Economics and Finance 15 (2006) 487–504<br /> <br /> <br /> DOVERN(t)<br /> 20<br /> 10 DOVERN(t-2)<br /> <br /> 0 10<br /> -10<br /> 0<br /> -20<br /> <br /> 0 10 20 30 40 0 10 20 30 40<br /> 5<br /> DOVERN(t-1) 10 DOVERN(t+1)<br /> <br /> 0<br /> <br /> -5 0<br /> <br /> -10<br /> <br /> 0 10 20 30 40 0 10 20 30 40<br /> DOVERN(t+2)<br /> 10<br /> <br /> 5<br /> <br /> 0<br /> <br /> <br /> 0 10 20 30 40<br /> <br /> Fig. 2. All the proportional responses for the overnight rate. t= event day response; t+1 and t+2=delayed responses; t1 and<br /> t2=anticipation effects.<br /> <br /> <br /> variance of market rates increased. Second, this was a period of significant turbulence associated with<br /> the Asian financial crisis.<br /> <br /> 5.3. Individual day significance levels<br /> <br /> The above methodology may overestimate the reaction of market rates. Although particular responses<br /> within the event window may be insignificant, this is hidden, since one is testing for the overall<br /> significance. Hence, using the methodology described in Section 3.4, we will measure the day-by-day<br /> significance levels. Tables 7 and 8 report the h statistics regarding the significance levels, which are<br /> computed using the estimate of the variance obtained by using Eq. (10).<br /> There is clearly a difference in the response observed between the two periods. It is evident that in<br /> both periods there is a difference between the reaction of the overnight rate and that of the longer money<br /> market maturities. In the first period, the overnight rate reacts strongly negatively in the days before the<br /> <br /> <br /> Table 1<br /> Mean responses in the days surrounding the policy change: May 1995–March 1997<br /> Overnight 1-month 2-month 3-month 6-month 9-month 12-month<br /> rate rate rate rate rate rate rate<br /> 2 days before 31.07 37.09 36.83 35.27 30.60 41.49 46.9<br /> 1 day before 15.47 20.75 21.48 25.14 25.14 19.19 25.49<br /> Day of change 99.95 37.83 40.76 38.32 36.37 32.16 29.56<br /> 1 day after 39.96 40.27 39.30 34.66 38.32 43.05 40.46<br /> 2 days after 6.30 5.26 6.09 9.68 6.09 11.25 0<br /> A. Kaketsis, N. Sarantis / International Review of Economics and Finance 15 (2006) 487–504 499<br /> <br /> <br /> Table 2<br /> Cumulative responses in the days surrounding the policy change: May 1995–March 1997<br /> Overnight 1-month 2-month 3-month 6-month 9-month 12-month<br /> rate rate rate rate rate rate rate<br /> 2 days before 31.07 37.09 36.83 35.27 30.60 41.49 46.83<br /> 1 day before 15.59 57.83 58.30 60.41 55.74 60.68 72.32<br /> Day of change 84.36 95.66 99.07 98.73 92.11 92.84 101.89<br /> 1 day after 124.31 135.94 138.36 133.39 130.43 135.89 142.34<br /> 2 days after 130.62 141.20 144.45 143.07 136.52 147.15 142.34<br /> <br /> <br /> <br /> policy change, and subsequently overreacts. In the second period, the large negative reaction occurs on<br /> the event day. Reading through the financial press, it is noted that in the first period the bank used to dry<br /> up the market excessively in the days before policy rate cuts. As a result, the overnight rate jumped up.<br /> This fact may also help explain the significant anticipation effect. In the second period, interventions<br /> occurred only once a week under normal circumstances. Hence on the day the reverse repo was held, the<br /> bank swiped up all the liquidity. Consequently the overnight jumped up to balance the market for high-<br /> powered money. Moreover, looking at the plots of proportional responses in Figs. 1 and 2, it is clear that<br /> after the 17th observation when the second sample begins, the proportional reactions of the overnight<br /> rate are larger and more oscillatory. This conforms with our expectations. Since the bank moved to a<br /> longer maturity in the second period and no longer intervened on a daily basis, it controlled the overnight<br /> rate far less closely.<br /> With regards to the behaviour of the longer maturities, the results on Table 7 suggest that there was a<br /> highly significant anticipation effect in the first period. There are systematic movements in markets<br /> leading up to the change, indicating that policy moves were broadly anticipated over this period. More<br /> than half of the change in the intervention rate was on average anticipated by the markets. By the day of<br /> the change in policy, the change has been fully discounted, since the cumulative change by time t is<br /> between 92% and 99% for maturities 1–6. Moreover, market rates display significant learning effects,<br /> but only for 1 day after the policy change. Similar results were found for the UK (Dale, 1993) and the<br /> United States (Cook & Hahn, 1989). In Greece one would expect these reactions to be pronounced, since<br /> the money markets had only been recently established. Markets lacked experience with market<br /> determined interest rates, and were inexperienced in interpreting the future policy intentions of the<br /> central bank from current policy changes. The results for the 9- and 12-month maturities are similar, but<br /> react less strongly on average. However, they should be viewed with skepticism, since the 9- and 12-<br /> <br /> <br /> Table 3<br /> Mean responses in the days surrounding the policy change: April 1997–April 2000<br /> Overnight 1-month 2-month 3-month 6-month 9-month 12-month 10-year<br /> rate rate rate rate rate rate rate rate<br /> 2 days before 81.81 32.08 13.72 30.53 31.69 21.64 28.22 18.04<br /> 1 day before 3.71 26.96 45.89 28.41 26.38 31.50 27.83 8.70<br /> Day of change 86.90 13.80 15.74 18.06 17.50 13.80 8.70 0.22<br /> 1 day after 137.29 15.07 7.44 6.18 5.80 6.67 6.18 7.42<br /> 2 days after 59.50 1.35 1.26 1.74 3.77 2.03 0.48 2.02<br /> 500 A. Kaketsis, N. Sarantis / International Review of Economics and Finance 15 (2006) 487–504<br /> <br /> <br /> Table 4<br /> Cumulative responses in the days surrounding the policy change: April 1997–April 2000<br /> Overnight 1-month 2-month 3-month 6-month 9-month 12-month 10-year<br /> rate rate rate rate rate rate rate rate<br /> 2 days before 81.81 32.08 13.72 30.53 31.69 21.64 28.21 18.04<br /> 1 day before 78.10 59.03 59.61 58.94 58.07 53.14 56.04 26.74<br /> Day of change 8.80 72.83 75.35 77 75.57 66.94 64.74 26.52<br /> 1 day after 128.48 87.90 82.79 83.18 81.36 73.60 70.93 33.95<br /> 2 days after 187.98 89.26 84.05 84.92 85.13 75.63 71.41 35.96<br /> <br /> <br /> <br /> <br /> month maturities were not introduced in the Greek interbank market until the beginning of 1996. Thus,<br /> very few observations were available. Overall, all market rates overreacted to the policy change, except<br /> the 12-month rate.<br /> The second and first periods share similar strong anticipation effects. Again, markets discount more<br /> than half of the operational rate change. However, the difference is that the cumulative response is<br /> smaller. With the exception of the overnight rate, we do not observe any significant learning effects (see<br /> Table 8). This may be for two reasons. Firstly agents had time to get accustomed with market<br /> determined interest rates. Moreover, experience was built in interpreting the central bank moves. Also,<br /> one could argue that the switch to the regular more thinly conducted open market operations as well as<br /> the independence of the central bank made policy more transparent, thus markets did not have to digest<br /> information after the changes in policy. The second is related to the different inflation environment,<br /> with inflation rates being lower in the second period due to the efforts by the government and Bank of<br /> Greece to meet the Maastricht convergence criteria. Another important difference from the first<br /> operational period is that all market rates underreacted, with the significant cumulative changes being<br /> well below 100%.<br /> One can also observe differences along the term structure between the two periods. In the first<br /> period, cumulative responses were fairly uniform across the maturity spectrum (see Tables 2 and 7). In<br /> the second period, however, we observe a pronounced decline in responses along the maturity<br /> spectrum (see Tables 4 and 8). Moreover, we also observe a decline in significance along the maturity<br /> spectrum. This highlights the need to look at individual days within the event window. For instance,<br /> <br /> <br /> Table 5<br /> Statistical results across the event windows: May 1995–March 1997<br /> Overnight 1-month 2-month 3-month 6-month 9-month 12-month<br /> rate rate rate rate rate rate rate<br /> Normal change (l i )_ 0.003 0.006 0.007 0.009 0.010 0.009 0.009<br /> PPP<br /> CACi 0.3093 0.326 0.322 0.307 0.288 0.137 0.132<br /> rˆ 0.0599 0.1031 0.099 0.0983 0.0874 0.0657 0.0833<br /> h Statistic 5.165* 3.161* 3.252* 3.122* 3.297* 2.084* 1.585<br /> rˆ =The sample standard error obtained using the estimate of the variance of cumulative abnormal changes for market rate i<br /> (Eq. (10)).<br /> h Statistic is computed using rˆ.<br /> * Statistically significant coefficients.<br /> A. Kaketsis, N. Sarantis / International Review of Economics and Finance 15 (2006) 487–504 501<br /> <br /> <br /> Table 6<br /> Statistical results across the event windows: April 1997–April 2000<br /> Overnight 1-month 2-month 3-month 6-month 9-month 12-month 10-year<br /> rate rate rate rate rate rate rate rate<br /> Normal change (l i )_ 0.021 0.008 0.008 0.008 0.009 0.010 0.011 0.004<br /> PPP<br /> CACi 0.517 0.372 0.354 0.352 0.340 0.288 0.265 0.140<br /> rˆ 0.592 0.163 0.151 0.149 0.141 0.138 0.137 0.093<br /> h Statistic 0.875 2.282* 2.344* 2.362* 2.411* 2.072* 1.934* 1.505<br /> * Statistically significant coefficients.<br /> <br /> <br /> <br /> <br /> from Table 5, one would conclude by looking at the h statistic that the reaction of the 12-month rate is<br /> not significant. Similar conclusions would be made for the 10-year rate from Table 6. However, these<br /> would be misleading. By checking individual significance levels, we can obtain the significant<br /> component of the reaction, reported in Tables 7 and 8. For the 10-year rate for instance, the significant<br /> element in the response over the event window is one quarter of the change in the operational rate,<br /> down from one-third. Similarly, one could conclude that the 12-month rate in the first sample period<br /> underreacted to changes in the operational rate. However, both for the 12-month as well as for the 9-<br /> month maturities there are few observations available, so the results should be interpreted with<br /> caution.<br /> How do the cumulative reactions obtained for Greece compare to those reported for other countries?<br /> In the first sample period, cumulative responses were fairly uniform across the maturity spectrum, which<br /> contrasts with the evidence for other countries. But in the second period, the cumulative response is<br /> closer to the response observed in the United States (Cook & Hahn, 1989; Thornton, 1998), Germany<br /> (Deutsche Bundesbank, 1996; Hardy, 1998), the UK (Dale, 1993), and Denmark (Pedersen, 1997). We<br /> observe that the common empirical finding of a pronounced decline in responses along the maturity<br /> spectrum is also occurring in Greece.<br /> After entering a lower inflation environment as well as a period of higher credibility towards the end<br /> of the 1990s, the responses of the Greek money market rates seem to match the other countries in the<br /> European Union. This is in line with Buttiglione et al.’s (1997) empirical findings which suggested that<br /> the inflation environment, the credibility of the central bank, as well the state of public finances were<br /> factors found to be closely related to differences and similarities in money market responses to Central<br /> <br /> <br /> <br /> Table 7<br /> h Statistics, indicating the individual day significance in the days surrounding the policy change: May 1995–March 1997<br /> Overnight 1-month 2-month 3-month 6-month 9-month 12-month<br /> rate rate rate rate rate rate rate<br /> 2 days before 6.17* 3.77* 3.85* 3.63* 3.41* 2.92* 2.27*<br /> 1 day before 2.83* 2.11* 2.24* 2.64* 2.90* 0.99 1.00<br /> Day of change 19.83* 4.11* 4.59* 4.26* 4.45* 2.11* 1.50<br /> 1 day after 7.76* 4.40* 4.41* 3.81* 4.72* 3.05* 2.24*<br /> 2 days after 0.84 0.23 0.28 0.61 0.18 0.19 0.52<br /> * Statistically significant coefficients.<br /> 502 A. Kaketsis, N. Sarantis / International Review of Economics and Finance 15 (2006) 487–504<br /> <br /> <br /> Table 8<br /> h Statistics, indicating the individual day significance in the days surrounding the policy change: April 1997–April 2000<br /> Overnight 1-month 2-month 3-month 6-month 9-month 12-month 10-year<br /> rate rate rate rate rate rate rate rate<br /> 2 days before 2.11* 3.53* 1.89* 3.63* 4.14* 2.77* 3.78* 3.77*<br /> 1 day before 0.23 3.22* 6.08* 3.68* 3.57* 4.39* 3.83* 1.79*<br /> Day of change 3.17* 1.78* 1.99* 2.33* 2.36* 1.80* 0.97 0.27<br /> 1 day after 4.05* 1.77* 0.42 0.26 0.34 0.54 0.45 1.40<br /> 2 days after 2.44* 0.02 0.038 0.01 0.26 0.00 0.30 0.30<br /> * Statistically significant coefficients.<br /> <br /> <br /> <br /> Bank rates. In the second sample period, the Greek economy displayed greater convergence to its EU<br /> partners on these factors.<br /> <br /> <br /> 6. Conclusions<br /> <br /> This paper has examined an important aspect of the monetary transmission mechanism in Greece<br /> during the transition period of the 1990s, when the operational procedures of the Bank of Greece<br /> underwent a number of major changes. The principal objectives of the research were: first, to provide an<br /> analytical account of the main features of the transition from a system of direct monetary controls to<br /> more indirect methods of conducting monetary policy, where the operating strategy is designed to<br /> influence the price of credit and markets have an important say. Second, to investigate the transmission<br /> process between the Bank of Greece’s operating interest rate instruments and the market interest rates at<br /> various maturities, by applying the event study methodology used in the field of finance.<br /> Our event study results suggest that changes in the official interest rates exert a significant influence<br /> on short-term and intermediate-term market interest rates, and that this relationship was affected by the<br /> changes in the Bank of Greece’s operational procedures during the 1990s. This is reflected in both the<br /> relative strength of anticipation and learning responses of market rates to policy changes, and in the<br /> responses across the maturity spectrum.<br /> It seems that the Greek money markets anticipate the bank’s moves and discount changes in official<br /> rates quickly. This indicates that markets quickly adjusted to a market based system where the central<br /> bank guides the markets through signals, rather than direct actions. We also found significant learning<br /> responses for the first part of our sample period, but not for the second period associated with the latest<br /> operational procedure adopted by the Bank of Greece. These empirical findings appear to suggest that<br /> increased policy transparency may have speeded up the transmission process. Importantly, in the second<br /> period, the response of Greek market interest rates seems to be closer to the response observed in other<br /> more advanced industrial countries. The common empirical finding of a pronounced decline in responses<br /> along the maturity spectrum is also observed in Greece.<br /> Our study could be extended in a number of useful ways. First, one could investigate the links<br /> further down the monetary transmission mechanism. However Greece is reaching the end of the<br /> transition process and another operational regime change might well apply as a result of entrance in<br /> the EMU. Second, it would be interesting to apply the event study methodology to other countries<br /> going through a similar process of changes in the operational procedures of their Central Bank, and in<br /> A. Kaketsis, N. Sarantis / International Review of Economics and Finance 15 (2006) 487–504 503<br /> <br /> <br /> particular to the Central and Eastern European Transition economies, which aim to join the European<br /> Union.<br /> <br /> <br /> Acknowledgements<br /> <br /> This paper was presented at the 2003 annual conference of the European Economics and Finance<br /> Society, University of Bologna, Bologna, Italy, May 2003. We are grateful to the participants of this<br /> conference and to two anonymous referees for their helpful comments. 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