Measuring the Impact of Monetary Policy on Asset Prices in Turkey

CENTRAL BANK OF THE REPUBLIC OF TURKEY WORKING PAPER NO: 10/17

Measuring the Impact of Monetary Policy on Asset Prices in Turkey

September 2010

Murat DURAN Gülserim ÖZCAN Pınar ÖZLÜ Deren ÜNALMIŞ

© Central Bank of the Republic of Turkey 2010 Address: Central Bank of the Republic of Turkey Head Office Research and Monetary Policy Department İstiklal Caddesi No: 10 Ulus, 06100 Ankara, Turkey Phone: +90 312 507 54 02 Facsimile: +90 312 507 57 33

The views expressed in this working paper are those of the author(s) and do not necessarily represent the official views of the Central Bank of the Republic of Turkey. The Working Paper Series are externally refereed. The refereeing process is managed by the Research and Monetary Policy Department.

Measuring The Impact of Monetary Policy on Asset Prices in Turkey1

Murat Durana, Gülserim Özcanb, Pınar Özlüa , Deren Ünalmışa a

Research and Monetary Policy Department, Central Bank of the Republic of Turkey b Department of Economics, Bilkent University September 2010

Abstract The transmission of policy decisions to financial markets is an integral part of the monetary transmission mechanism. However, one of the major problems in estimating the effect of monetary policy on asset prices is the simultaneous response of policy actions and the asset prices to each other. Rigobon and Sack (2004) suggest a heterokedasticity-based generalized method of moments (GMM) technique to overcome this problem. For emerging markets, there are very few studies using this method. This study applies the heteroskedasticity-based technique to estimate the impact of monetary policy on the Turkish bond, currency and stock markets. This technique also addresses the omitted variables problem. The empirical results verify the findings obtained by event study methods in earlier studies. Firstly, the impact of monetary policy on market interest rates is found to be positive, which diminishes with maturity for maturities longer than 9 months. Secondly, the results suggest that a rise in the policy rate leads to a moderate appreciation of the domestic currency, where the TL/EUR rate is affected more significantly compared to the TL/US dollar rate. Finally, the results show that an increase in the policy rate leads to a decline in stock prices, and monetary policy has the greatest impact on the share prices of the financial sector firms. Keywords: Monetary Policy; Asset Prices; Identification through Heteroskedasticity

JEL Classification: E43; E44; E52

1

This paper has benefited from Refet Gürkaynak’s comments and suggestions. We are grateful to Turalay Kenç and Soner Başkaya for helpful comments. We thank Roberto Rigobon for providing GAUSS codes. The views expressed in this paper do not necessarily represent those of the Central Bank of the Republic of Turkey. All remaining errors are ours.

1. Introduction The pass-through from policy rates to financial asset prices constitutes the first step of the monetary policy transmission mechanism. Changes in financial asset prices, in turn, affect investor and consumer decisions, which are essential components of economic activity and important determinants of inflation dynamics. Therefore, in order to formulate effective policy decisions, it is crucial for central banks to obtain reliable estimates of the reaction of asset prices to monetary policy. There are two major difficulties in the measurement of the reaction of asset prices to monetary policy. First, while the asset prices are affected by the monetary policy decisions, the policy rate may also respond to changes in the asset prices. Second, some common factors, such as macroeconomic outlook and changes in risk preferences, can simultaneously affect both policy decisions and asset prices. Hence, measurement of the reaction of asset prices to the policy changes is complicated due to endogeneity and omitted variables bias problems. In the literature, to overcome these problems, the most commonly adopted estimation method is the event study (ES) approach.2 Rigobon and Sack (2004) (henceforth, RS) develop the heteroskedasticitybased generalized method of moments (GMM) technique and instrumental variables method (IV) as alternatives to the ES approach. These methods are considered more reliable as they require weaker assumptions compared to the ES approach. 3 The results from the heteroskedasticity-based GMM estimation in RS suggest a significant negative impact of monetary policy on stock indices and a significant positive impact on longer-term market interest rates in the US.4 Recently, an increasing number of studies have investigated the impact of monetary policy on asset prices using the heteroskedasticity-based methods and find similar results with RS. For stock and bond markets Ehrmann et al. (2005) analyze the 2

This method basically compares asset prices immediately after monetary policy announcements with those immediately before, and attributes the changes to monetary policy surprises. For details and two notable examples using the ES approach, see Kuttner (2001) and Gürkaynak et al. (2005). 3 Besides, as indicated in RS, the GMM method is theoretically more efficient than the IV method. The heteroskedasticity-based GMM technique is explained in detail in the next section. 4 The related literature finds similar results for the responses of asset prices. Findings on the impact of the policy rate on the long-term interest rates generally suggest a positive relationship (Ellingsen and Soderstrom 2001; Gürkaynak et al. 2005; Ehrmann et al. 2005). On the other hand, studies on stock prices generally conclude that the impact of the policy rates on stock markets is negative (Bernanke and Kuttner 2005; Ehrmann et al. 2005; Bohl et al. 2008; Kholodilin et al. 2009). Studies on the exchange rate market generally find that the monetary policy tightening leads to an appreciation of the domestic currency (Ehrmann et al. 2005).

1

degree of financial transmission between money, bond, equity and foreign exchange markets within and between the United States and the Euro Area. Bohl et al. (2008) investigate the impact of the European Central Bank decisions on the equity indices of the largest four European countries while Kholodilin et al. (2009) use the consolidated equity indices for all the European countries and different from Bohl et al (2008), analyze the sub-sectors.5 Studies using the methods developed by RS as an alternative to the ES approach are not available for Turkey and very rare for other emerging markets.6,7 The goal of this study is to measure the response of asset prices to monetary policy in Turkey, using the heteroskedasticity-based GMM method suggested by RS. RS indicates that the heteroskedasticity-based GMM method is theoretically equivalent to the heteroskedasticity-based IV method, and the GMM method is theoretically more efficient than the IV method since it incorporates all the moment conditions into the estimation. For that reason, this study reports only the GMM estimation results.8 With this identification approach, the change in the variance of the policy shock on policy dates allows us to measure the response of asset prices to the policy change with a rather weak set of assumptions compared to the strict assumptions required by the ES approach. The plan of the remainder of the paper is as follows. We briefly present the methods employed in Section 2. Section 3 describes the data. We discuss the empirical evidence in Section 4 and finally Section 5 concludes. 2. Methodology RS points out two main problems in estimating the relation between monetary policy rate and an asset price. The first one is that both variables simultaneously respond to each other. Monetary policy may also respond to asset prices since changes in the asset prices reflect information about the expectations on policy rate and inflation. The other problem is that both short-term rate and other asset prices may 5

Ehrmannn et al. (2005) use the heteroskedasticity-based GMM technique, Bohl et al. (2008) use the heteroskedasticity-based IV method while Khodolin et al. (2008) implements both methods. 6 However, in a parallel study, Duran et al. (2010) focuses on the stock markets in Turkey. Rezessy (2005) and Goncalves and Guimaraes (2007) apply the heteroskedasticity-based methodology to the asset prices in Hungary and Brazil, respectively. 7 See Aktaş et al. (2009), which is the only study that provides a comprehensive analysis on the impact of monetary policy in Turkey using the ES approach. 8 The IV results are not reported here, but available from authors on request. The results from the IV method are similar to the ones obtained by the GMM.

2

respond to common factors such as changes in macroeconomic outlook and the risk premium. RS suggests a new methodology to circumvent both the endogeneity and the omitted variable problems in an efficient way. Formally, the dynamics of the short-term interest rate and asset prices are written as follows: ∆it = β∆st + γz t + ε t

(1)

∆st = α∆it + z t + η t

(2)

where ∆it is the change in the policy rate and ∆st is the change in the asset price. Equation (1) can be interpreted as a monetary policy reaction function, where the policy rate responds to the asset price and a set of variables z t , which may or may not be observed. Equation (2) represents the asset price equation, which captures the response of asset price to the monetary policy and other variables z t . In our setup, z t is taken as a single unobservable variable, which represents all the omitted common factors in both equations. Since zt is an unobservable variable, its coefficient is normalized to one in Equation (2). The setup is flexible enough to include observable common factors as well. The variable ε t is the monetary policy shock and η t is the asset price shock. The shocks ε t and η t are assumed to be serially uncorrelated and to be uncorrelated with each other and with the common shock z t . In this paper, the parameter of interest is α , which measures the impact of a change in the policy rate ∆it on the change in the asset price ∆st . The ES approach is to estimate Equation (2) with OLS. Therefore, the ES estimate of α is as follows:

αˆ ES = (∆it ' ∆it ) −1 ∆it ' ∆st

(3)

The mean of αˆ ES is: E (αˆ ES ) = α + (1 − αβ )

βσ η + ( β + γ )σ z σ ε + β 2σ η + ( β + γ ) 2 σ z

(4)

where E(.) is the expectation operator and σ x represents the variance of shock x. According to Equation (4), estimating Equation (2) with OLS may suffer from both

3

the presence of simultaneity bias (if β ≠ 0 and σ η > 0) and omitted variables bias (if

γ ≠ 0 and σ z > 0). To overcome these problems, researchers applying the ES approach use the asset price changes directly after the announcement of the monetary policy committee (MPC) decision. In that case, the assumptions required by the ES approach is that in the limit, the variance of the policy shock becomes infinitely large relative to the variance of other shocks, that is σ ε σ η → ∞ and σ ε σ z → ∞ on policy dates. That is, it is assumed that within the policy day, the effects of the asset price shock and the common shock (simultaneity and omitted variables problems) on the monetary policy decision are negligible. The heteroskedasticity-based identification method suggested by RS does not require such strong assumptions. To apply the heteroskedasticity-based identification method, we only need to observe a rise in the variance of the policy shock when the MPC decision is announced, while the variances of other shocks remain constant. This enables establishing causality from policy to the asset price on the policy dates, which is the basis for identification. Since the GMM technique requires much weaker assumptions, it can give more reliable estimates than the ES approach. Two subsamples, denoted by P and N are essential to implement the GMM technique. P stands for the policy dates (days when the MPC decisions are announced) and N stands for the non-policy dates (days immediately preceding the policy days). There are two assumptions for the heteroskedasticity-based identification method as follows: (i) The parameters of the model, α , β and γ are stable across the two subsamples. (ii) The policy shock is heteroskedastic and the other shocks are homoskedastic, which are represented by the following equations:

σε P > σε N

(5)

σ zP = σ zN

(6)

ση P = ση N

(7)

4

Under

the

assumptions

(i)

and

(ii),

a

detailed

analysis

of

the

heteroskedasticity-based identification approach is presented below. Reduced form equations for (1) and (2) are as follows: ∆it =

∆s t =

1 1−α β 1 1−α β

[( β + γ ) zt + βη t + ε t ]

(1’)

[(1 + αβ ) z t + η t + αε t ]

(2’)

The covariance matrices of the variables in each subsample are the following:

ΩP =

σ ε P + ( β + γ ) 2 σ z P + β 2σ η P  (1 − αβ ) 2  .

ασ ε P + ( β + γ )(1 + αγ )σ z P + βσ η P   α 2σ ε P + (1 + αγ ) 2 σ z P + σ η P 

ΩN =

σ ε N + ( β + γ ) 2 σ z N + β 2σ η N  (1 − αβ ) 2  .

ασ ε N + ( β + γ )(1 + αγ )σ z N + βσ η N   α 2σ ε N + (1 + αγ ) 2 σ z N + σ η N 

1

1

The heteroskedasticity-based GMM technique uses a comparison of the covariance matrices on the policy and the non-policy dates.9 Under the assumptions (i) and (ii) of the model, the difference in the covariance matrices Ω P and Ω N is as follows: (σ ε − σ ε )  1 α    (1 − αβ ) 2 α α 2  P

∆Ω = Ω P − Ω N =

P

Denoting λ =

N

(8)

N

(σ ε − σ ε ) , (8) becomes the following: (1 − αβ ) 2 1 α  ∆Ω = λ  2 α α 

(8´)

Thus, the impact of policy changes on the asset prices, namely the parameter

α , can be identified from the change in the covariance matrix ∆Ω .

9

For details of the heteroskedasticity-based identification methods, see Rigobon (2003).

5

In RS, the coefficient α is estimated in two different ways: by GMM estimation and IV regression. However, as shown in RS, IV estimation makes use of only two equations in (8´) at a time, resulting in multiple estimates of α . On the other hand, GMM utilizes all three orthogonality conditions in (8´). That is, there is an improvement in efficiency from incorporating the additional moment conditions into the estimation in the GMM approach compared to the IV approach. Thus, in this paper, GMM estimation will be used to obtain an estimate of the asset price response to the monetary policy changes. Besides, in the GMM approach, the overidentification restrictions enable us to test the model as a whole. 2.a Implementation Through GMM

There are two parameters to be estimated, namely; α , the parameter of P

interest, and λ =

N

(σ ε − σ ε ) , a measure of the degree of heteroskedasticity that is (1 − αβ ) 2

present in the data. This coefficient can be used to test whether the change in the volatility is enough to identify parameter α . Hence, in order to estimate α with this approach, we need λ to be statistically significant. Under assumptions (i) and (ii) of the heteroskedasticity-based identification, the sample estimate of the difference in the covariance matrix is:

ˆ =Ω ˆ −Ω ˆ ∆Ω P N

(9)

where ˆ = 1 Ω j Tj

∑ δ [∆i

∆st ] [∆it '

j

t

t

∆st ] for j = P, N

t∈T

and δ tj are dummy variables taking on the value 1 for the days in each subsample and

T j = ∑t∈(1,T ) δ tj are sample sizes of the subsamples, for j = P, N . The assumptions imply that the following moment conditions hold: E [bt ] = 0 where

6

ˆ − ∆Ω) , or bt = vech(∆Ω  T P T N  bt = vech  P δ t − N δ t [∆it T   T The limT →∞

GMM

estimator

∆st ] [∆it '

is

based

 ' ∆st ] − λ [1 α ] [1 α ]  on

the

condition

that

1 b = 0 . The intuition behind GMM is to choose an estimator for ∑ T t∈(1,T ) t

ˆ , that sets the three sample moments as close to zero as possible. Since there ∆Ω , ∆Ω

are more moment conditions than unknowns, (8´) is overidentified and it may not be possible to find an estimator setting all three moment conditions to exactly zero. In this case we take a 3X3 weighting matrix W3 and use it to construct a quadratic form in the moment conditions. The estimates of α and λ will be obtained by minimizing the following loss function:

[αˆ

'

    ˆ GMM , λ = arg min  ∑ bt  W3  ∑ bt  t∈[1,T ]  t∈[1,T ] 

]

(10)

Practically, GMM estimation proceeds in two steps. Initially GMM estimation with an identity-weighting matrix, i.e. taking W3 = I3, is conducted to obtain a consistent estimator of coefficients. In the second step, W3 is formed based on obtained residuals. Accordingly, W3 the optimal weighting matrix equal to the inverse of the estimated covariance matrix of the moment conditions is obtained. The efficient GMM estimator is obtained based on (10). 3. Data

Market interest rates are the yields on government bonds traded in Istanbul Stock Exchange (ISE) secondary market. These series are not available in a regular time-series format across each maturity and hence we use yield curve forecasts calculated using daily ISE data for various maturities. The policy rate is proxied by the yield on government bonds with one-month maturity, which is traded in a relatively more liquid market among the other alternative short rates. 10 Another advantage of using the one-month yield on the government bonds is that the results 10

The transactions of bonds and bills in ISE with the same value date close at 2 pm each day. To ensure time consistency, the close prices of the first session of the ISE are used.

7

can be comparable with other studies analyzing the impact of monetary policy for Turkey. We take ISE All, ISE 100, ISE 30 and the indices of the manufacturing, services, trade, financial and IT sectors, among the various the ISE stock indices. The TRL/USD and the TRL/EUR exchange rates are taken from the Datastream. The market rates are constructed as the daily changes of the interest rate series in basis points while the stock indices and the exchange rates are the daily percentage changes. While the ES methodology uses only changes in the asset prices on policy dates, the heteroskedasticity-based GMM estimates compare the changes in asset prices before and after the announcement of the policy decision. The sample covers the 2005-2009 period with sixty policy decisions. With the adopt of inflation targeting in 2005, we expect an increase in the influence of monetary policy over the financial markets. The data are plotted in levels in Figures 1-3. The longer-term interest rates seem to move generally in line with the short-term rate and the policy rate (Figure 1). There is no clear relationship observed over time between the short-rate and the exchange rate (Figure 2). The major stock market index, ISE All, generally moves in opposite direction with the short-rate (Figure 3). [Figure 1 around here] [Figure 2 around here] [Figure 3 around here] The descriptive statistics for the daily changes of the policy rate and asset prices are reported in Table 1. As expected, the standard deviation of the policy rate increases significantly on the policy date. The positive relationship between the policy rate and the market rates strengthen when the policy shock arrives on policy dates. While the correlations are between 0.21 and 0.38 before the arrival of the policy shock, they increase to the range between 0.59 and 0.77 after the policy announcement. The correlations between the exchange rates and the policy rate also seem to differ, and change sign on policy dates. Although the correlations with the policy rate were positive during non-policy dates (0.20 for the dollar rate and 0.43 for the euro rate), they turn to negative during the policy dates (-0.09 for the dollar rate and -0.21 for the euro rate). This might be due to the fact that, on the policy dates, the impact of the policy on the currency dominates the effect of the risk premium, making the 8

overall relationship negative. The policy rate and the stock indices are negatively correlated. Though the correlations are statistically insignificant and smaller in absolute value (in between 0.02 and 0.14) one day before the policy announcement, they become statistically significant and larger in absolute value in between 0.26 and 0.43 after the announcement of the policy decision. The fact that the interaction between the policy rate and the financial markets change considerably on the days when the policy shock arrives enables the parameter α to be estimated using the GMM method. [Table 1 around here] 4. Empirical Results

The estimates for the parameter α using both the ES approach and the heteroskedasticity-based GMM method are reported in Table 2. According to the GMM method, which is shown to be theoretically more reliable, yields on the government bonds with maturities ranging from 6 to 36 months respond to the change in the short-term interest rate significantly and in the same direction. At maturities longer than 9 months, the responses gradually decline; therefore when the policy rate increases, the yield curve becomes flatter (Figure 4). A 25 basis points increase in the short-term interest rate raises the 9-month rate by 44 basis points, benchmark rate by 15 basis points and the 36 month rate by 10 basis points. [Table 2 around here] [Figure 4 around here] Monetary policy changes do not have a large impact either on the change in the TRL/USD rate or on the change in the TRL/EUR rate. Even though the effect of the policy changes on TRL/EUR rate is found to be statistically significant, the estimated coefficient (0.997 in absolute value) is small in magnitude, suggesting that a one percentage point change in the unanticipated component of the policy rate causes only 0.997 % change in the Euro exchange rate in the opposite direction. The policy rate does not have a significant impact on the TRL/USD rate. Moreover, there is no cross currency effect, that is, the impacts on the Euro and the US Dollar rates are not statistically different. It is important to note that the estimated responses of exchange rates under the heteroskedasticity-based identification method, while still small, are larger (in absolute value) than the corresponding estimates under the ES approach.

9

The responses of various stock indices to a rise in the short-term rate are significant and negative. While the ISE Financials give the largest response, the least response is given by the trade sector under the ISE Services. According to the GMM estimates, a 25 basis points increase in the short-term interest rate decreases ISE All Shares by %0.85, ISE Financials by %0.99, ISE Industrials by %0.69, ISE Services by %0.65, and ISE IT by %0.61. The responses of various stock indices are different due to the differences in the interest rate sensitivity of the firms in different sectors. Financial sector firms are relatively more affected by the business cycles. Specifically, the firms in the financial sector carry a large amount of government bonds in their balance sheets, thus it is expected that they respond to the changes in the interest rates strongly. On the other hand, both the assets and the liabilities of the non-financial firms are less sensitive to the interest rate changes.11 [Table 3 around here] Contrary to Aktaş et al. (2009), this study finds significant estimates for stock index coefficients using the ES estimation. The reasons for this might be that this study covers more recent data and ensures time consistency by using the stock market data on the close of first session (see footnote 11). Different from Aktaş et al. (2009), we investigate not only the response of financial sector index, but also some other sectors. Moreover, the fact that the empirical findings are all in the same direction and quantitatively similar under both the ES and the GMM methods suggests that the results are statistically reliable. These results imply that the pass-through of policy rates to asset prices, the first step of monetary transmission, functions well in Turkey. The robustness checks for the estimates are reported in Table 3. The results of the tests confirm that the assumptions of the GMM method are more reliable. RS uses the t-statistic on the coefficient λ to test whether the change in the volatility on the policy date is satisfactory for the GMM estimation (see Equation 4). The t-statistics with respect to the related coefficient are depicted in the second column of the table, which show that the change in the volatility of the monetary policy shock is enough to identify α . The over identification test results, shown in the third column of Table 3, do not indicate an overidentification problem for any of the variables used. 11

See Özlü and Yalçın, 2010.

10

The difference between the ES estimates and the heteroskedasticity-based GMM method likely reflects a bias in the ES estimates. The significance of the potential biasedness in the ES approach, which is denoted by Equation (5), can be tested using a Hausman-type test. Whether the event-study estimates are biased compared to the GMM method is tested and reported in the last column of the table. The empirical results for the stock indices suggest that the ES estimates are not statistically biased compared to the GMM estimates. However, for the interest rates with 6 to 18 moths maturity, the ES estimates are found to be statistically biased. On the other hand, the ES estimates for longer-term interest rates do not exhibit significant bias. 5. Conclusion

This study estimates the impact of monetary policy on asset prices in Turkey using the heteroskedasticity-based GMM method suggested by Rigobon and Sack (2004), which takes into account both the simultaneity and the omitted variables problems. The empirical results are compared with the results using the most popular approach in the literature, the event study analysis. Both methods depend on an increase in the variance of the monetary policy shocks on days of MPC meetings. The results are in line with the literature. Increases in the policy rate lead to a decline in stock prices, rises in government bond yields with longer maturities, and an appreciation of the domestic currency. These findings suggest strong evidence that the first step of the monetary policy transmission mechanism works in Turkey.

11

References

Aktaş, Z., H. Alp, R. Gürkaynak, M. Kesriyeli and M. Orak. 2009. Türkiye’de para politikasının aktarımı: para politikasının mali piyasalara etkisi. İktisat, İşletme ve Finans. 24 (278), 9-24. Bernanke, B.S. and K.N. Kuttner. 2005. What explains the stock markets reaction to Federal Reserve policy? Journal of Finance, 60 (3), 1221-1257. Bohl, M.T., P.L. Siklos and D. Sondermann. 2007. European stock markets and the ECB's monetary policy surprises. International Finance. 11 (2), 117-130. Duran, M., P. Özlü and D. Ünalmış. 2010. TCMB faiz kararlarının hisse senedi piyasaları üzerine etkisi. Central Bank Review. 10 (2), 23-32. Ehrmann, M., M. Fratzscher and R. Rigobon. 2005. Stocks, bonds, money markets and exchange rates: Measuring international financial transmission. NBER Working Paper, No. 11166. Ellingsen, T. and U. Soderstrom. 2001. Monetary policy and market interest rates. American Economic Review, 91 (5), 1294-1607. Goncalves, C.E.S. and B. Guimaraes. 2007. Monetary policy, default risk and the exchange rate. CEPR Discussion Paper, No. 6501. Gürkaynak, R., B. Sack and E. Swanson. 2005. Do actions speak louder than words? International Journal of Central Banking, 1 (1), 55-93. Kholodilin, K., A. Montagnoli, O. Napolitano and B. Siliverstovs. 2009. Assessing the impact of the ECB’s monetary policy on the stock markets: A sectoral view. Economics Letters. 105, 211-213. Kuttner, K. 2001. Monetary policy surprises and interest rates: Evidence from the Fed funds futures market. Journal of Monetary Economics, 47 (3), 523-544. Özlü, P. and C. Yalçın. 2010. Firma ticari borçları ve kredi aktarım mekanizması. CBRT Economic Notes. No. 10/03. Rezessy, A. 2005. Estimating the immediate impact of monetary policy shocks on the exchange rate and other asset prices in Hungary. MNB Occasional Papers, 2005/38. Rigobon, R. 2003. Identification through heteroskedasticity. The Review of Economics and Statistics. 85 (4), 777-792. Rigobon, R. and B. Sack. 2003. Measuring the response of monetary policy to the stock market. Quarterly Journal of Economics. 118, 639-669. Rigobon, R. and B. Sack. 2004. The impact of monetary policy on asset prices. Journal of Monetary Economics. 51, 1553-1575.

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Table 1. The standard deviations and the correlations with the policy rate on the policy dates and the non-policy dates Standard Deviation

Policy Rate

Policy Dates

Non-Policy Dates

0.35

0.17

Correlation Policy Dates Non-Policy Dates -

-

Yields on Government Bonds with Different Maturities 6 months

0.35

0.19

0.74***

0.38***

9 months

0.51

0.20

0.77***

0.31***

12 months

0.48

0.26

0.75***

0.21*

15 months

0.43

0.31

0.72***

0.21*

18 months

0.41

0.33

0.67***

0.24**

Benchmark Rate

0.40

0.34

0.64***

0.27**

21 months

0.40

0.34

0.64***

0.27**

24 months

0.40

0.35

0.61***

0.30**

27 months

0.39

0.36

0.60***

0.32***

30 months

0.39

0.36

0.60***

0.33***

33 months

0.38

0.37

0.60***

0.34***

36 months

0.38

0.37

0.59***

0.35***

TRL/USD

0.87

1.04

-0.09

0.20

TRL/EUR

0.92

0.99

-0.21*

0.43***

ISE All

2.29

1.93

-0.43***

-0.13

ISE 100

2.35

2.02

-0.43***

-0.13

ISE 30

2.49

2.16

-0.42***

-0.13

Industry

2.09

1.86

-0.38***

-0.08

Services

1.91

1.60

-0.39***

-0.11

Trade

1.91

1.71

-0.26**

-0.14

Financial

2.71

2.27

-0.43***

-0.14

IT

2.24

2.61

-0.29**

-0.02

Exchange Rates

Stock Indices

Notes: The interest rates are daily changes in basis points, the exchange rates and the stock market indices are in daily percent changes. The maturity of the benchmark rate is around 20.4 months. ***, ** and *, indicate the significance levels at 1%, 5% and 10% levels respectively.

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Table 2. Estimation Results Event Study Estimate

Heteroskedasticity-based GMM

Std.Dev.

Estimate

Std.Dev.

Yields on Government Bonds with Different Maturities 6 months

0.731***

0.085

1.050***

0.061

9 months

1.092***

0.120

1.769***

0.143

12 months

1.005***

0.117

1.382***

0.178

15 months

0.866***

0.110

0.915***

0.215

18 months

0.770***

0.110

0.696***

0.193

Benchmark Rate

0.724***

0.112

0.623***

0.172

21 months

0.716***

0.113

0.611***

0.168

24 months

0.687***

0.115

0.558***

0.161

27 months

0.670***

0.115

0.502***

0.172

30 months

0.659***

0.114

0.447**

0.189

33 months

0.648***

0.112

0.411**

0.197

36 months

0.637***

0.110

0.399**

0.198

TRL/USD

-0.224

0.315

-0.511

0.438

TRL/EUR

-0.516*

0.333

-0.997***

0.394

ISE All

-2.760***

0.754

-3.385***

0.914

ISE 100

-2.856***

0.775

-3.445***

0.942

ISE 30

-2.928***

0.828

-3.503***

0.976

Industry

-2.258***

0.704

-2.762***

0.818

Services

-2.104***

0.640

-2.634***

0.817

-1.347**

0.676

-3.233***

0.895

-1.778**

0.785

Exchange Rates

Stock Indices

Trade Financial IT

-1.433 -3.982*** -2.433**

0.931 1.050 1.131

Notes: The maturity of the benchmark rate is around 20.4 months. ***, ** and *, indicate the significance levels at 1%, 5% and 10% levels respectively.

14

Table 3. Robustness Checks Hetero. Test Between Regimes

Over Identification Test

Hausman Test for Biasedness

t-stat.

GMM-OIR

GMM-ES

Yields on Government Bonds with Different Maturities 6 months

9.512***

0.215

29.054***

9 months

10.859***

0.292

76.854***

12 months

8.102***

0.052

7.957***

15 months

4.738***

0.027

0.071

18 months

3.751***

0.109

0.217

Benchmark Rate

3.569***

0.113

0.610

21 months

3.545***

0.111

0.721

24 months

3.441***

0.115

1.307

27 months

3.305***

0.169

1.742

30 months

3.162***

0.312

1.982

33 months

3.090***

0.559

2.139

36 months

3.073***

0.857

2.090

TRL/USD

2.891***

0.744

0.895

TRL/EUR

2.839***

0.909

5.194***

ISE All

3.188***

0.091

1.468

ISE 100

3.158***

0.053

1.209

ISE 30

3.140***

0.065

1.232

Industry

3.059***

0.017

1.460

Services

3.172***

0.140

1.093

Trade

2.873***

0.138

0.018

Financial

3.250***

0.155

1.864

2.565**

1.379

0.646

Exchange Rates

Stock Indices

IT

Notes: The heteroskedasticity test between the two regimes is a t-test on the coefficient λ. GMM over identification test has a χ2(1) distribution. F1,59 distribution is used for the Hausman-type biasedness test. The maturity of the benchmark rate is around 20.4 months. ***, ** and *, indicate the significance levels at 1%, 5% and 10% levels respectively.

15

Figure 1. Policy Rate and the Market Interest Rates 30.00

25.00

20.00

15.00

10.00 1 month

36 month

Policy rate

12-09

09-09

06-09

03-09

12-08

09-08

06-08

03-08

12-07

09-07

06-07

03-07

12-06

09-06

06-06

03-06

12-05

09-05

06-05

03-05

5.00

Figure 2. Policy Rate and the Exchange Rates 2.4

25.00 23.00

2.2 21.00 2

19.00 17.00

1.8

15.00 1.6

13.00 11.00

1.4

9.00 1.2 7.00

1 month

Policy rate

TRL/USD rate

TRL/EUR rate

16

12-09

09-09

06-09

03-09

12-08

09-08

06-08

03-08

12-07

09-07

06-07

03-07

12-06

09-06

06-06

03-06

12-05

09-05

06-05

1 03-05

5.00

Figure 3. Policy Rate and the Stock Market Index 25.00

60000

23.00

55000

21.00

50000

19.00

45000

17.00

40000

15.00

35000

13.00

30000

11.00

25000

9.00

20000 1 month

7.00

Policy rate

ISE All (right axis)

15000

12-09

09-09

06-09

03-09

12-08

09-08

06-08

03-08

12-07

09-07

06-07

03-07

12-06

09-06

06-06

03-06

12-05

09-05

06-05

10000 03-05

5.00

Figure 4. The Impact of Monetary Policy on Government Bond Yields (±2 standard error bands) 2.1

Event Study GMM, heteroskedasticity-based

1.8

Event Study-band Event Study-band

1.5

GMM-band

Alpha

GMM-band

1.2 0.9 0.6 0.3 0.0 5

10

15

20

25

Maturity (months)

17

30

35

40

Central Bank of the Republic of Turkey Recent Working Papers The complete list of Working Paper series can be found at Bank’s website (http://www.tcmb.gov.tr).

The Trade Credit Channel of Monetary Policy Transmission: Evidence from Non-financial Firms in Turkey (Pınar Özlü, Cihan Yalçın Working Paper No. 10/16, September 2010)

Economic Uncertanity and Money Demand Stability in Turkey (K. Azim Özdemir, Mesut Saygılı Working Paper No. 10/15, August 2010)

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Organization of Innovation and Capital Markets (Cüneyt Orman Working Paper No. 10/10, May 2010)

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Optimal Monetary Policy under Sectoral Heterogeneity in Inflation Persistence (Sevim Kösem Alp Working Paper No. 10/04, March 2010)

Recovering Risk-Neutral Densities from Exchange Rate Options: Evidence in Turkey (Halil İbrahim Aydın, Ahmet Değerli, Pınar Özlü Working Paper No. 10/03, March 2010)

Türkiye İmalat Sanayiin İthalat Yapısı (Şeref Saygılı, Cengiz Cihan, Cihan Yalçın, Türknur Hamsici Çalışma Tebliğ No. 10/02, Mart 2010)

Dış Ticarette Küresel Eğilimler ve Türkiye Ekonomisi (Faruk Aydın, Hülya Saygılı, Mesut Saygılı, Gökhan Yılmaz Çalışma Tebliğ No. 10/01, Mart 2010)