COMMON VOLATILITY TRENDS AMONG CENTRAL AND EASTERN EUROPEAN CURRENCIES

COMMON VOLATILITY TRENDS COMMON VOLATILITY TRENDS AMONG CENTRAL AND AMONG CENTRAL AND EASTERN EUROPEAN EASTERN EUROPEAN

CURRENCIESCURRENCIES

MSc Student: ODANGIU ANDREEA RALUCAMSc Student: ODANGIU ANDREEA RALUCACoordinator: Professor MOISĂ ALTĂRCoordinator: Professor MOISĂ ALTĂR

Bucharest, July 2007Bucharest, July 2007

ACADEMY OF ECONOMIC STUDIESACADEMY OF ECONOMIC STUDIESDOCTORAL SCHOOL OF FINANCE AND DOCTORAL SCHOOL OF FINANCE AND

BANKINGBANKING

Dissertation paper outlineDissertation paper outline

The importance of common trends in CEE exchange rate volatilityThe importance of common trends in CEE exchange rate volatility The aims of the present paperThe aims of the present paper Brief review of recent literature on exchange rate volatilityBrief review of recent literature on exchange rate volatility The dataThe data The Component GARCH modelThe Component GARCH model The Spillover IndexThe Spillover Index The Orthogonal GARCH modelThe Orthogonal GARCH model Concluding remarksConcluding remarks ReferencesReferences

The importance of common trends in The importance of common trends in CEE exchange rate volatilityCEE exchange rate volatility

For the 12 new member states of the EU, adopting the euro as the For the 12 new member states of the EU, adopting the euro as the national currency some time in the next few years is not optional; it is a national currency some time in the next few years is not optional; it is a definite requirementdefinite requirement

Before adopting the euro, every country has to be part of ERM II, for at Before adopting the euro, every country has to be part of ERM II, for at least two yearsleast two years

We examine the exchange rate volatility patterns of the Czech We examine the exchange rate volatility patterns of the Czech Republic, Hungary, Poland, Romania and Slovakia, over the sample Republic, Hungary, Poland, Romania and Slovakia, over the sample period May 2001 – April 2007period May 2001 – April 2007

Poland is the only one of the twelve new member states that has not Poland is the only one of the twelve new member states that has not yet proposed a definite deadline for euro adoption, while Slovakia has yet proposed a definite deadline for euro adoption, while Slovakia has already joined ERM II as of 28 November 2005. However, due to already joined ERM II as of 28 November 2005. However, due to constant appreciation pressures on the koruna, the Slovak Central constant appreciation pressures on the koruna, the Slovak Central Bank has had to intervene frequently on the foreign exchange market, Bank has had to intervene frequently on the foreign exchange market, and eventually gain approval from the European Central Bank to lift and eventually gain approval from the European Central Bank to lift the central parity rate by 8.5% as of 19 March 2007. The RON also the central parity rate by 8.5% as of 19 March 2007. The RON also faces similar appreciation pressures, which is one of the reasons why faces similar appreciation pressures, which is one of the reasons why the National Bank of Romania has cut its monetary policy rate four the National Bank of Romania has cut its monetary policy rate four times already since the beginning of 2007. Hungary was forced to times already since the beginning of 2007. Hungary was forced to postpone its plan to adopt the euro in 2010 after running up the postpone its plan to adopt the euro in 2010 after running up the European Union’s widest budget deficit in 2006.European Union’s widest budget deficit in 2006.

The aims of the paperThe aims of the paper

To identify a unitary model for the five exchange rate To identify a unitary model for the five exchange rate volatilities and to identify similar patterns among them;volatilities and to identify similar patterns among them;

To isolate the different sources of exchange rate volatility and To isolate the different sources of exchange rate volatility and to compute a measure for how much the currencies influence to compute a measure for how much the currencies influence each other;each other;

To examine how the correlations between these five currencies To examine how the correlations between these five currencies have evolved over the time period under analysis.have evolved over the time period under analysis.

Brief literature reviewBrief literature review

Teräsvirta (2006): extensive review of several univariate GARCH Teräsvirta (2006): extensive review of several univariate GARCH modelsmodels

The Component GARCH model: introduced by Engle and Lee The Component GARCH model: introduced by Engle and Lee (1993), used in recent papers such as Maheu (2005), Guo and (1993), used in recent papers such as Maheu (2005), Guo and Neely (2006), Christoffersen et al. (2006) and Bauwens and Storti Neely (2006), Christoffersen et al. (2006) and Bauwens and Storti (2007)(2007)

Exchange rate volatility: Byrne and Davis (2003) – G7 countries; Exchange rate volatility: Byrne and Davis (2003) – G7 countries; Kóbor and Székely (2004), Pramor and Tamirisa (2006)Kóbor and Székely (2004), Pramor and Tamirisa (2006) – CEE – CEE currencies; Borghijs and Kuijs (2004)currencies; Borghijs and Kuijs (2004) - SVAR approach to - SVAR approach to examine the usefulness of flexible exchange rates as shock examine the usefulness of flexible exchange rates as shock absorbers in CEE countriesabsorbers in CEE countries

Spillover Index: Diebold and Yilmaz (2007)Spillover Index: Diebold and Yilmaz (2007) Orthogonal GARCH model: Klaassen (1999), Alexander (2000)Orthogonal GARCH model: Klaassen (1999), Alexander (2000)

The DataThe Data

Daily nominal exchange rates of five CEE currencies against the euro, Daily nominal exchange rates of five CEE currencies against the euro, namely the Czech koruna (CZK), the Hungarian forint (HUF), the Polish namely the Czech koruna (CZK), the Hungarian forint (HUF), the Polish zloty (PLN), the Romanian new leu (RON) and the Slovak koruna (SKK). zloty (PLN), the Romanian new leu (RON) and the Slovak koruna (SKK). The data is obtained from Eurostat (for SKK) and from the web site of The data is obtained from Eurostat (for SKK) and from the web site of each Central Bank respectively (for CZK, HUF, PLN and RON). Each each Central Bank respectively (for CZK, HUF, PLN and RON). Each exchange rate is quoted as number of national currency units per euroexchange rate is quoted as number of national currency units per euro

The sampling period covers 4 May 2001 to 5 April 2007; we will also be The sampling period covers 4 May 2001 to 5 April 2007; we will also be studying two sub-periods, May 2001 to November 2004 and December studying two sub-periods, May 2001 to November 2004 and December 2004 to April 20072004 to April 2007

All series in levels display a unit root, as evident from the ADF test All series in levels display a unit root, as evident from the ADF test results. Hence the series are transformed into log-differences and we results. Hence the series are transformed into log-differences and we obtain the continuously compounded exchange rate returns (which are obtain the continuously compounded exchange rate returns (which are I(0)):I(0)):

)ln()ln( 1 ttt SSy

The Component GARCH ModelThe Component GARCH Model

ttty

)()()1( 21

21

2211

22 ttttt hhh

1112

11112 )()()( ttttttttt Dqqhqqh

The conditional variance in the GARCH(1,1) model can be written as:

Allowing for the possibility that σ2 is not constant over time, but a time-varying trend qt, yields:

)()( 112

1 tttt hqq

),0(~| 1 ttt hNI

where Dt is a slope dummy variable that takes the value Dt = 1 for εt < 0 and Dt = 0 otherwise, in order to capture any asymmetric responses of volatility to shocks. We test for the significance of this term using the Engle-Ng test for sign bias and include it where relevant.

qt is the permanent component (or trend) of the conditional variance, while ht-qt is the transitory component.Stationarity of the CGARCH model and non-negativity of the conditional variance are ensured if the following inequality constraints are satisfied: 1 > ρ > (α+β), β > Φ > 0, α > 0, β > 0, Φ > 0, ω > 0.

CGARCH EstimatesCGARCH Estimates2001:5 – 2007:4 CZK HUF PLN RON SKK

Trend intercept ω 0.00001238*** 0.00001955*** 0.00003181*** 0.00011813*** 0.00026282***

Trend AR Term ρ 0.9914*** 0.9889*** 0.9771*** 0.9982*** 0.9999***

Forecast Error φ 0.0338*** 0.0088 0.0344*** 0.1146*** 0.0265**

ARCH Term α 0.1242*** 0.2693*** 0.1420*** 0.1275*** 0.3385***

GARCH Term β 0.5312*** 0.7058*** 0.4361*** -0.1992 0.4261***

Asymm. Term γ - -0.2919*** -0.0778** - -0.3535***

2001:5 – 2004:112001:5 – 2004:11 CZK HUF PLN RON SKK

Trend intercept ω 0.00001635*** 0.00002016*** 0.00003733*** 0.00009251 0.00000490

Trend AR Term ρ 0.9899*** 0.9626*** 0.9775*** 0.9991*** 1.0000***

Forecast Error φ 0.0478 0.0061 0.0460*** 0.0483** 0.0261***

ARCH Term α 0.1418*** 0.2991*** 0.2154*** 0.0285 0.0940***

GARCH Term β 0.4873*** 0.5827*** 0.3105*** 0.9283*** 0.7298***

Asymm. Term γ - -0.2985*** -0.1254** - -


Trend intercept ω 0.00000747*** 0.00002801 0.00001701*** 0.00002088*** 0.00001484***

Trend AR Term ρ 0.9908*** 0.9958*** 0.9967*** 0.9467*** 0.9800***

Forecast Error φ 0.0149 0.0474*** 0.0153*** 0.0420 0.0171

ARCH Term α 0.0855** 0.1481*** 0.0428*** 0.1300** 0.0461**

GARCH Term β 0.5705** 0.7961*** 0.7406*** 0.7282*** 0.7999***

Asymm. Term γ - -0.1136*** -0.0206*** 0.1633*** -

Ljung-Box TestLjung-Box Test

2001:5 – 2007:4 CZK HUF PLN RON SKK

L-B test for squared returns 210.3951 156.9530 533.4170 332.1083 59.6085

L-B test for squared standardized residuals 12.5879 8.0893 8.2748 19.1325 10.0056







The results show a tremendous improvement in the values of the Q* statistics over the ones for the squared returns, so the component model successfully captures the typical pattern of serial correlation.

All the Engle-Ng tests, Ljung-Box tests and CGARCH estimates have been computed using Rats 6.01.

m=15 lags

CGARCH Conditional Variance CGARCH Conditional Variance ComponentsComponents

EURHUF 2001 - 2004

-0.00005

0.00005

0.00015

0.00025

0.00035

0.00045

0.00055

0.00065

0.00075

5/4/

2001

7/4/

2001

9/4/

2001

11/4

/200

1

1/4/

2002

3/4/

2002

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/200

2

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3

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2004

11/4

/200

4

Permanent cond var

Transitory cond var

EURCZK 2001 - 2004

-0.00005

-0.00003

0.00000

0.00003

0.00005

0.00008

0.00010

0.00013

0.00015

5/4/

2001

7/4/

2001

9/4/

2001

11/4

/200

1

1/4/

2002

3/4/

2002

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2

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3

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7/4/

2004

9/4/

2004

11/4

/200

4

Permanent cond var

Transitory cond var

EURCZK 2004 - 2007

-0.00005

-0.00003

0.00000

0.00003

0.00005

0.00008

0.00010

0.00013

0.00015

12/1

/200

4

2/1/

2005

4/1/

2005

6/1/

2005

8/1/

2005

10/1

/200

5

12/1

/200

5

2/1/

2006

4/1/

2006

6/1/

2006

8/1/

2006

10/1

/200

6

12/1

/200

6

2/1/

2007

4/1/

2007

Permanent cond var

Transitory cond var

EURHUF 2004 - 2007

-0.00005

0.00005

0.00015

0.00025

0.00035

0.00045

0.00055

0.00065

0.00075

12/1

/200

4

2/1/

2005

4/1/

2005

6/1/

2005

8/1/

2005

10/1

/200

5

12/1

/200

5

2/1/

2006

4/1/

2006

6/1/

2006

8/1/

2006

10/1

/200

6

12/1

/200

6

2/1/

2007

4/1/

2007

Permanent cond var

Transitory cond var

CGARCH Conditional Variance Components cont’dCGARCH Conditional Variance Components cont’d

EURPLN 2001 - 2004

-0.00005

0.00005

0.00015

0.00025

0.00035

0.00045

0.00055

0.00065

0.000755/

4/20

01

7/4/

2001

9/4/

2001

11/4

/200

1

1/4/

2002

3/4/

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2

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3

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11/4

/200

4

Permanent cond var

Transitory cond var

EURPLN 2004 - 2007

-0.00005

0.00005

0.00015

0.00025

0.00035

0.00045

0.00055

0.00065

0.00075

12/1

/200

4

2/1/

2005

4/1/

2005

6/1/

2005

8/1/

2005

10/1

/200

5

12/1

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5

2/1/

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4/1/

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6/1/

2006

8/1/

2006

10/1

/200

6

12/1

/200

6

2/1/

2007

4/1/

2007

Permanent cond var

Transitory cond var

EURRON 2001 - 2004

-0.00005

0.00000

0.00005

0.00010

0.00015

0.00020

0.00025

0.00030

5/4/

2001

7/4/

2001

9/4/

2001

11/4

/200

1

1/4/

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3

1/4/

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5/4/

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7/4/

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9/4/

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11/4

/200

4

Permanent cond var

Transitory cond var

EURRON 2004 - 2007

-0.00005

0.00000

0.00005

0.00010

0.00015

0.00020

0.00025

0.00030

12/1

/200

4

2/1/

2005

4/1/

2005

6/1/

2005

8/1/

2005

10/1

/200

5

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/200

5

2/1/

2006

4/1/

2006

6/1/

2006

8/1/

2006

10/1

/200

6

12/1

/200

6

2/1/

2007

4/1/

2007

Permanent cond var

Transitory cond var

EURSKK 2001 - 2004

-0.00005

-0.00003

0.00000

0.00003

0.00005

0.00008

0.00010

0.00013

0.00015

5/4/

2001

7/4/

2001

9/4/

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11/4

/200

4

Permanent cond varTransitory cond var

EURSKK 2004 - 2007

-0.00005

-0.00003

0.00000

0.00003

0.00005

0.00008

0.00010

0.00013

0.00015

12/1

/200

4

2/1/

2005

4/1/

2005

6/1/

2005

8/1/

2005

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5

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6

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/200

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2/1/

2007

4/1/

2007

Permanent cond varTransitory cond var

RemarksRemarks The autoregressive parameters in the trend equations, ρ, is very close The autoregressive parameters in the trend equations, ρ, is very close

to one for all currencies and all time periods (the smallest being 0.9467 to one for all currencies and all time periods (the smallest being 0.9467 for RON 2004 – 2007), so the series are very close to being integrated.for RON 2004 – 2007), so the series are very close to being integrated.

The shock effects on the transitory component of volatilities (the α The shock effects on the transitory component of volatilities (the α coefficients), are much larger than the shock effects on the permanent coefficients), are much larger than the shock effects on the permanent component (the φ coefficients) – generally around three to six times component (the φ coefficients) – generally around three to six times larger. However, as found in all the papers that use the CGARCH larger. However, as found in all the papers that use the CGARCH specification, the shocks to short-run volatility are very short-lived, specification, the shocks to short-run volatility are very short-lived, even if they are stronger.even if they are stronger.

ρρ and and ββ coefficients are generally higher in the late sample period, while coefficients are generally higher in the late sample period, while φφ and and αα coefficients are smaller, which implies that volatility is coefficients are smaller, which implies that volatility is becoming less responsive to shocks and more persistent. The only becoming less responsive to shocks and more persistent. The only exception is the RON.exception is the RON.

The asymmetric effects are highly significant for HUF and PLN (for all The asymmetric effects are highly significant for HUF and PLN (for all sample periods). sample periods). γγ coefficients are consistently negative, which coefficients are consistently negative, which indicates that negative returns actually decrease variances, and that indicates that negative returns actually decrease variances, and that exchange rate volatility is lower during times of currency appreciation.exchange rate volatility is lower during times of currency appreciation.

The five currencies appear to respond to temporary market shocks in The five currencies appear to respond to temporary market shocks in similar ways (as suggested by positive correlations between transitory similar ways (as suggested by positive correlations between transitory volatilities), they respond differently to more permanent shocks.volatilities), they respond differently to more permanent shocks.

The Spillover IndexThe Spillover Indexttt xx 1

ttt xx ,1

1,2

1,1

22,021,0

12,011,010,11,1

t

ttttttt u

uaaaa

uAxxe

The typical representation of a covariance stationary first-order VAR is:

The optimal 1-step-ahead forecast is:

and the corresponding 1-step-ahead error vector (assuming a two-variable VAR):

where ut = Qtεt, and Qt-1 is the unique lower-triangular Cholesky factor

of the covariance matrix of εt.

For the pth-order N-variable VAR using H-step-ahead forecasts, the Spillover Index is:

)( 00

1

0

,1,

2,0

1

0

TH

h

N

jijiij

H

h

AAtrace

aS

The Spillover Index, 2001 - 2004The Spillover Index, 2001 - 2004

Permanent volatilityPermanent volatilityFROMFROM Contribution Contribution

fom othersfom othersHUFHUF SKKSKK RONRON CZKCZK PLNPLN

TOTO

HUFHUF 98.9998.99 0.390.39 0.050.05 0.220.22 0.360.36 1.011.01

SKKSKK 2.002.00 92.5992.59 0.910.91 2.352.35 2.152.15 7.417.41

RONRON 0.650.65 0.500.50 94.5494.54 2.502.50 1.821.82 5.465.46

CZKCZK 0.390.39 0.300.30 1.831.83 96.7996.79 0.690.69 3.213.21

PLNPLN 14.8714.87 6.816.81 4.324.32 0.950.95 73.0473.04 26.9626.96

Contribution to othersContribution to others 17.9117.91 8.008.00 7.117.11 6.026.02 5.025.02 44.0544.05

Contribution including ownContribution including own 116.90116.90 100.59100.59 101.64101.64 102.81102.81 78.0678.06 500.00500.00

Spillover IndexSpillover Index 8.81%8.81%

Transitory volatilityTransitory volatilityFROMFROM Contribution Contribution


TOTO

HUFHUF 97.1697.16 0.980.98 0.280.28 0.800.80 0.780.78 2.842.84

SKKSKK 4.104.10 91.8591.85 1.021.02 1.591.59 1.441.44 8.158.15

RONRON 0.180.18 0.490.49 92.6092.60 0.910.91 5.825.82 7.407.40

CZKCZK 0.270.27 1.041.04 0.320.32 98.0498.04 0.330.33 1.961.96

PLNPLN 9.609.60 5.995.99 1.941.94 0.280.28 82.1982.19 17.8117.81




The Spillover Index, 2004 - 2007The Spillover Index, 2004 - 2007

Permanent volatilityPermanent volatilityFROMFROM Contribution Contribution


TOTO

HUFHUF 97.8397.83 0.070.07 0.050.05 1.861.86 0.190.19 2.172.17

SKKSKK 21.1021.10 73.7173.71 0.360.36 4.774.77 0.070.07 26.6926.69

RONRON 2.332.33 0.300.30 93.3193.31 4.004.00 0.060.06 6.696.69

CZKCZK 0.330.33 21.7921.79 5.935.93 70.0270.02 1.941.94 29.9829.98

PLNPLN 11.2211.22 0.820.82 0.240.24 11.2611.26 76.4676.46 23.5423.54




Transitory volatilityTransitory volatilityFROMFROM Contribution Contribution


TOTO

HUFHUF 97.0097.00 1.581.58 0.600.60 0.530.53 0.280.28 3.003.00

SKKSKK 3.533.53 88.8188.81 1.671.67 2.202.20 3.793.79 11.1911.19

RONRON 0.280.28 0.170.17 96.7296.72 0.420.42 2.412.41 3.283.28

CZKCZK 0.850.85 8.938.93 0.150.15 83.4183.41 6.666.66 16.5916.59

PLNPLN 29.2429.24 1.171.17 0.180.18 3.513.51 65.9065.90 34.1034.10




RemarksRemarks The appropriate number of lags for each VAR model is determined The appropriate number of lags for each VAR model is determined

using the information criteria. We also perform a check on the AR using the information criteria. We also perform a check on the AR roots, and the results indicate that all six VAR specifications are roots, and the results indicate that all six VAR specifications are stable.stable.

We use 20-step-ahead forecast error variance and a Cholesky We use 20-step-ahead forecast error variance and a Cholesky ordering as shown in the table headers. The reasons behind these ordering as shown in the table headers. The reasons behind these decisions are as follows: volatility has been found to be highly decisions are as follows: volatility has been found to be highly persistent (especially the trend component), so a large enough persistent (especially the trend component), so a large enough number of forecast steps is necessary; furthermore, according to number of forecast steps is necessary; furthermore, according to Brooks (2002), the differences between the different Cholesky Brooks (2002), the differences between the different Cholesky orderings become smaller as the number of forecast periods orderings become smaller as the number of forecast periods increases.increases.

The results clearly indicate that volatility spillovers have increased The results clearly indicate that volatility spillovers have increased over time, in line with the findings of Kóbor and Székely (2004) but over time, in line with the findings of Kóbor and Székely (2004) but contrary to Pramor and Tamirisa (2006). Furthermore, spillovers into contrary to Pramor and Tamirisa (2006). Furthermore, spillovers into permanent volatility appear stronger than into the transitory permanent volatility appear stronger than into the transitory component.component.

While the results are sensitive to series ordering, in many cases the While the results are sensitive to series ordering, in many cases the HUF appears to have been the most important source of volatility in HUF appears to have been the most important source of volatility in the region, while the PLN has been the most important shock the region, while the PLN has been the most important shock absorber. Pramor and Tamirisa (2006) and Borghijs and Kuijs (2004) absorber. Pramor and Tamirisa (2006) and Borghijs and Kuijs (2004) reach similar conclusions.reach similar conclusions.

The Orthogonal GARCH ModelThe Orthogonal GARCH Model

The steps involved in estimating this model are as follows:The steps involved in estimating this model are as follows:Step 1Step 1: Computing the principal components of the normalized initial : Computing the principal components of the normalized initial

system:system:Step 2Step 2: Estimating the conditional variance of the principal components by : Estimating the conditional variance of the principal components by

standard univariate GARCH(1,1) models:standard univariate GARCH(1,1) models:

XWP

jjtt pE }{1

0},{1 ltjtt ppCov

}{}){(}{ 122

1211 jttjjttjtjjjtt pVpEppV

for every principal component j, l = 1,…,k (j ≠ l).Step 3: Transform the conditional moment of the principal components into the ones for the original series:

}{}{ 11 tttt pAEyE T

tttt ApAVyV }{}{ 11

where A = (ω*ij) = wijσi

We follow the approach of Klaassen (1999) and we consider the same We follow the approach of Klaassen (1999) and we consider the same number of principal components as series in the initial system. This presents number of principal components as series in the initial system. This presents several advantages, such as eliminating the problem of the arbitrary choice several advantages, such as eliminating the problem of the arbitrary choice of k or avoiding the danger of losing important information about the initial of k or avoiding the danger of losing important information about the initial system by ignoring the last components, which may sometimes contain more system by ignoring the last components, which may sometimes contain more than just ‘noise’.than just ‘noise’.

The most influential component is the first one, but it only explains just over The most influential component is the first one, but it only explains just over 40%. This is to be expected, because the correlations between the original 40%. This is to be expected, because the correlations between the original series are not very high to begin with (at least when compared to industrial series are not very high to begin with (at least when compared to industrial countries).countries).

The fifth component accounts for almost 10%, which is quite high.The fifth component accounts for almost 10%, which is quite high.

PC1 PC2 PC3 PC4 PC5

EigenvalueExpl. VarianceCumulated

2.0525441.05%41.05%

1.0173420.35%61.40%

0.8562517.13%78.52%

0.5879111.76%90.28%

0.485969.72%

100.00%

The Orthogonal GARCH ModelThe Orthogonal GARCH Model

GARCH(1,1) Estimtes for PCsGARCH(1,1) Estimtes for PCs

PC1 PC2 PC3 PC4 PC5

Mean μ -0.022126 0.012332 0.006714 0.007600 0.006167

Cond. var. intercept

ω 0.120778*** 0.018724** 0.064347*** 0.017480 0.155779***

ARCH Term α 0.141271*** 0.066344*** 0.160548*** 0.031962*** 0.158405***

GARCH Term β 0.739553*** 0.915587*** 0.779134*** 0.952518*** 0.682667***

Evolution of 3 Selected Conditional Evolution of 3 Selected Conditional Correlations, With 60-day Moving Correlations, With 60-day Moving

AveragesAverages CZK - SKK

-0.20

0.00

0.20

0.40

0.60

0.80

1.00

04/0

5/20

01

04/0

9/20

01

04/0

1/20

02

04/0

5/20

02

04/0

9/20

02

04/0

1/20

03

04/0

5/20

03

04/0

9/20

03

04/0

1/20

04

04/0

5/20

04

04/0

9/20

04

04/0

1/20

05

04/0

5/20

05

04/0

9/20

05

04/0

1/20

06

04/0

5/20

06

04/0

9/20

06

04/0

1/20

07

HUF - PLN

-0.20

0.00

0.20

0.40

0.60

0.80

1.00

04/0

5/01

04/0

9/01

04/0

1/02

04/0

5/02

04/0

9/02

04/0

1/03

04/0

5/03

04/0

9/03

04/0

1/04

04/0

5/04

04/0

9/04

04/0

1/05

04/0

5/05

04/0

9/05

04/0

1/06

04/0

5/06

04/0

9/06

04/0

1/07

PLN - RON

-0.20

0.00

0.20

0.40

0.60

0.80

1.00

04/0

5/01

04/0

9/01

04/0

1/02

04/0

5/02

04/0

9/02

04/0

1/03

04/0

5/03

04/0

9/03

04/0

1/04

04/0

5/04

04/0

9/04

04/0

1/05

04/0

5/05

04/0

9/05

04/0

1/06

04/0

5/06

04/0

9/06

04/0

1/07

Higher volatility is generally associated with higher correlation coefficients among the CEE currencies.

Examination of the longer-term trends of correlations reveals that they have generally increased over the sample period in question (May 2001 – April 2007), or at least remained at broadly similar levels. The only exception: CZK - SKK

Concluding RemarksConcluding Remarks Many papers have focused on the degree of business cycle convergence; Many papers have focused on the degree of business cycle convergence;

however, we believe that exchange rate volatility is also a very important however, we believe that exchange rate volatility is also a very important aspect, especially when entering ERM II, prior to actual changeover. aspect, especially when entering ERM II, prior to actual changeover. Under these circumstances, an analysis such as ours is important Under these circumstances, an analysis such as ours is important because it appears essential for Central Banks to know very well the because it appears essential for Central Banks to know very well the exchange rate volatility patterns of their country’s own currency, but exchange rate volatility patterns of their country’s own currency, but also the ones of the other currencies in the region, in order to have also the ones of the other currencies in the region, in order to have better expectations of how the exchange rate is going to be affected.better expectations of how the exchange rate is going to be affected.

We find evidence of higher correlations of volatility components, We find evidence of higher correlations of volatility components, increasing spillovers and higher conditional correlations among increasing spillovers and higher conditional correlations among currencies, which suggest growing convergence and stronger cross-currencies, which suggest growing convergence and stronger cross-linkages between the five exchange rates in question.linkages between the five exchange rates in question.

Policy makers of each country have to increasingly take into account Policy makers of each country have to increasingly take into account other countries’ actions when making their own decisions. This calls for other countries’ actions when making their own decisions. This calls for more coordinated courses of action, which would be a very good exercise more coordinated courses of action, which would be a very good exercise in preparation for euro adoption and a single, unified monetary policy.in preparation for euro adoption and a single, unified monetary policy.

Possible directions for future research: estimate volatilities with more Possible directions for future research: estimate volatilities with more complex models, such as smooth transition or Markov-switching GARCH, complex models, such as smooth transition or Markov-switching GARCH, or using intra-day returns; a study of contagion phenomena among the or using intra-day returns; a study of contagion phenomena among the CEE currencies, especially during turbulent market times, using one of CEE currencies, especially during turbulent market times, using one of the approaches presented in Dungey et al. (2004).the approaches presented in Dungey et al. (2004).

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*** European Central Bank, Convergence Report May 2006*** European Central Bank, Convergence Report May 2006

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COMMON VOLATILITY TRENDS AMONG CENTRAL AND EASTERN EUROPEAN CURRENCIES

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