Contagion Phenomenon among Central Contagion Phenomenon among Central andand

Eastern European Currencies Eastern European Currencies

Student: Roteanu Cosmina GeorgianaStudent: Roteanu Cosmina Georgiana

ACADEMY OF ECONOMIC STUDIESACADEMY OF ECONOMIC STUDIESDOCTORAL SCHOOL OF FINANCE AND BANKINGDOCTORAL SCHOOL OF FINANCE AND BANKING

Bucharest, July 2009Bucharest, July 2009

Dissertation paper outlineDissertation paper outline

The importance of contagion among CEE currenciesThe importance of contagion among CEE currencies

The aims of the present paperThe aims of the present paper

Brief review of the literature on contagionBrief review of the literature on contagion

Model specificationsModel specifications

Data, estimation and resultsData, estimation and results

ConclusionsConclusions

ReferencesReferences

The importance of contagion among CEE currenciesThe importance of contagion among CEE currencies

Contagion was at the heart of the global financial crisis.

September 2008 – critical stage of the crisis.

Effects on CEE countries: heightened volatility, an increase in risk premia and plummeting currencies.

Shift in the attitude towards adopting Euro:

“ Secure public finances and a quick adoption of the euro are the best way out of the crisis for Poland” – Finance Minister, February 2009.

In January 2009 Czech prime minister announced that the government will determine a date for adopting the euro in November 2009, the most realistic target being 2013.

“For countries in the EU, euroisation offers the largest benefits in terms of resolving the foreign currency debt overhang, removing uncertainty and restoring confidence.” – IMF, April 2009.

Before adopting the euro, every country has to be part of ERM II, for at least two years, but meeting the convergence criteria appears more difficult in the light of the crisis events.

Aims of the paperAims of the paper

This paper focuses on the exchange rate behavior of Czech Koruna, Polish Zloty and Romanian Leu during normal and heightened volatility periods.

The objectives are:

o to isolate different sources of exchange rate volatility : common and idiosyncratic

o to compute a measure of contagion represented by the spillover effects from unanticipated local shocks from one foreign exchange market to another after conditioning on common factors.

Broad definition of contagion(World Bank)

“Contagion is the cross-country transmission of shocks or the general cross-country spillover effects”

• Contagion takes place both during “good” and “bad” times.

• Most of the literature distinguishes ‘fundamental’ linkages from contagion:o Calvo and Reinhart (1996) : fundamentals – based contagion

“true” contagiono Kaminsky et al.(2000) : fundamentals – based contagion

common cause contagion pure contagion

Restrictive Definition(World Bank)

“Contagion is the transmission of shocks to other countries or the cross-country correlation, beyond any fundamental link among the

countries and beyond common shocks.”

o Fundamental linkages across countries include:

- Financial links

- Real links

Very Restrictive Definition(World Bank)

“Contagion occurs when cross-country correlations increase during ‘crisis times’ relative to correlations during ‘tranquil times.”

• Only increases in correlation are recognized as contagion

• Forbes and Rigobon (2002) :

“ … “ … a significant increase in cross-market linkages after a shock to one country.”a significant increase in cross-market linkages after a shock to one country.”

• It needs to control for general volatility rising during financial crises

Our versionOur version

Dungey et al. (2005) definition of contagion as “unexpected shocks” or news

• Other terms: “pure contagion”, “shift contagion”Other terms: “pure contagion”, “shift contagion”

• Contagion takes place because transmission arises over and above the anticipated Contagion takes place because transmission arises over and above the anticipated

links, so the reaction is beyond what could have been expected on the basis of links, so the reaction is beyond what could have been expected on the basis of

fundamental linkages. fundamental linkages.

Measure of ContagionMeasure of Contagion

Latent Factor Model proposedLatent Factor Model proposed by Dungey et al. (2005) by Dungey et al. (2005)

• It includes global and country factors to capture market fundamentals and also It includes global and country factors to capture market fundamentals and also

additional movements over and above market fundamentals during crisis period to additional movements over and above market fundamentals during crisis period to

account for contagionaccount for contagion

• This modeling strategy is shown to encompass many of the existing approaches to This modeling strategy is shown to encompass many of the existing approaches to

measuring contagion, including measuring contagion, including

o the correlation analysis proposed by Forbes and Rigobon (2002)the correlation analysis proposed by Forbes and Rigobon (2002)

o the vector autoregression (VAR) approach of Favero and Giavazzi (2002)the vector autoregression (VAR) approach of Favero and Giavazzi (2002)

o the probability models of Eichengreen et al. (1996)the probability models of Eichengreen et al. (1996)

o the co-exceedance approach of Bae et al. (2003)the co-exceedance approach of Bae et al. (2003)

DFGM - A Model of InterdependenceDFGM - A Model of Interdependence

Latent factor model of asset returns during non-crisis periodLatent factor model of asset returns during non-crisis period

Based on Arbitrage Pricing TheoryBased on Arbitrage Pricing Theory

xx1,t1,t = = λλ11wwt t + + γγ11uu1,t1,t

xx2,t2,t = = λλ22wwtt + + γγ22uu2,t 2,t (1)

xx3,t3,t = = λλ33wwt t + + γγ33uu3,t 3,t

xxtt – returns during non-crisis period – returns during non-crisis period

wwtt – world factor (common shock) – world factor (common shock)

uui,ti,t – idiosyncratic factor – idiosyncratic factor

λλii, γ, γii >0 – factor loadings that determine the contribution of each shock to the volatility of >0 – factor loadings that determine the contribution of each shock to the volatility of

asset marketsasset markets Assumptions:Assumptions:

o the factors are stochastic processes with zero mean and constant variance: the factors are stochastic processes with zero mean and constant variance: wwtt ~ (0,1) ~ (0,1) uui,ti,t ~ (0,1)~ (0,1)

o all factors are independent:all factors are independent: jiuuE tjti 0],[ ,, iwuE tti 0],[ ,

DFGM - A Model of ContagionDFGM - A Model of Contagion

The factor model is augmented to allow an avenue for contagion between the foreign exchange markets in all directions:

yy1,t1,t = λ = λ11wwtt + γ + γ11uu1,t1,t + δ + δ1,21,2 u u2,t2,t+ δ+ δ1,31,3 u u3,t3,t

yy2,t 2,t = λ= λ22wwt t + γ + γ22uu2,t2,t + δ + δ2,12,1 u u1,t 1,t + δ+ δ2,32,3 u u3,t3,t (2)

yy3,t3,t = λ = λ33wwtt + γ + γ33uu3,t3,t + δ + δ3,13,1 u u1,t1,t+ δ+ δ3,23,2 u u2,t2,t

yi,t = returns during crisis period

δi,j = effects of unanticipated local shocks from the asset j to i (strength of contagion across markets)

The parameters of the model are estimated by The parameters of the model are estimated by GMMGMM: : q = arg G’WGq = arg G’WG (Hamilton, 1994) (Hamilton, 1994) (3) (3) ΘΘ

The model in (1) is just identified as there are six unknown parameters and six unique variances and The model in (1) is just identified as there are six unknown parameters and six unique variances and

covariances, whilst the model in (2) is unidentified as there are N (N+1) / 2 = 6 unique moment conditions covariances, whilst the model in (2) is unidentified as there are N (N+1) / 2 = 6 unique moment conditions

and 12 unknown parameters. In this special case, the model becomes block-recursive with identification and 12 unknown parameters. In this special case, the model becomes block-recursive with identification

of the factor loadings using the pre-crisis period moments (the first block), whilst the parameters of the factor loadings using the pre-crisis period moments (the first block), whilst the parameters

capturing the effect of contagion are identified by the empirical moments from the variance-covariance capturing the effect of contagion are identified by the empirical moments from the variance-covariance

matrix of the crisis period.matrix of the crisis period.

Variance - Covariance DecompositionsVariance - Covariance Decompositions

The covariances and variances between asset returns during the period of tranquility are given by:The covariances and variances between asset returns during the period of tranquility are given by:

During the period of turbulence, the variances and covariances of asset returns become:During the period of turbulence, the variances and covariances of asset returns become:

The change in volatility between the two periods is due to the existence of contagion: The change in volatility between the two periods is due to the existence of contagion:

In addition, the change in covariance between the two periods is given by:In addition, the change in covariance between the two periods is given by:

jixxE jitjti ],[ ,,

ixE iiti 222, ][

kjiyE kijiiiti 2,

2,

222, ][

kjiyyE kjkijijijijitjti ,,,,,, ][

kjixxEyyE kjkijijijitjtitjti ,,,,,,,, ][][

jixEyE kijititi 2,

2,

2,

2, ][][

A practical interpretation of asset returns volatility is provided by the decomposition of the variance-covariance matrix into the contributions of each shock.

The DataThe Data

Daily nominal exchange rates of three CEE currencies against the euro, namely the

Czech koruna (CZK), the Polish zloty (PLN) and the Romanian new leu (RON). The

data is obtained from the website of the European Central Bank .

The sampling period covers August 1st, 2005 to March 31st, 2009, considering:

- tranquil period: from August 1st, 2005 to August 29th, 2008

- turbulent period: from September 1st, 2008 to March 31st, 2009

The choice for the beginning of the sample is motivated by the rationale of constant

monetary policy regime so as to exclude any shifts in the links between currencies

that could be generated by a shift in the monetary policy of one of the countries

considered.

Preliminary AnalysisPreliminary Analysis

ADF test results indicate that all series in levels display a unit root . Consequently, the series are transformed into log-differences and continuously compounded percentage exchange rate returns are obtained (which are I(0)):

yt =100*( ln(St ) − ln(St −1)),

where St is the spot rate.

Daily percentage returns. The vertical line splits the sample into the two periods

analyzed.

LevelsLevels CZK PLN RON

ADF t-stat -1.659479 -0.478959 -0.306392

p-value 0.4517 0.8926 0.9214

Returns CZK PLN RON

ADF t-stat -29.60356 -28.13557 -27.43686

p-value 0.0000 0.0000 0.0000

CZK/EUR

-4

-3

-2

-1

0

1

2

3

4

08-2005 08-2006 08-2007 08-2008

PLN/EUR

-5

-4

-3

-2

-1

1

2

3

4

5

08-2005 08-2006 08-2007 08-2008

RON/EUR

-4

-3

-2

-1

1

2

3

4

08-2005 08-2006 08-2007 08-2008

Empirical ResultsEmpirical Results

Parameter GMM Estimate Standard error t-statistic

λ RON 0.1893 0.020

9.278 λ CZK 0.1254 0.013

9.649 λ PLN 0.4270 0.034 12.379

γRON 0.4503 0.016 27.729

γCZK 0.2993 0.016 18.275

γPLN 0.0465 0.304 0.153

δCZK,RON 0.4615 0.211 2.190

δPLN,RON 1.1139 0.076 14.529

δRON,CZK -0.5592 0.050 11.212

δPLN,CZK 0.5532 0.103 -5.165

δRON,PLN 0.0401 0.124 0.029

δCZK,PLN 0.7277 0.136 4.741

The unconstrained system of equations (1) and (2) by GMM using (3) as a criterion, with the moment conditions equal to the differences between the sample and the theoretical variances and covariances. The objective function is minimized using the OPTMUM procedure in Gauss.

We prefiltered the data by using a trivariate VAR(1) in the currencies returns (Dungey (2009)).

We have also adopted the approach of Forbes and Rigobon (2002), who considered in their empirical application US returns as common variable control, and estimated a VAR containing one lag and EUR/USD returns. This lead us to similar results.

The parameters estimates are presented bellow along with their corresponding standard errors and t-statistics.

Volatility Decomposition – Tranquil PeriodVolatility Decomposition – Tranquil Period

Variance decomposition (proportion)  

Factor RON/EUR CZK/EUR PLN/EUR

 

World 15.02 14.92 98.83  

Country 84.98 85.08 1.17  

Total 100 100 100  

Variance decomposition (using GMM)  

Factor RON/EUR CZK/EUR PLN/EUR

 

World 0.0358 0.0157 0.1823  

Country 0.2028 0.0896 0.0021  

Total 0.2386 0.1053 0.1844  

Covariance decomposition (using GMM)

Factor RON - CZK RON -PLN CZK - PLN

World 0.0237 0.0808 0.0535

Country - - -

Total 0.0237 0.0808 0.0535

The decompositions are based on GMM estimates.

The country-specific factors such as macroeconomic fundamentals account for about 85% of the volatility in the individual foreign exchange markets in Romania and Czech Republic, whilst in this group of countries PLN/EUR acts to a great extent as a common factor, which suggest a greater financial integration of the latter pair in the regional economy.

The funding that the volatility of Czech koruna is driven mainly by factors different from the ones influencing the other CEE exchange rates might reveal the CZK role of a funding currency for investments in other currencies of the region, as suggested by Pramor and Tamirisa (2006).

Variance -covariance matrix (from data)

Tranquil period

0.239 0.024 0.081

0.024 0.105 0.054

0.081 0.054 0.184

Volatility Decomposition – Turbulent PeriodVolatility Decomposition – Turbulent PeriodVariance decomposition (proportion) – turbulent period

Factor RON/EUR CZK/EUR PLN/EUR

World 6.48 1.85 10.53

Country 36.68 10.57 0.12

Contagion from RON 0 25.12 71.67

Contagion from CZK 56.55 0 17.68

Contagion from PLN 0.29 62.46 0

Total 100 100 100

Variance decomposition (using GMM) – turbulent period

Factor RON/EUR CZK/EUR PLN/EUR

World 0.0358 0.0157 0.1823

Country 0.2028 0.0896 0.0021

Contagion from RON 0 0.213 1.2407

Contagion from CZK 0.3127 0 0.3061

Contagion from PLN 0.0016 0.5295 0

Total 0.5529 0.8478 1.7312

Covariance decomposition (using GMM) – turbulent period

Factor RON - CZK RON -PLN CZK - PLN

World 0.0237 0.0808 0.0535

Contagion from RON 0.2078 0.5016 0.5140

Contagion from CZK -0.1673 -0.3093 0.1656

Contagion from PLN 0.0291 0.0018 0.0338

Total 0.0933 0.2749 0.7669

The variance decompositions show that

asset return volatility in the post-Lehman

period was dominated by contagion with much

smaller contributions from the world and

idiosyncratic factors. The PLN/EUR returns

experienced the greatest increase in variance

over the two periods with an important

contribution of contagion from both RON/EUR

(72%) and CZK/EUR (18%). The absolute levels

effects are greatest in the transmission from

Romanian leu to the Polish zloty. Significant

effects of contagion are observed in the other

returns as well, both direct and reverse, the

only exception being the absence of a

contagious channel from PLN/EUR to RON/EUR.

The increase in covariances over the two

periods can be also considered evidence of

contagion.

The Romanian leu was the most important

source of volatility in the region, while the

Polish zloty acted as the main shock absorber

(consistent with Borghijs and Kuijs (2004) ).

Contagion TestsContagion Tests

Bivariate results of tests of contagion - Wald tests of the null hypothesis of no contagion H0: δi,j = 0

Contagion from Jointly test statistic p-value

CZK/EUR 436.792* 0.000

PLN/EUR 58.913* 0.000

RON/EUR 219.610* 0.000

*1% significance level;**5% significance level; ***10%significance level; distribution χ2 with df=1

*1% significance level; distribution χ2 with df=2 

Multivariate tests of contagion

*1% significance level; distribution χ2 with df=6 

Contagion from

Contagion to

CZK/EUR PLN/EUR RON/EUR

Test statistic p-value Test statistic p-value Test statistic p-value

CZK/EUR - - 28.825* 0.000 127.200* 0.000

PLN/EUR 28.765* 0.000 - - 0.104 0.748

RON/EUR 4.794** 0.029 212.893* 0.000 - -

Joint test for the existence of contagion in FX markets

Test statistic p-value Contagion indicated

823.936* 0.000 YES

RemarksRemarks

The results of bivariate testing of each contagious channel within the region confirms the existence of contagion initially identified from the variance - covariance decompositions. The null hypothesis of no contagion is accepted only in case of the transmission from PLN/EUR to RON/EUR.

The multivariate tests examine whether a shock emerging in one particular foreign exchange market is transmitted to the others countries in the system. The significance results are consistent with the information from the bivariate tests as they find significant contagion from each asset market individually to the other two.

We also tested for the presence of contagion anywhere in the region, without a priori specifying a point of origin for that contagion. The null hypothesis of no contagion is rejected even at a significance level of 1%.

Concluding remarksConcluding remarks

Studying the propagation of shocks among the Romanian Leu, Polish Zloty and Czech Koruna , we were able to reject the null hypothesis of no contagion over the Post-Lehman period. We found that during periods of heightened volatility, in addition to common shocks and spillovers from some identifiable local channel, a new channel of volatility transmission emerge: contagion. Consequently, the linkages between the three currencies strengthen over turbulent periods, the result being consistent with that of Kóbor and Székely. We also discover that RON represents the most important source of volatility, whilst PLN only acts as a shock absorber within the region.

The conclusions laid above have very important implications for the conduct of monetary policy.

Policy makers should take into account other countries’ actions when making their own decisions. This is most obvious when considering the objective of euro adoption by the countries included in the analysis. In order to meet the Maastricht exchange rate stability criterion, the central banks will probably undertake intramarginal interventions in the foreign exchange markets to keep their currencies within the band. Having in view that these operations are almost by definition carried out in periods of heightened volatility, our results suggest that the intervention in one FX market will have strong and valuable influences on the other exchange rates. This calls for increased cooperation and coordination of monetary policy within the region.

From a different perspective, the detection of contagion in the three foreign exchange markets suggests that a solution to how these countries can reduce their vulnerability to external shocks during periods of high volatility consists of short-term strategies, like foreign exchange intervention.

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