Transcript Contagion Phenomena among Central and Eastern European

ACADEMY OF ECONOMIC STUDIES
DOCTORAL SCHOOL OF FINANCE AND BANKING
Contagion Phenomenon among Central and
Eastern European Currencies
Student: Roteanu Cosmina Georgiana
Bucharest, July 2009
Dissertation paper outline
The importance of contagion among CEE currencies
The aims of the present paper
Brief review of the literature on contagion
Model specifications
Data, estimation and results
Conclusions
References
The 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 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 crosscountry 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” contagion
Kaminsky et al.(2000) : fundamentals – based contagion
common cause contagion
pure contagion
o
Restrictive Definition
(World Bank)
“Contagion is the transmission of shocks to other countries or the crosscountry 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.”
•
It needs to control for general volatility rising during financial crises
Our version
Dungey et al. (2005) definition of contagion as “unexpected shocks” or news
•
Other terms: “pure contagion”, “shift contagion”
•
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 fundamental linkages.
Measure of Contagion
Latent Factor Model proposed by Dungey et al. (2005)
•
It includes global and country factors to capture market fundamentals and also additional
movements over and above market fundamentals during crisis period to account for contagion
•
This modeling strategy is shown to encompass many of the existing approaches to measuring
contagion, including
o
the correlation analysis proposed by Forbes and Rigobon (2002)
o
the vector autoregression (VAR) approach of Favero and Giavazzi (2002)
o
o
the probability models of Eichengreen et al. (1996)
the co-exceedance approach of Bae et al. (2003)
DFGM - A Model of Interdependence

Latent factor model of asset returns during non-crisis period

Based on Arbitrage Pricing Theory
x1,t = λ1wt + γ1u1,t
x2,t = λ2wt + γ2u2,t
(1)
x3,t = λ3wt + γ3u3,t
xt – returns during non-crisis period
wt – world factor (common shock)
ui,t – idiosyncratic factor
λi, γi >0 – factor loadings that determine the contribution of each shock to the volatility of asset markets
Assumptions:
o the factors are stochastic processes with zero mean and constant variance:
wt ~ (0,1)
ui,t ~ (0,1)
o all factors are independent:
E[ui ,t , u j ,t ]  0i  j
E[ui,t , wt ]  0i
DFGM - A Model of Contagion
The factor model is augmented to allow an avenue for contagion between the foreign exchange markets in all
directions:
y1,t = λ1wt + γ1u1,t + δ1,2 u2,t+ δ1,3 u3,t
y2,t = λ2wt + γ2u2,t + δ2,1 u1,t + δ2,3 u3,t
(2)
y3,t = λ3wt + γ3u3,t + δ3,1 u1,t+ δ3,2 u2,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 GMM: q = arg G’WG (Hamilton, 1994)
(3)
Θ
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 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 capturing the effect of contagion are identified by the empirical
moments from the variance-covariance matrix of the crisis period.
Variance - Covariance Decompositions

The covariances and variances between asset returns during the period of tranquility are given by:
E[ xi ,t , x j ,t ]  i  j i  j
E[ xi ,t ]  i   i i
2

2
2
During the period of turbulence, the variances and covariances of asset returns become:
E[ yi ,t ]  i   i   i , j   i ,k i  j  k
2
2
2
2
2
E[ yi ,t y j ,t ]  i  j   i j ,i   ji , j  i ,k j ,k i  j  k

A practical interpretation of asset returns volatility is provided by the decomposition of the
variance-covariance matrix into the contributions of each shock.
 The change in volatility between the two periods is due to the existence of contagion:
E[ yi ,t ]  E[ xi ,t ]   i , j   i ,k i  j
2
2
2
2
 In addition, the change in covariance between the two periods is given by:
E[ yi,t y j ,t ]  E[ xi,t x j ,t ]   i j ,i   ji , j  i,k j ,k i  j  k
The 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 Analysis
Daily percentage returns. The vertical line splits
the sample into the two periods analyzed.
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)),
CZK/EUR
4
3
2
1
0
-1
-2
-3
-4
08-2005
08-2006
where St is the spot rate.
08-2007
08-2008
PLN/EUR
5
4
3
2
Levels
ADF t-stat
p-value
CZK
PLN
RON
-1.659479
-0.478959
-0.306392
0.4517
0.8926
0.9214
1
-1
-2
-3
-4
-5
08-2005
Returns
ADF t-stat
CZK
PLN
08-2006
RON
-29.60356
-28.13557
-27.43686
0.0000
0.0000
0.0000
08-2007
08-2008
RON/EUR
4
3
p-value
2
1
-1
-2
-3
-4
08-2005
08-2006
08-2007
08-2008
Empirical Results
 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.
Parameter
λ RON
GMM Estimate
Standard error
0.1893
t-statistic
0.020
9.278
λ CZK
λ PLN
γRON
γCZK
γPLN
δCZK,RON
δPLN,RON
δRON,CZK
δPLN,CZK
δRON,PLN
δCZK,PLN
0.1254
0.4270
0.4503
0.2993
0.0465
0.4615
1.1139
-0.5592
0.5532
0.0401
0.7277
0.013
0.034
0.016
0.016
0.304
0.211
0.076
0.050
0.103
0.124
0.136
9.649
12.379
27.729
18.275
0.153
2.190
14.529
11.212
-5.165
0.029
4.741
Volatility Decomposition – Tranquil Period
 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 decomposition (proportion)
Factor
0.024
CZK/EUR
PLN/EUR
World
15.02
14.92
98.83
Country
84.98
85.08
1.17
100
100
100
Total
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)
Variance -covariance matrix (from data)
0.239
RON/EUR
0.081
0.024
0.105
0.054
0.081
0.054
0.184
Tranquil period
Factor
RON - CZK
RON -PLN
World
0.0237
0.0808
Country
-
-
Total
0.0237
0.0808
CZK - PLN
0.0535
0.0535
Volatility Decomposition – Turbulent Period
Variance decomposition (proportion) – turbulent period
Factor
World
RON/EUR
CZK/EUR
PLN/EUR
6.48
1.85
10.53
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
Country
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 Tests
Bivariate results of tests of contagion - Wald tests of the null hypothesis of no contagion H0: δi,j = 0
Contagion to
CZK/EUR
Contagion from
PLN/EUR
Test statistic
p-value
-
-
CZK/EUR
PLN/EUR
28.765*
0.000
RON/EUR
4.794**
0.029
RON/EUR
Test statistic
p-value
Test statistic
p-value
28.825*
0.000
127.200*
0.000
0.104
0.748
-
-
212.893*
0.000
-
-
*1% significance level;**5% significance level; ***10%significance level; distribution χ2 with df=1
Joint test for the existence of contagion in FX markets
Multivariate tests of contagion
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; distribution χ2 with df=2
Test statistic
p-value
Contagion indicated
823.936*
0.000
YES
*1% significance level; distribution χ2 with df=6
Remarks
 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 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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