Latin America suffer before each democratic election due to its structural clivage, high social inequity and demands. The objective of this paper is to show that at elections, for some importants countries, the contagion effects increase the correlation of the return series markets.Political Economics, Contagion Methods, Return Series, Electoral Cycles

Angelo Polydoro

English

Research Papers in Economics

Electoral Contagion in Latin America
Angelo Polydoro∗ †
July 1, 2004
Abstract
Latin America suﬀer before each democratic election due to its
structural clivage, high social inequity and demands. The objective
of this paper is to show that at elections, for some importants coun-
tries, the contagion eﬀects increase the correlation of the return series
markets.
JEL Classiﬁcation: C20, N26.
∗I would like to thank the revision work of Fabio Abe.
†e-mail:polydoro@fgvmail.br
11 Introduction
During the last presidential run, Brazil suﬀered a heavy uncertainty about
the running of economy at Lula’s government, the ﬁrst candidate on previous
bullets. Lula is the great worker’s leader of the PT (”‘Workers Party”’)1 .
At a proposed program for their administration, the leaders of PT proposed
a new model for the brazilian economy based on the government investments
and market misregulation 2. The brazilian ﬁnancial market index, called
Bovespa, and the exchange rate reﬂected the political uncertainty . Brazilian
exchange rate rose from 2.87 on 07/05/2002 to 3.69 at 10/04/20023.
Venezuela suﬀered in the same way at the electoral run before Chave’s
victory. Argentina after their crisis suﬀered due to populist promisses of
Kirshiner’s government.
The ﬁnancial market of the three countries related above experienced
days of high nervosism represented by an increase of volatility. One simple
question arises after a look of this data: Do the others latin countries suﬀer
due to this speciﬁc electoral uncertainty?
This paper will try to answer this question or simply clarify the problem.
We may think that in these episodes the uncertainty at one market spilled
to others. So this hypothesis will be discussed in a contagion framework.
The empirical methods of contagion stemfrom[Bek92], [ERFL90], [KW90]
and from historical papers like [Sha64], [GR71]. The current research focuses
on many branches: (i) Reactions to unexpected shocks or news (ii) Corre-
lation analysis (iii) Probability tests (iv) Extreme return tests (v) Others
tests, according to the classiﬁcation of the World Bank 4. This paper is
based on the correlation analysis and reactions to unexpected news. The
electoral run can be thought as a sussection of unexpected news of previous
ballots or macroeconomic results which can inﬂuentiate the performance of
the situation candidate.
Contagion is treated the same way of [Mas99] , i.e a crisis triggered that
can not be explained by changes in fundamentals or any sort of "‘mechani-
cal"’ spillover. This can be possibly caused by shifts in market sentiments.
The mechanical eﬀects cited above arise at balance of payments crisis, for
1See [Sam02] for a detailed view about PT
2”‘The implementation of our government program for Brazil, by a democratic and
popular, will represents a rupture to this economic model, founded at the market openness
and radical desregulation of the brazilian economy and thus subordination of their dynamic
to the globalized ﬁnancial capital humors and interests.”’ Olinda government program
12/2001.
3See in graph the exchange rate series of the electoral year.
4www.worldbank.gov
2example, transforms to deterioration of fundamentals in other countries.
The role of the investor in a electoral crisis episode can be appointed
in two ways: (1) An increase of risk perception in this emerging country
and/or increase of risk aversion by investors; (2) The implication of (1) is the
need to rebalance portfolios to contain "‘Value at Risk"’ and/or move their
investments to saﬀer countries. Due to the fact that each Latin economy is
connected with a high degree of trade with others, investors may forecast
that the other economies will suﬀer because of the electoral uncertainty at
the "‘ground zero"’ and start a crisis cycle.
Another way of contagion is through trade. With high levels of trade,
a ﬁnancial crisis aﬀects their trade partners by fall in demand. For the
latin america countries, treated at this work, this is an important link. The
exports of Argentina to Brazil represents about 30 percent of total exports.
Further works should treat this channel. Only the ﬁnancial channel will be
taken in account, because it seems that the stock market is more sensitive to
unanteciped news.5
At the second section the Forbes and Rigobon model used will be dis-
cussed and will be given explanation for the choice of this model instead of
others like the probability model.
The results of the regression treated at the second section will be in the
third section.
The last one concludes and gives same appointments for further research.
5See [Rij99] for a framework testing if the source of contagion is ﬁnance or trade.
32 Model
[FR02] deﬁned crisis as an increase in correlation between two countries
during a ﬁxed period. So it’s implemented by testing the contagion from the
host market, to another asset market. The test is based on the (uncondi-
tional) correlation between pairs of assets returns in the crisis and non-crisis
period. The correlation between the assets returns of country 1 to country 2
during the crisis period is
ρy =
Cov(y1,t,y2,t)
￿
V ar(y1,t)V ar(y2,t)
=
σy,1,2 ￿
σ2
y,1σ2
y,2
(1)
whilst
ρx =
Cov(x1,t,x2,t)
￿
V ar(x1,t)V ar(x2,t)
=
σx,1,2 ￿
σ2
x,1σ2
x,2
(2)
Represents the correlation in the pre-crisis period. If there’s an increase
in the volatility in the county’s asset return, σ2
y,1 > σ2
x,1 without having
any change in the two countries fundamentals, then ρy > ρx given the false
appearance of contagion. To adjust for this bias, Forbes and Rigobon show
that the adjusted (unconditional) correlation is given by
νy =
ρy ￿
1 +
￿
σ2
y,1−σ2
x,1
σ2
x,1
￿ (3)
To test that there is a signiﬁcant change in correlation, the null hypothesis
is
H0 : νy = ρx (4)
H1 : νy > ρx (5)
The t-statistic for testing these hypothesis is given by
FR1 =
ˆ νy − ˆ ρy ￿
1
Ty + 1
Tx
(6)
where the hat indicates the sample estimator, and Ty and Tx are the respec-
tive sample size of crisis and non-crisis periods. To improve the ﬁnite sample
properties , [For02] suggest using the Fisher transformation
FR2 =
1
2ln
￿
1+ ˆ νy
1− ˆ νy
￿
− 1
2ln
￿
1+ ˆ ρy
1− ˆ ρy
￿
￿
1
Ty−3 + 1
Tx−3
(7)
4An alternative way to implement the Forbes and Rigobon test is to scale the
asset returns and perform the contagion test within a regression framework.
This framework will perform a contagion test from the asset market of coun-
try 1 to the asset market of country 2, scalling the assets returns during the
pre-electoral period by their respective standard deviations. Following the
regression:
￿
z2,t
σx,2
￿
= γ0 + γ1dt + γ2
￿
z1,t
σx,1
￿
+ γ3
￿
z1,t
σx,1
￿
dt + ηt (8)
where zt is the all observated data, i.e. zt = x1,x2,...,xTx,y1,y2,...,yTy.
ηt is a normally distributed white noise. dt is a crisis dummy with 1 for 30,
90, 180 days before elections. Thus Forbes and Rigobon contagion test can
be implemented by estimating the equation above by OLS and performing a
one-sided-t-test of:
H0 : γ3 = 0 (9)
3 Results
The data used were the return series calculated by the stock market index
of each country6.
The model given at equation 8 was estimated with Newey and West(1987)
estimator that is consistent in the presence of both heteroskedasticity and
autocorrelation of unknown form. The three kinds of dummy variables used
was the 30, 90 and 180 days before presidential elections7.
The contagion test (γ3 = 0) performed for all the seven countries gave
interesting results. The contagion tends to persist withing groups, i.e. if
the host eﬀects other in 30 days it tends to eﬀect on the mid and long run
(90,180 days). Despite pointed view about the results, important countries
eﬀects others and aren’t eﬀected. Brazil and Argentina contagy all coun-
tries, Chile and Venezuela don’t aﬀect only Ecuador, Colombia and Ecuador
eﬀect any country. The three most important countries (Brazil, Argentina,
Chile) in general are only aﬀected by itself and by Venezuela. Remember
that Venezuela is a great oil exporter. The contagion coeﬃcient for the sig-
niﬁcative equations can be either positive or negative, but remember that
we’re working with the return series, so a 1 percent change on the foreign
market at the elections episode change the home stock market around 0.003
percent on average, thus a very insigniﬁcant one.
6Brazil: BOVESPA, Argentina: Merval, Chile etc...
7See table ? for a short description about the governance pattern of the ﬁve Latin
America countries treated here.
5Another issue that can be treated using the framework given at the second
section is the interaction between stock markets. The coeﬃcient γ2 gives the
inﬂuence of a market to another, so if γ2 is signiﬁcative the stock markets
are related. The results of this test are given at interdependence subsection
of appendix. Save Colombia and Ecuador all the Latin America markets are
related, an intuitive result.
4 Conclusion
Uncertainty derived by electoral campaign eﬀects the others, if it is an
important country, increasing the correlation between these markets. This
eﬀect cannot be related to changes on fundamentals. Given the information
cost investors may choose to invest their resourses on more safety countries
increasing the volatility at the short-run.
Many works in the literature shows that the crisis on 90’s is due to a
high correlation at the Latin America countries do not taken into account on
portfolio management. The results presented reinforce this intuitive point of
view.
One important issue which isn’t regarded at this work is the source of
uncertanity. [Dun99] shows that the source of contagion can be their own
volatility or the eﬀects of the partner. All the kinds of contagion models used
on the literature can be used with this same idea8.
It’s also important to verify if political variables like fragmentation of par-
lament or coalition discipline aﬀects the contagion eﬀects at the pre-elections
period.
8See [Dun03] for an excelent survey of the empirical literature of contagion.
6References
[Bek92] R. Bekaert, G. ; Hodrick. Characterising predicable components
in excess returns on equity and foreign exchange markets. Journal
of Finance, 47:467—509, 1992.
[Dun99] M. Dungey. Decomposing exchange rate volatility around the
asia-paciﬁc rim. Journal of Asian Economics, 10(4):525—35, 1999.
[Dun03] M. Dungey. Empirical modelling of contagion:
A review of methodologies. Downloaded from :
http://rspas.anu.edu.au/economics/staﬀ/dungey/mardihp.htm,
2003.
[ERFL90] T. Ito Engle R. F. and W. Lin. Meteor showers or heat waves?
heteroskedastic intra-daily volatility in the foreign exchange mar-
ket. Econometrica, 58:525—42, 1990.
[For02] R. Forbes, H. ; Rigobon. International Financial Contagion,
chapter Measuring Contagion, Conceptual and Empirical Issues.
Kluwer Acedemic Publishers, 2002.
[FR02] K. Frobes and R. Rigobon. No contagion only interdependence:
Measuring stock market co-movements. Journal of Finance, 57(5),
2002.
[GR71] H. G. Grubel and Fadner R. The interdependence of international
equity markets. Journal of Finance, 26(89-94), 1971.
[KW90] M. King and S. Wadhwani. Transmission of volatility between
stock markets. Review of Financial Studies, 3(1):5—33, 1990.
[Mas99] P. Masson. Multiple equilibria, contagion and the emerging mar-
ket crisis. Working Paper 164, IMF, 1999.
[Rij99] B. Rijckeghem, C. ; Weder. Sources of contagion: Finance or
trade? Working Paper 146, IMF, 1999.
[Sam02] D. Samuels. From socialism to social democracy? the evolution of
the worker’s party in brazil. Comparative Political Studies, 37(9),
2002.
[Sha64] W. Sharpe. Capital asset prices: A theory of market equilibrium
under conditions of risk. Journal of Finance, 19:425—442, 1964.
75 Appendix
Exchange Rate (R$/US$)
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Table 1: Argentina Contagion Coeﬃcient
Independent Variable: Argentina
30 days 90 days 180 days
Brazil 0.218529 -0.189462*** -0.210917***
Chile -0.047913 -0.070011 -0.119615*
Colombia -0.571645*** -0.236845 -0.071792
Ecuador 2.320181*** -0.013511 -0.081506
Peru 0.02833 0.209199*** 0.19574***
Venezuela -0.048361 0.139241 0.178971***
*p-value≤0.05. ***p-value≤0.01.
8Table 2: Brazil Contagion Coeﬃcient
Independent Variable: Brazil
30 days 90 days 180 days
Argentina -0.104585 0.258662*** 0.23567***
Chile -0.160461 -0.152713*** -0.112484***
Colombia 0.163529* 0.18594*** 0.019497
Ecuador 2.430389*** 0.197596*** 0.000646
Peru 0.083649 0.157125*** 0.152995***
Venezuela -0.022278 0.114211 0.138536***
* p-value≤0.05. ***p-value≤0.01.
Table 3: Chile Contagion Coeﬃcient
Independent Variable: Chile
30 days 90 days 180 days
Argentina 0.001787 0.45507*** 0.35159***
Brazil 0.385304*** -0.106221 -0.12985
Colombia NA NA NA
Ecuador -0.189957 0.122415 -0.05282
Peru 0.187583 0.290995*** 0.28984***
Venezuela -0.049767 0.323566*** 0.312916***
* p-value≤0.05. ***p-value≤0.01.
Table 4: Colombia Contagion Coeﬃcient
Independent Variable: Colombia
30 days 90 days 180 days
Argentina 0.148062 0.136171 0.155355
Brazil -0.276163 -0.146646 -0.054369
Chile NA NA NA
Ecuador -0.615335*** -0.19811 -0.028973
Peru NA NA NA
Venezuela NA NA NA
* p-value≤0.05. ***p-value≤0.01.
9Table 5: Ecuador Contagion Coeﬃcient
Independent Variable: Ecuador
30 days 90 days 180 days
Argentina -0.119285 -0.048727 -0.107514
Brazil -0.019116 -0.006125 0.024688
Chile -0.01224 0.09127 -0.010395
Colombia -0.0211 0.074314 0.05864
Peru 0.079733* 0.110126 0.145586
Venezuela -0.045106 -0.051967 -0.05124
* p-value≤0.05. ***p-value≤0.01.
Table 6: Peru Contagion Coeﬃcient
Independent Variable: Peru
30 days 90 days 180 days
Argentina -0.069002 -0.049357 0.196562
Brazil 0.029839 0.041925 0.02379
Chile -0.291527 -0.366828*** -0.276939***
Colombia NA NA NA
Ecuador -0.420996 -0.19719*** -0.189059***
Venezuela 0.167171*** 0.110583 0.161827***
* p-value≤0.05. ***p-value≤0.01.
Table 7: Venezuela Contagion Coeﬃcient
Independent Variable: Venezuela
30 days 90 days 180 days
Argentina -0.124989 -0.220674*** -0.14573***
Brazil 0.155517 0.286918*** 0.130246*
Chile -0.286951 -0.196506* -0.161039*
Colombia NA NA NA
Ecuador 0.847297 0.095275 0.02014
Peru -0.181823*** -0.128931*** -0.124309***
* p-value≤0.05. ***p-value≤0.01.
10Table 8: Argentina Interdependence Coeﬃcient
Independent Variable: Argentina
30 days 90 days 180 days
Brazil 0.298459*** 0.330643** 0.3472***
Chile 0.301954*** 0.301879*** 0.304583***
Colombia -0.006872 -0.002637 0.002397
Ecuador 0.00391 0.004659 0.006134
Peru 0.188215*** 0.157591*** 0.153539***
Venezuela 0.169059*** 0.15047*** 0.124516***
* p-value≤0.05. ***p-value≤0.01.
Table 9: Brazil interdependence Coeﬃcient
Independent Variable: Brazil
30 days 90 days 180 days
Argentina 0.311152*** 0.297205*** 0.280068***
Chile 0.318021*** 0.321612*** 0.322375***
Colombia 0.004812 -0.001671 0.001342
Ecuador -0.011931 -0.012664 -0.011667
Peru 0.216363*** 0.191462*** 0.186616***
Venezuela 0.142209*** 0.127965*** 0.10895***
* p-value≤0.05. ***p-value≤0.01.
Table 10: Chile interdependence Coeﬃcient
Independent Variable : Chile
30 days 90 days 180 days
Argentina 0.301791*** 0.282665*** 0.269013***
Brazil 0.30101*** 0.321223*** 0.328863***
Colombia NA NA NA
Ecuador 0.00114 0.0000839 0.00251
Peru 0.250626*** 0.215875*** 0.209195***
Venezuela 0.18566*** 0.151338*** 0.122725***
* p-value≤0.05. ***p-value≤0.01.
11Table 11: Colombia interdependence Coeﬃcient
Independent Variable: Colombia
30 days 90 days 180 days
Argentina -0.015847 -0.016209 -0.025701
Brazil 0.033602 0.039352 0.028662
Chile NA NA NA
Ecuador -0.040382 -0.038095 -0.040103
Peru NA NA NA
Venezuela NA NA NA
* p-value≤0.05. ***p-value≤0.01.
Table 12: Ecuador interdependence Coeﬃcient
Independent Variable: Ecuador
30 days 90 days 180 days
Argentina 0.008503 0.010143 0.026748
Brazil -0.016981 -0.015402 -0.021661
Chile 0.001181 -0.000924 0.00192
Colombia -0.038178 -0.041484 -0.048566
Peru -0.002704 -0.018883 -0.03112
Venezuela 0.051921 0.056875 0.059246
* p-value≤0.05. ***p-value≤0.01.
Table 13: Peru interdependence Coeﬃcient
Independent Variable: Peru
30 days 90 days 180 days
Argentina 0.256075*** 0.263098*** 0.228705***
Brazil 0.337256*** 0.302647*** 0.292261***
Chile 0.291073*** 0.303999*** 0.311242***
Colombia NA NA NA
Ecuador 0.001369 0.002432 0.004954
Venezuela 0.175151*** 0.167026*** 0.142986***
* p-value≤0.05. ***p-value≤0.01.
12Table 14: Venezuela interdependence Coeﬃcient
Independent Variable: Venezuela
30 days 90 days 180 days
Argentina 0.221467*** 0.2372*** 0.243394***
Brazil 0.2241*** 0.179786*** 0.187595***
Chile 0.195783*** 0.199255*** 0.203374***
Colombia NA NA NA
Ecuador 0.051696 0.052008 0.054644
Peru 0.218795*** 0.224033*** 0.230501***
* p-value≤0.05. ***p-value≤0.01.
13

Contagion in Latin America

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