The Impact of Foreign Exchange Intervention in Colombia. An Event Study Approach
| Autor | Juan José Echavarría, Luis Fernando Melo, Mauricio Villamizar |
| Páginas | 7-31 |
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D E S A R R O . S O C . 71, P R I M E R S E M E S T R E D E 2013, P P . X-X X , I S S N 0120-3584
Revista
Desarrollo y Sociedad
73
Primer semestre 2014
PP. 7-31, ISSN 0120-3584
The Impact of Foreign Exchange Intervention in
Colombia. An Event Study Approach1
El impacto de las intervenciones cambiarias en
Colombia. Un estudio de eventos
Juan José Echavarría
Luis Fernando Melo
Mauricio Villamizar
DOI: 10.13043/DYS.73.1
Abstract
To date, there is still great controversy as to which exchange rate model should
be used or which monetary channel should be considered, when measuring
the effects of monetary policy. Since most of the literature relies on structural
models to address identification problems, the validity of results largely turn
on how accurate these assumptions are in describing the full extent of the
economy. In this paper we compare the effects of different types of central
bank intervention for the Colombian case during 2000-2012, without imposing
restrictive parametric assumptions or without the need to adopt a structural
model. Using an event study approach, we find that all types of interventions
(international reserve accumulation options, volatility options and discretion-
ary) have been successful according to the smoothing criterion. In particular,
volatility options had the strongest effect. Results are robust when using dif-
ferent windows sizes and counterfactuals.
1 Authors’ email: jechavso@banrep.gov.co, lmelovel@banrep.gov.co, mvillavi@banrep.gov.co.
The views expressed in this manuscript are not necessarily those of the Central Bank of Colombia
or of its Board of Directors. Any errors are the responsibility of the authors. The authors gratefully
acknowledge the assistance of Santiago Tellez.
Este artículo fue recibido el 26 de octubre de 2013; revisado el 19 de febrero de 2014 y, finalmente,
aceptado el 11 de marzo de 2014.
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Key words: Central bank intervention, foreign exchange intervention mecha-
nisms, event study.
JEL classification: E52, E58, F31.
Resumen
Hasta la fecha, hay gran controversia sobre el modelo de tipo de cambio que
se debe utilizar o el mecanismo de transmisión que debe ser considerado para
medir los efectos de la política monetaria. Dado que la mayoría de la literatura
se basa en modelos estructurales como estrategia de identificación, la validez
de los resultados depende, en gran medida, de la validez de sus supuestos. Este
artículo compara los efectos de diferentes tipos de intervenciones para el caso
colombiano durante el período 2000-2012, sin imponer supuestos paramétri-
cos restrictivos y sin la necesidad de adoptar un modelo estructural. Nuestros
resultados muestran que todos los tipos de intervención cambiaria (opciones
de acumulación de reservas, opciones de volatilidad e intervenciones discrecio-
nales) han tenido éxito según el criterio de suavización en el estudio de even-
tos. En particular, las opciones de volatilidad parecen haber tenido el mayor
efecto. Los resultados son robustos cuando se utilizan ventanas de diferentes
tamaños y diferentes escenarios.
Palabras clave: intervenciones de bancos centrales, intervenciones cambiarias,
estudio de eventos.
Clasificación JEL: E52, E58, F31.
Introduction
In context of discretionary central bank intervention, monetary authorities sys-
tematically react to informative variables when setting their policy decisions,
i.e. the timing and magnitude of interventions are driven by market behav-
ior in order to meet explicit or implicit policy objectives. As such, research-
ers usually assume functional forms of both the policy rule and the process
determining the economy in order to estimate causal effects. However, since
most of these studies purely rely on structural models to address identifica-
tion problems (see Christiano, Trabandt and Walentin, 2011) then the validity
Juan José Echavarría, Luis Fernando Melo y Mauricio Villamizar 9
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of results largely depends on how accurate the assumptions are in describing
the full extent of the underlying economy.
To date, there is still great controversy as to which exchange rate model should
be used (stock, monetary, microstructure-based, etc.) or which monetary chan-
nel should be considered (signaling, portfolio, or expectations), when measuring
the effects of policy. Moreover, the Colombian case poses additional meth-
odological challenges since there have been multiple mechanisms of foreign
exchange rate intervention. These consist of: international reserve accumula-
tion and volatility options in the first part of the 2000s, discretionary (dirty)
interventions during 2004-2007 and day-to-day constant and preannounced
interventions during 2008-2012. A better understanding of these mechanisms
and their effects is hence warranted, without imposing restrictive parametric
assumptions or without the need to adopt a full-blown structural model.
While several other countries (e.g. Mexico, Turkey, Japan or the Czech Repub-
lic) have also conducted policy with multiple intervention mechanisms, few of
them have intervened in a systematic way. For instance, countries like Japan
exhibited large-scale but sporadic foreign exchange interventions (i.e. they
purchased 24 billion dollars in September 2010). Also, countries like Mexico
have held pre-established dates to accumulate reserves with the adoption of
volatility options (1996-2001). The Colombian experience is thus an interesting
case study because its policy framework has consisted of explicit interventions
which systematically reacted to either past movements in the exchange rate
(volatility options) or the behavior of monetary authorities (discretionary).
In this paper we compare the effects of international reserve accumula-
tion, volatility options and discretionary interventions,2 using an event study
approach. This paper is complementary to the work of Echavarría, Melo and
Villamizar (2013) which focuses on preannounced interventions. Following the
methodology presented in Fatum and Hutchison (2001), we define four criteria
to evaluate a successful intervention: 1) Direction (Frankel, 1994); 2) Reversal
(Fatum and Hutchison, 2001); 3) Smoothing (Humpage, 1996); and 4) Match-
ing. Results show that all types of interventions were successful according
to the smoothing criterion. In particular, volatility options had the strongest
2 Preannounced interventions were not used given the few events available.
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effect according to several criteria. Results are robust when using different
windows sizes and counterfactuals.
This paper is organized as follows: Section I provides a general overview of
the Colombian foreign exchange rate intervention. Parts of this section (in
italics) are taken directly from Echavarría et al. (2013). Section II describes
the event study methodology and Section III presents the results. Finally, sec-
tion IV concludes.
I. Foreign Exchange Interventions
Foreign exchange interventions for the Colombian case during the period 2000-
2012 are summarized in Graph 1. Average yearly purchases were close to US$
2,200 million, much larger than average sales (US$ 571 millions). Purchases
were especially high in 2005 and 2007, and also during 2010-20123. Yearly
purchases represented 0.12% of (yearly) market transactions in and 4.06% in
2005, with an average of 1.70% in 2000-2012. They represented 1.0% of the
average stock of international reserves in 2003 and 33% in 2005, with an aver-
age of 11.86% in 2000-20124.
Table 1 shows the relative importance of the different mechanisms of interven-
tion: options for reserve accumulation, options for volatility control, discretion-
ary interventions, and fixed (close to) US$ 20 million per day interventions.5
Put options for reserve accumulation, partially implemented to replenish the
strong reduction of international reserves observed in 1997-2000, accounted
for all purchases in 2000-2003, while discretional interventions explained a
large part of purchases in 2004-2007. The amounts and periods of interven-
tions were initially announced, but that practice changed when periods and
amounts became indefinite.
3 There were some sales of US$ dollars to the government in 2004-2006, intended to repay external
debt.
4 Daily transactions in the market were close to US$ 1000 million at the end of the sample, and to US$
320 million in 2001-2004 (average). The stock of international reserves was close to US$ 33,000 mil-
lion at the end of the sample and to US$ 10,611 in 2001-2004 (average).
5 Next day purchases accumulate when there is a holiday in the United States or when t-1 auctions are
not fully exercised.
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Graph 1. Colombian Central Bank Interventions
5000
4000
3000
2000
1000
0
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Purchases Sales Sales to government
US $Millions
Source: Banco de la República and authors’ calculations. The year 2012 includes January - August.
Volatility options were used to buy and (mainly) sell foreign currency in some
days in 2004, 2006, 2007, 2008 and 2009. Options were auctioned automati-
cally whenever the difference between the exchange rate of the previous day
(the TRM) and the moving average of the last twenty days was higher or lower
than 5%. This percentage changed to 4% in December 2001; to 2% in Febru-
ary 6, 2006; to 5% in June 24, 2008; and to 4% in October 13, 2011. However,
volatility options have not been used during the last years, partially because
there are doubts about their impact, and partially because they could conflict
with the effect of the US$ 20 million purchases (the central bank could be
selling and buying dollars during the same day).
Put/call options for reserve accumulation were auctioned monthly and agents
had the right to exert them (totally or partially) during the next month, as
long as the exchange rate was lower than the average of the last 20 days. This
meant that international reserves were bought at a “low” price (opposite for
sales). The Board of the Central Bank could announce a new auction during
the month even if the previous auction had not yet expired.
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Table 1. Colombian Central Bank Interventions, 2000-2012
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012*
PURCHASES (US $) 319 629 252 106 2905 4658 1781 5082 2381 539 3060 3720 2840
Participation(%)
Options Put 100 100 100 100 54 0 33 11 41 100 0 0 0
International Reserve
Accumulation 100 100 100 100 48 0 0 0 19 0 0 0 0
Volatility Options 0 0 0 0 6 0 33 11 22 100 0 0 0
$20 million/day aprox. 0 0 0 0 0 0 0 0 59 0 100 100 100
Discretional Intervention 0 0 0 0 46 100 67 89 0 0 0 0 0
SALES (US $) 0 0 414 345 500 3250 1944 369 235 369 0 0 0
Participation (%)
Options Call 0 0 100 100 0 0 49 100 100 100 0 0 0
International Reserve
Reduction 000100000000000
Volatility Options 0 0 100 0 0 0 49 100 100 100 0 0 0
Sales to National Government 0 0 0 0 100 100 51 000000
NET PURCHASES 319 629 -163 -238 2405 1408 -164 4713 2147 171 3060 3720 2840
Source: Author’s calculations. * The year 2012 includes January - August.
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Graphs 2 and 3 show the evolution of the different types of foreign exchange
intervention and the nominal exchange rate
St
()
, for both Colombia and Bra-
zil during 2000-2012. Discretional interventions
Idisc
p
and preannounced inter-
ventions of US$ 20 million
Ip
20
are shown in Graph 2 and reserve accumulation
Ires opt
p
_
and volatility options
Ivol opt
p
_
in Graph 3. In total, there were 723 days of
discretionary purchases, with an average of US$ 20 million and a maximum of
US$ 723 million (on March 390, 2007); 437 days of US$ 20 million interven-
tions distributed in four episodes; 80 days of reserve accumulation (purchases)
with an average of US$ 41 million and a maximum of US$ 200 million; and
41 days of volatility option purchases with an average of US$ 51 million and
a maximum of US$ 170 million.
Graph 2. Different Types of Intervention and the Nominal Exchange Rate in Colombia
and Brazil
200
150
100
50
2000 2002 2004 2006 2008 2010 2012
p
disc
I
Colombia
S
Brazil
S
800
400
200
50
600
p
20
I
Index
Interventions (US$ Millions)
Source: Central Bank of Colombia and Authors’ Calculations.
Overall, exchange rate interventions in Colombia have been relatively transpar-
ent (See Ramírez, 2005). Options are announced on the same day that they are
exercised (the name of the firm remains secret) and the amount of interven-
tion is announced each week. Very often the Board of Directors pre-announced
the total amount of dollars to be purchased/sold during the next months. For
example, the Board announced an intervention of US$ 1 billion during the
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last three months of 2004,6 and in June 20, 2008 the Board announced the
new US$ 20 million daily interventions, with an amount of US$ 2.4 billion to
be bought between July and December.
To date, there is still a general lack of consensus within the literature regard-
ing the effectiveness of Central Bank intervention. This, in part, is the result of
the different methodologies employed. Studies that have used a GARCH meth-
odology in Colombia include Toro and Julio (2005), Kamil (2008), Echavarria,
Vasquez and Villamizar (2009b), Rincon and Toro (2010). On the other hand,
studies that have used structural Vector Autoregressions (SVARs) or General
Equilibrium models (DSGEs) include Echavarria, Lopez and Misas (2009a) and
Vargas, Gonzales and Rodriguez (2013).
Results of these studies vary in terms of both significance and duration of
policy effects. And, since most studies purely rely on parametric assumptions
to model the behavior of monetary authorities, then the validity of estima-
tions largely turn on how accurate these assumptions are in describing the
full extent of the underlying economy. Hence, this paper will help shed some
6 But in December 2004 the Board announced additional undefined interventions and periods.
Graph 3. Different Types of Intervention and the Nominal Exchange Rate in Colombia
and Brazil
200
150
100
50
2000 2002
p
res_opt
I
Colombia
S
Brazil
S
50
Index
Interventions (US$ Millions)
2004 2006 2008 2010 2012
800
600
400
200
0
200
150
100
50
2000 2002
p
vol_opt
I
Colombia
S
Brazil
S
Index
2004 2006 2008 2010 2012
800
600
400
200
Source: Central Bank of Colombia and Authors’ Calculations.
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light on the effects of foreign exchange intervention using an event study
approach, without imposing restrictive parametric assumptions.
II. Methodology: An Event Study Approach
Event studies were originally applied in the area of finance (MacKinlay, 1997),
but in recent years they have also been used in areas as diverse as: the impact
of different local factors on financial crisis (IMF, 2007, pp.124-132), the rela-
tionship between the development of capital markets and the environment in
emerging countries (Dasgupta, Laplante and Mamingi, 1997), the effects of
fiscal policy in the process of disinflation (Celasum, Gelos and Pratti, 2004),
and even the impact of the merits of the Central Bank Governor on financial
markets (Kuttner and Posen, 2007).
There are some limitations when using a non-parametric approach to estimate
the effects of policy. One of these drawbacks consists of a certain degree of
subjectivity when choosing the window size of the event window, event and
post-event. While we refer to standard cross-validation techniques and allow
for multiple window sizes for robustness, it is usually the case that large win-
dows over-smooth the density of the underlying data structure. On the other
hand, small bandwidths might reduce the bias but at the expense of obtain-
ing a larger variance in the estimates. Also, the longer the event window is
defined, the fewer events are found within the sample. Finally, long pre and
post estimation windows increase the likelihood of exogenous shocks (for-
eign and domestic) that might affect the exchange rate (always expressed as
Pesos per Dollar). Bearing these limitations in mind, we believe that our event
study approach holds clear advantages over the bulk of the literature that uses
restrictive parametric assumptions.
In the related literature, Humpage (1996), Fatum and Hutchison (2001), Fatum
and Hutchison (2008) and Fratzscher (2012) used event studies to analyze the
effect of interventions on the exchange rate. All of them conclude that inter-
ventions produce the desired results, even when considering a 15 day window
(the longest period considered by most of them). For Fratzscher (2012, pp. 739)
“overall, there is overwhelming evidence that both actual and oral interven-
tion events for the G3 economies have been successful”, and the success rate
remains relatively stable when extending the time window to 40 days.
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In this section we compare the cumulative effect of the different types of
foreign exchange intervention. We exclude day-to-day constant and prean-
nounced interventions from our analysis given the few events available. The
methodology starts with the definition of the event window comprised by: a)
the pre-event window, b) purchases and sales of foreign exchange (the event);
and c) the post-event window.
Following Fatum and Hutchison (2001), Hutchison (2002) and Fratzscher
(2012), we consider a sensitivity analysis for pre and post events of 2, 5, 10 and
15 days. Additionally, we define the event as the cluster of foreign exchange
intervention in which the Central Bank did not stop intervening for 2, 5, 10 or
15 days. In other words, the event begins when the central bank first conducts
purchases or sales in the foreign exchange market and ends when 2, 5, 10 or
15 consecutive days have elapsed without interventions. We then define four
criteria to evaluate a successful intervention: 1) Direction (Frankel, 1994); 2)
reversal (Fatum and Hutchison, 2001); smoothing (Humpage, 1996); and 4)
Matching. They can be summarized as follows:
• TheDirectioncriterionconsidersa successfulevent whenthe exchange
rate depreciates (appreciates) after USD purchases (USD sales), without
any regard about the trend of the exchange rate before intervention. As
Frankel (1994) argues, a successful intervention means that the exchan-
ge rate moves in the direction wanted by the central bank. In this sense,
the Direction criterion does not take into account the behavior of the
exchange rate before interventions take place. The central bank could
simply be following a leaning-with-the-wind policy, with the behavior of
the exchange rate probably dictated by market conditions.
• TheReversalcriterion ismoredemanding,and successrequiresthat the
exchange rate depreciates (appreciates) after USD purchases (USD sales).
The difference with the direction criterion is that it now requires the
exchange rate to be appreciating (depreciating) before an intervention
episode.
• TheSmoothing criterionalso considers thepre-intervention period,but
it is less demanding. This criterion defines success when exchange rate
appreciations (depreciations) are lesser in magnitude after USD purchases
(USD sales).
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• TheMatchingcriterionissimilartothesmoothingcriterionbutconsiders
the magnitude of exchange rate changes as opposed to comparing the
number of successful events. Hence, the matched sample test consists of
verifying whether the behavior of the exchange rate experienced a signi-
ficant variation between the pre and post-event windows.
The statistical analysis for the first three criteria (Direction, Reversal and
Smoothing) consists of counting the number of successful events and com-
paring it with the total number of events. Specifically, we use a sign t-test,
based on a binomial distribution, to check if the probability of a successful
event (p) is greater or equal than 0.5 (or a given probability value). As for the
Matched criterion, the analysis consisted of computing the difference between
the corresponding pre and post event exchange rate values. And, by assum-
ing that the variation of the exchange rate of both sub-samples is normally
distributed, we use a t-test with n-1 degrees of freedom (“n” being the num-
ber of matched pairs).
Formally, the four criteria can be expressed as shown in Graph 4 for the case
of purchases (vice versa for sales).
Graph 4. Definitions of Criteria for Successful Interventions
Criteria Pre-event Event Pos-event
Direction USD Purchases
+>St0
Reversal
−<St0
USD Purchases
+>St0
Smoothing
−<St0
USD Purchases
+−
>SS
tt
Matching (magnitude) USD Purchases
+−
>SS
tt
Overall, reversal is a more demanding criterion than direction since it does not
consider the behavior of the exchange rate in the pre-event window. It is also
more demanding than the smoothing criterion since it does not require the
exchange rate to depreciate (appreciate) after USD purchases (USD sales).
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III. Results
Table 2 presents the results when the estimation window, the pre, and the
post-event periods correspond to five days. Column (1) presents the different
types of intervention which include A) discretionary, B) options for reserve
accumulation, C) options for volatility control, D) the combination t2 which
considers interventions that were set by the board of directors (A+B), and E)
the combination t3 which considers all types of interventions (A+B+C). We
recognize that different types of foreign exchange intervention could have
been motivated by different covariates and policy objectives. However, it might
be of interest to know if the combined effect (t2 or t3 ) had an impact on the
exchange rate. That is, regardless of trying to depreciate/appreciate domestic
currency or stem exchange rate volatility (or affect any central moment for
that matter), it is crucial to see if interventions had an effect on the exchange
rate (and specifically, over the smoothing, direction and reversal criteria).
Column (2) distinguishes sales from purchases, and columns (4) - (6) present
the total and the successful number of cases. Columns (7) – (10) consider the
p-value associated with the sign test for different values (probability of success):
0.5, 0.6, 0.7 and 0.8. Highlighted values correspond to p-values less than 0.10.7
The results confirm that all types of intervention are successful, when consider-
ing
Hp
005:.≤
(column 7) meaning that an exchange rate appreciation is less
intense after purchases of foreign currency by the central bank (vice-versa for
sales). However, only the volatility options are successful when considering a
more rigorous null hypothesis
Hp
00607 08:.,. . or ≤
(and t3 in some cases).
Results of Table 3 suggest that t3 and volatility options were successful accord-
ing to the direction criterion
Hp
005:. ≤
()
. The former, with p-values of 0.02
for sales, and 0.11 for purchases. The combined effect of volatility options,
“purchases + sales” is also significant at the 10% level (not reported). The
stronger effect of volatility options also appears in Table 4 for reversals (t3 is
not significant in this last case) and in Table 5 for matching. We also report in
the next section the results of the same tests, controlling for two alternative
scenarios. Scenario (a) considers the evolution of the exchange rate in Brazil;
7 Tables 2 – 5 only considered event, pre and post periods of five days, but results for the other combina-
tions yield similar results. We report in the Appendix the case of 10 days.
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Table 2. Sign Test, Smoothing
Type of Intervention Purchases/
sales window Total
Cases
Favorable
Cases % Success H0: p ≤ 0.5 H0: p ≤ 0.6 H0: p ≤ 0.7 H0: p ≤ 0.8
P-value P-value P-value P-value
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
A. Discretionary Purchases 5 11 8 72.7 0.03 0.12 0.31 0.62
Sales 5 0 - - - - - -
B. Options reserve accumulation Purchases 5 19 12 63.2 0.08 0.31 0.67 0.93
Sales 5 1 1 100.0 . . . .
C. Options volatility Purchases 5 11 10 90.9 0.00 0.00 0.02 0.09
Sales 5 9 9 100.0 0.00 0.00 0.00 0.00
t2=(A)+(B) Purchases 5 30 20 66.7 0.02 0.18 0.59 0.94
Sales 5 1 1 100.0 . . . .
t3=(A)+(B)+(C) Purchases 5 38 28 73.7 0.00 0.03 0.26 0.78
Sales 5 8 8 100.0 0.00 0.00 0.00 0.00
Source: Authors’ calculations. Pre, post, and event-windows correspond to 5 days. The statistical analysis consisted of counting the number of successful events and
comparing it with the total number of events. We use a sign t-test, based on a binomial distribution, to check if the probability of a successful event (p) is greater or equal
than a given probability value.
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and Scenario (b) considers what happened in those cases in which volatility
options should have been applied if the rule were in place.
Table 3. Sign Test, Direction
Type of
Intervention
Purchases/
sales Window Total
Cases
Favorable
Cases % Success H0: p ≤ 0.5
p-value
(1) (2) (3) (4) (5) (6) (7)
A. Discretionary Purchases 511 654.5 0.27
Sales 5 0 0 . .
B. Options reserve
accumulation
Purchases 5 19 11 57.9 0.18
Sales 5 1 1 100.0
C. Options
volatility
Purchases 511 763.6 0.11
Sales 5 9 7 77.8 0.02
t2=(A)+(B) Purchases 5 30 17 56.7 0.18
Sales 5 1 0 - -
t3=(A)+(B)+(C) Purchases 5 38 23 60.5 0.07
Sales 510 880.0 0.01
Source: Authors’ calculations. Pre, post, and event-windows correspond to 5 days. The statistical analysis
consisted of counting the number of successful events and comparing it with the total number of events.
We use a sign t-test, based on a binomial distribution, to check if the probability of a successful event (p)
is greater or equal than a given probability value.
Table 4. Sign Test, Reversal
Type of
Intervention
Purchases/
sales Window Total Cases Favorable
Cases % Success H0: p ≤ 0.5
p-value
(1) (2) (3) (4) (5) (6) (7)
A. Discretionary Purchases 511 545.5 0.50
Sales 5 0 0 . .
B. Options
reserve
accumulation
Purchases 5 19 6 31.6 0.92
Sales 5 1 0 - .
C. Options
volatility
Purchases 511 763.6 0.11
Sales 5 9 7 77.8 0.02
t2=(A)+(B) Purchases 5 30 11 36.7 0.90
Sales 5 1 0 - .
t3=(A)+(B)+(C) Purchases 5 38 17 44.7 0.69
Sales 510 880.0 0.01
Source: Authors’ calculations. Pre, post, and event-windows correspond to 5 days. The statistical analysis
consisted of counting the number of successful events and comparing it with the total number of events.
We use a sign t-test, based on a binomial distribution, to check if the probability of a successful event (p)
is greater or equal than a given probability value.
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Table 5. Matching Test
Type of
Intervention
Purchases/
sales Window Total Cases Average
Difference
P-value H0: D ≤ 0
or H0: D ≥ 0*
(1) (2) (3) (4) (5) (6)
A. Discretionary Purchases 5 11 0.06 0.42
Sales 5 0 . .
B. Options reserve
accumulation
Purchases 5 19 0.05 0.39
Sales 5 1 -0.16 .
C. Options volatility Purchases 5 11 1.08 0.11
Sales 5 9 -0.72 0.02
t2 = (A)+(B) Purchases 5 30 0.05 0.41
Sales 5 1 -0.10 .
t3 = (A)+(B)+(C) Purchases 5 38 0.30 0.32
Sales 5 10 -0.67 0.04
* D ≤ 0 for purchases and D ≥ 0 for sales.
Source: Authors’ calculations. Pre, post, and event-windows correspond to 5 days. The statistical analysis
consisted of computing the difference between the corresponding pre and post event exchange rate values.
We assume that the variation of the exchange rate of both sub-samples is normally distributed, so we use
a t-test with n-1 degrees of freedom (“n” being the number of matched pairs) to draw inference.
A. Counterfactuals
The Colombian exchange rate could have increased after an intervention epi-
sode for a variety of reasons, including the effects of other countries like Bra-
zil (See Section I). For this reason, Tables 6a-6c present the same exercise of
Tables 2-5 but for the case of Brazil. In other words, we consider the evolu-
tion of the exchange rate in Brazil in periods corresponding to pre and post
Colombian volatility interventions. This provides a counterfactual experiment
that allows us to test for possible bias that might have been introduced by
predetermined variables.
Results for volatility options are presented in Table 6a which suggests that
interventions under this counterfactual were not successful according to the
direction, reversal and the matched criteria. However, the case of Brazil casts
some doubts when considering the impact of intervention on the smoothing
criterion (it is also significant in Brazil with the associated null hypothesis of
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Ho: p ≤ 0.5).8 This result no longer holds when considering the null hypoth-
esis of Ho: p ≤ 0.8, which is significant for the Colombian case (see Table 2)
but not significant for the case of Brazil.
Results for discretionary interventions and reserve accumulation are presented
in Tables 6b and 6c, respectively. While discretionary interventions in Brazil
exhibited no significant effects, options for reserve accumulation were sig-
nificant under the smoothing and direction criteria for the null hypothesis of
Ho: p ≤ 0.5. In sum, out of the 12 counterfactual exercises presented in Tables
6a-6c, only 3 were significant, two of which correspond to the weakest cri-
teria (smoothing with a null of Ho: p ≤ 0.5).
Table 6a. Control I: Sign Tests for Volatility Options Using Brazil As Counterfactual
Criterion Window
Total Cases
(Purchases
+ Sales)
Favorable
Cases % Success
Ho: p ≤
0.5, Ho: p ≤ 0.8, Ho: D
≤ 0,
p-value p-value p-value
Smoothing 5 20 17 85 0.00 0.21 -
Direction 5 20 12 60 0.13 - -
Reversal 5 20 9 45 0.58 - -
Matched 5 20 - - - - 0.26
Source: Authors’ calculations. Refer to Tables 2-5 for the statistical analysis of each criterion.
Table 6b. Con trol I : Sign Tests for Discreti onary Intervention s Using Brazi l As
Counterfactual
Criterion Window
Total Cases
(Purchases
+ Sales)
Favorable
Cases % Success Ho: p ≤ 0.5, Ho: p ≤
0.8,
Ho: D
≤ 0,
p-value p-value p-value
Smoothing 511 7 64 0.11 0.84 -
Direction 511 3 27 0.89 - -
Reversal 511 3 27 0.89 - -
Matched 511 - - - - 0.44
Source: Authors’ calculations. Refer to Tables 2-5 for the statistical analysis of each criterion.
8 We are assuming that Brazil was not intervening in those same periods, or that the pattern of interven-
tion during the whole period was different.
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Table 6c. Control I: Sign Tests for Reserve Accumulation Options Using Brazil As
Counterfactual
Criterion Window
Total Cases
(Purchases +
Sales)
Favorable
Cases % Success
Ho: p ≤
0.5,
Ho: p ≤
0.8,
Ho: D
≤ 0,
p-value p-value p-value
Smoothing 5 20 14 70 0.02 0.80 -
Direction 5 20 14 70 0.02 - -
Reversal 5 20 10 50 0.41 - -
Matched 5 20 - - - - 0.33
Source: Authors’ calculations. Refer to Tables 2-5 for the statistical analysis of each criterion.
The second counterfactual considered was related to the behavior of the Colom-
bian exchange rate in periods in which rule-based volatility options should
have been triggered if the rule were in place, but was not, simply because
the board of the Central Bank decided to suspend interventions. In principle,
monetary authorities may have chosen to suspend this particular interven-
tion mechanism for reasons related to exchange rate movements, leading to
an endogenous relationship. However, we consider a period of over 3 years
(2010-2012) in which the rule was no longer in place. And, while exchange
rate movements in 2009 might have influenced the decision to permanently
terminate this type of interventions, it is very unlikely that this relationship
persisted for more than a few months (certainly not for years after the deci-
sion was taken). Given the small number of events available, we only consid-
ered the case of a 2-day event window, pre and post event.
Table 7 shows, once again, that our results are not biased by pre-existing dif-
ferences. Tables 6 and 7 thus suggest that the counterfactual experiments for
volatility options are robust for direction, reversal and for the matched test,
but not for smoothing.
Table 7. Control II: Sign Test for Volatility Options in Brazil Using Different Conditions
As Counterfactual
Criterion Window Total Cases
(Purchases+ Sales)
Favorable
Cases %Success H0: p ≤ 0.5,
p-value
H0: D ≤ 0,
p-value
Smoothing 2 10 770 0.05 -
Direction 2 10 770 0.58 -
Reversal 2 10 6 60 0.17 -
Matched 2 10 - - - 0.40
Source: Authors’ calculations. Refer to Tables 2-5 for the statistical analysis of each criterion.
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B. Robustness Checks
Graphs 5-8 show additional robustness checks for our proposed criteria:
Smoothing (Figure 5), Direction (Figure 6), Reversal (Figure 7) and Match-
ing (Figure 8). For each type of intervention, we computed the percentage
of successes and p-values of the evaluation test9 for different window sizes.
Two main results can be seen: 1) volatility options are successful according to
the four criteria and for all window sizes considered, and 2) all intervention
mechanisms are successful when considering only the smoothing criteria and
for window sizes that are less than 12 days.
Graph 5. Robust Exercise-Smoothing Criteria
5
100
80
60
40
20
0
10 15 20 25
1.0
0.8
0.6
0.4
0.2
0.0
Pre and post event window (days)
Discretionary Disc. p-val (rhs)
Reserve Opc. Res. p-val (rhs)
Volatility Opc. Vol. p-val (rhs)
% of successes
Source: Authors’ calculations. Refer to Tables 2-5 for the statistical analysis of each criterion.
9 For the match test only the p-value is presented.
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Graph 6. Robust Exercise Direction Criteria
5
100
80
60
40
20
0
10 15 20 25
1.0
0.8
0.6
0.4
0.2
0.0
Pre and post event window (days)
Discretionary Disc. p-val (rhs)
Reserve Opc. Res. p-val (rhs)
Volatility Opc. Vol. p-val (rhs)
% of successes
Source: Authors’ calculations. Refer to Tables 2-5 for the statistical analysis of each criterion.
Graph 7. Robust Exercise – Reversal Criteria
5
100
80
60
40
20
0
10 15 20 25
1.0
0.8
0.6
0.4
0.2
0.0
Pre and post event window (days)
Discretionary Disc. p-val (rhs)
Reserve Opc. Res. p-val (rhs)
Volatility Opc. Vol. p-val (rhs)
% of successes
Source: Authors’ calculations. Refer to Tables 2-5 for the statistical analysis of each criterion.
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Graph 8. Robust Exercise – Matched Test
5
1.0
0
10 15 20 25
Pre and post event window (days)
P-value
0.8
0.6
0.4
0.2
Volatility Opc. P-value
Reserve Opc. - P-value
Discretionary P-value-
Source: Authors’ calculations. Refer to Tables 2-5 for the statistical analysis of each criterion.
IV. Conclusions
We compare the effects of international reserve accumulation options, vola-
tility options and discretionary interventions for the Colombian case during
2000-2012, using an event study approach. Following Fatum and Hutchison
(2001), we define four different criteria to evaluate a successful intervention:
1) Direction, 2) Reversal, 3) Smoothing, and 4) Matching.
We also conduct two counterfactual exercises: 1) we consider the evolution of
the Brazilian exchange rate in periods corresponding to pre and post Colombian
volatility interventions and 2) we consider periods in which volatility options
should have been conducted if the intervention rule was in place, but was not,
because the board of the Central Bank decided to suspend interventions. Finally,
we conduct robustness checks by allowing for various event window sizes.
Results show that all types of interventions were successful according to
the smoothing criterion when considering the null hypothesis of
Hp
005:.≤
.
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Also, volatility options were successful when considering a more rigorous null
hypothesis of
Hp
00607 08:.,. . or ≤
. Moreover, volatility options were also
successful according to the direction, reversal and matching criteria.
We also find that volatility options, using Brazil as a counterfactual exercise,
were not successful according to the direction, reversal and matched criteria.
The case of Brazil casts some doubts when considering the smoothing criterion
(it is also significant in Brazil under the null hypothesis of
Hp
005:.≤
). How-
ever, this result no longer holds when considering the null hypothesis of H0:
p ≤ 0.5, which is significant for Colombia but not significant for Brazil. Also,
while discretionary interventions in Brazil exhibited no significant effects,
options for reserve accumulation were significant under the smoothing and
direction criteria under the null of
Hp
005:.≤
.
As a result, our findings indicate that: 1) volatility options were successful
according to the four criteria and for all window sizes considered, and 2) all
intervention mechanisms were successful under the smoothing criteria and
for window sizes of less than 12 days. Finally, most of our counterfactual
exercises suggest that our results are not biased by pre-existing differences.
However, this is not the case for the options for reserve accumulation. Suc-
cess for this mechanism should be further analyzed and results should be cau-
tiously interpreted.
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Appendix
Table A.1. Sign Test, Smoothing (10 days)
Type of
Intervention
Purchases/
sales Window Total
Cases
Favorable
Cases % Success H0: p ≤ 0.5
p-value
(1) (2) (3) (4) (5) (6) (7)
A. Discretionary Purchases 10 6 4 67 0.11
Sales 10 0 0 . .
B. Options int.
reserves
Purchases 10 14 10 71 0.03
Sales 10 1 1 100 .
C. Options
volatility
Purchases 10 10 10 100 0.00
Sales 10 5 5 100 0.00
t2=(A)+(B) Purchases 10 18 12 67 0.05
Sales 10 1 1 100 .
t3=(A)+(B)+(C) Purchases 10 24 19 79 0.00
Sales 10 6 6 100 0.00
Source: Authors’ calculations. Refer to Tables 2-5 for the statistical analysis of each criterion.
Table A.2. Sign Test, Direction (10 days)
Type of
Intervention
Purchases/
sales Window Total
Cases
Favorable
Cases % Success H0: p ≤ 0.5
p-value
(1) (2) (3) (4) (5) (6) (7)
A. Discretionary Purchases 10 6 3 50 0.34
Sales 10 0 0 . .
B. Options int.
reserves
Purchases 10 14 9 64 0.09
Sales 10 1 1 100 .
C. Options volatility Purchases 10 10 8 80 0.01
Sales 10 5 4 80 0.03
t2 = (A)+(B) Purchases 10 18 11 61 0.12
Sales 10 1 1 100 .
t3 = (A)+(B)+(C) Purchases 10 24 18 75 0.00
Sales 10 6 5 83 0.02
Source: Authors’ calculations. Refer to Tables 2-5 for the statistical analysis of each criterion.
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Table A.3. Sign Test, Reversal (10 days)
Type of
Intervention
Purchases/
sales Window Total
Cases
Favorable
Cases % Success
H0: p ≤ 0.5
p-value
(1) (2) (3) (4) (5) (6) (7)
A. Discretionary Purchases 10 6 2 33 0.66
Sales 10 0 0 . .
B. Options int.
reserves
Purchases 10 14 7 50 0.40
Sales 10 1 0 0 .
C. Options volatility Purchases 10 10 8 80 0.01
Sales 10 5 4 80 0.03
t2 = (A)+(B) Purchases 10 18 9 50 0.41
Sales 10 1 0 0 .
t3 = (A)+(B)+(C) Purchases 10 24 16 67 0.03
Sales 10 6 4 67 0.11
Source: Authors’ calculations. Refer to Tables 2-5 for the statistical analysis of each criterion.
Table A.4. Matching Test (10 days)
Type of
Intervention
Purchases/
sales Window Total Cases Average
Difference
P-value H0:
D
≤ 0 or
H0: D ≥ 0*
(1) (2) (3) (4) (5) (6)
A. Discretionary Purchases 10 6 -0.05 0.58
Sales 10 0 . .
B. Options int.
reserves
Purchases 10 14 0.06 0.30
Sales 10 1 -0.01 .
C. Options
volatility
Purchases 10 10 0.62 0.07
Sales 10 5 -0.63 0.05
t2 = (A)+(B) Purchases 10 18 0.04 0.41
Sales 10 1 -0.01 .
t3 = (A)+(B)+(C) Purchases 10 24 0.28 0.24
Sales 10 6 -0.53 0.11
* D ≤ 0 for purchases and D ≥ 0 for sales.
Source: Authors’ calculations. Refer to Tables 2-5 for the statistical analysis of each criterion.
