Description
The purpose of this paper is to examine empirical characteristics of two commonly
mentioned expressions of international financial crisis, “sudden stops” and currency crises.
Journal of Financial Economic Policy
Sudden stops and currency crises
Levan Efremidze Samuel M. Schreyer Ozan Sula
Article information:
To cite this document:
Levan Efremidze Samuel M. Schreyer Ozan Sula, (2011),"Sudden stops and currency crises", J ournal of
Financial Economic Policy, Vol. 3 Iss 4 pp. 304 - 321
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D
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Sudden stops and currency crises
Levan Efremidze
Claremont Institute for Economic Policy Studies and Center for Neuroeconomics
Studies, Claremont Graduate University, Claremont, California, USA
Samuel M. Schreyer
Department of Economics, Finance, and Accounting,
Fort Hays State University, Hays, Kansas, USA, and
Ozan Sula
Department of Economics, Western Washington University,
Bellingham, Washington, USA and
Claremont Institute for Economic Policy Studies,
Claremont Graduate University, Claremont, California, USA
Abstract
Purpose – The purpose of this paper is to examine empirical characteristics of two commonly
mentioned expressions of international ?nancial crisis, “sudden stops” and currency crises.
Design/methodology/approach – Sudden stop and currency crisis events are identi?ed and
empirical regularities among them are analyzed based on the annual data of 25 emerging market
countries from 1990 to 2003.
Findings – Puzzlingly, these two seemingly close expressions of crises overlap less than 50 percent
of the time and sudden stops more frequently precede than follow currency crises. Also the two
different sudden stop measures are not strongly correlated with each other.
Research limitations/implications – This shows that it can make a great deal of difference what
measure is used and suggests that studies in this area should be sure to check the robustness of their
results to different measures.
Practical implications – The authors think that the proper analysis should focus on how to use
these different measures to understand the nature of the crises. Thus, sudden stop and currency crisis
measures should be used as complements, rather than substitutes.
Social implications – The alarming frequency of the emerging market crises during the last three
decades has motivated a large volume of theoretical and empirical literature on the subject. The
paper’s results advance understanding of these events.
Originality/value – A large body of studies on currency crises coexists with a growing
literature on sudden stops yet a majority of the studies that investigate either one of these
phenomena do not mention the other. The paper adds value by investigating empirical relationships
between them.
Keywords International economics, International ?nance, International investments,
Capital movements, International factor movements, International business, Current account adjustment,
Open economy, Macroeconomics, International trade, Financial markets
Paper type Research paper
1. Introduction
The alarming frequency of the emerging market crises during the last three decades
has motivated a large volume of theoretical and empirical literature on the subject.
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1757-6385.htm
JEL classi?cation – F02, F0, F, F2, F2, F3, F32, F41, F4, G15, G1, G
JFEP
3,4
304
Journal of Financial Economic Policy
Vol. 3 No. 4, 2011
pp. 304-321
qEmerald Group Publishing Limited
1757-6385
DOI 10.1108/17576381111182891
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The signi?cance of these events is obvious from the sizable output losses the affected
countries suffered. Argentina’s losses in the early 1980s crisis amounted to 55 percent of
its GDP. During the 1997 Asian crises, Thailand, Indonesia, Malaysia and South Korea
experienced on average an 11 percent drop in their per capita real GDP (RGDP).
Increased volatility of international capital ?ows and sharp exchange rate movements
have been a de?ning feature of these crises.
A survey of the vast literature on these events leads to a conceptual puzzle. Most of
the widely publicized emerging market crises are classi?ed as both sudden stops (SSs)
and currency crises (CC). A large body of studies on CC coexists with a growing
literature on SSs yet a majority of the studies that investigate either one of these
phenomena do not mention the other. Implicitly these two types of events are either
assumed to be the same or completely unrelated. Among the small number of studies
that do discuss both, Hutchison and Noy (2006) de?ne an SS as the joint occurrence of
CC and current account reversal (CAR), and Calvo et al. (2004) treats them as separate
yet related events. Calvo et al. (2004) point out the timing difference between SSs and CC
and prefer using SSs to study crises, which are seen as originating from “credit shocks
in international markets.” The study also argues that a measure that is based on the
?nancial account (FA) would identify more crises episodes than CA de?cit-based
measures, because some countries have low volatility in CAs.
In addition to the ambiguities of the crisis concepts, there is the measurement issue. The
term“sudden stop” was ?rst introduced by Dornbusch et al. (1995). It refers to sudden and
large drops incapital in?ows andthe termcomes fromthe banker’s adage: “it isn’t the speed
that kills you, it’s the sudden stop.” While the essence of this description of SSs is broadly
representative of that taken in the literature, there has been less consensus on howto de?ne
these events empirically. This is not entirely surprising. A description of SSs – as with
many macroeconomic phenomena – does not lend itself to a single, precise mathematical
criterion. Similar debates on the appropriate measure to identify CC also exist.
In this study, our goal is to further examine these issues. First, we present a short
survey of the measures of SSs used in the recent literature. There are two major
alternative empirical approaches. In Section 3, we examine the empirical characteristics
of these measures of SSs and their relationships to measures of CC using annual data for
25 emerging market countries from 1990 to 2003. We ?nd that not only do the CC
and SSs often fail to overlap but also the two different SS measures are not strongly
correlated with each other. This shows that it can make a great deal of difference what
measure is used and suggests that studies in this area should be sure to check the
robustness of their results to different measures. In Section 4, we discuss several other
complications that arise in the crisis literature. We conclude that CC and SSs are neither
the same nor unrelated.
2. Measures of SSs
To capture a broad spectrum of conceptual and empirical de?nitions of SSs,
we conducted a keyword search using “sudden stops” in the EconLit database. The
search yielded 30 published and working papers since 2004 which are shown in Table I,
along with a brief de?nition and description of the main crisis or SS measures used in
each paper. A brief examination of this table reveals that myriad criteria have been
used in the recent literature to identify SSs. Nonetheless, there are several facets that
many of these de?nitions have in common.
Sudden stops
and currency
crises
305
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Table I.
SS de?nitions
in the literature
JFEP
3,4
306
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e
t
a
l
.
(
2
0
0
8
)
C
a
l
v
o
e
t
a
l
.
(
2
0
0
6
)
–
1
9
9
0
-
2
0
0
4
,
m
o
n
t
h
l
y
;
2
1
d
e
v
e
l
o
p
e
d
a
n
d
8
9
d
e
v
e
l
o
p
i
n
g
e
c
o
n
o
m
i
e
s
C
a
l
v
o
e
t
a
l
.
(
2
0
0
6
)
C
a
l
v
o
e
t
a
l
.
(
2
0
0
6
)
–
1
9
9
0
-
2
0
0
1
,
m
o
n
t
h
l
y
;
1
5
e
m
e
r
g
i
n
g
m
a
r
k
e
t
s
a
n
d
1
7
d
e
v
e
l
o
p
e
d
e
c
o
n
o
m
i
e
s
C
a
t
a
o
(
2
0
0
7
)
(
1
)
D
F
A
#
2
2
s
;
a
n
d
/
o
r
(
2
)
D
F
A
/
G
D
P
#
2
3
%
D
F
A
r
e
p
r
e
s
e
n
t
s
d
e
v
i
a
t
i
o
n
s
f
r
o
m
a
l
i
n
e
a
r
t
r
e
n
d
,
r
a
t
h
e
r
t
h
a
n
y
e
a
r
-
o
n
-
y
e
a
r
c
h
a
n
g
e
s
.
A
n
S
S
o
c
c
u
r
s
w
h
e
n
(
1
)
D
F
A
i
s
a
t
l
e
a
s
t
t
w
o
S
D
s
b
e
l
o
w
z
e
r
o
,
a
n
d
/
o
r
(
2
)
D
F
A
i
s
a
t
l
e
a
s
t
3
p
e
r
c
e
n
t
o
f
G
D
P
.
A
n
S
S
b
e
g
i
n
s
w
h
e
n
F
A
a
t
t
a
i
n
s
i
t
s
p
e
a
k
a
n
d
e
n
d
s
w
h
e
n
F
A
s
t
a
r
t
s
r
i
s
i
n
g
r
e
l
a
t
i
v
e
t
o
t
r
e
n
d
w
i
t
h
o
u
t
f
a
l
l
i
n
g
b
a
c
k
t
o
i
t
s
l
o
w
e
s
t
l
e
v
e
l
w
i
t
h
i
n
a
f
o
u
r
-
y
e
a
r
p
e
r
i
o
d
1
8
7
0
-
1
9
1
3
,
a
n
n
u
a
l
;
1
6
c
o
u
n
t
r
i
e
s
C
a
v
a
l
l
o
(
2
0
0
5
)
C
a
v
a
l
l
o
a
n
d
F
r
a
n
k
e
l
(
2
0
0
8
)
–
–
C
o
w
a
n
e
t
a
l
.
(
2
0
0
8
)
(
1
)
D
F
A
#
m
2
s
;
(
2
)
D
F
A
/
G
D
P
#
2
5
%
T
h
e
n
e
t
c
a
p
i
t
a
l
?
o
w
s
e
r
i
e
s
i
s
s
c
a
l
e
d
b
y
a
l
i
n
e
a
r
t
r
e
n
d
o
f
G
D
P
t
o
“
d
i
s
e
n
t
a
n
g
l
e
”
?
u
c
t
u
a
t
i
o
n
s
i
n
c
a
p
i
t
a
l
?
o
w
s
f
r
o
m
?
u
c
t
u
a
t
i
o
n
s
i
n
R
G
D
P
a
n
d
t
h
e
r
e
a
l
e
x
c
h
a
n
g
e
r
a
t
e
.
A
f
t
e
r
d
o
i
n
g
t
h
i
s
,
a
n
S
S
o
c
c
u
r
s
w
h
e
n
(
1
)
t
h
e
s
c
a
l
e
d
D
F
A
i
s
a
t
l
e
a
s
t
o
n
e
S
D
b
e
l
o
w
i
t
s
a
v
e
r
a
g
e
,
a
n
d
(
2
)
t
h
e
s
c
a
l
e
d
D
F
A
i
s
a
t
l
e
a
s
t
5
p
e
r
c
e
n
t
o
f
G
D
P
1
9
7
5
-
2
0
0
4
,
a
n
n
u
a
l
;
3
2
e
m
e
r
g
i
n
g
m
a
r
k
e
t
s
a
n
d
2
1
d
e
v
e
l
o
p
e
d
e
c
o
n
o
m
i
e
s
D
e
b
(
2
0
0
5
)
(
1
)
D
F
A
/
G
D
P
#
2
5
%
(
2
)
C
C
A
n
S
S
o
c
c
u
r
s
w
h
e
n
a
d
r
o
p
i
n
F
A
i
s
a
t
l
e
a
s
t
5
p
e
r
c
e
n
t
o
f
G
D
P
a
n
d
a
C
C
o
c
c
u
r
s
a
t
t
i
m
e
t
o
r
t
þ
1
1
9
7
5
-
1
9
9
9
,
a
n
n
u
a
l
;
a
l
l
p
o
s
s
i
b
l
e
c
o
u
n
t
r
i
e
s
D
u
r
d
u
e
t
a
l
.
(
2
0
0
9
)
–
T
h
e
a
u
t
h
o
r
s
u
s
e
S
S
s
i
d
e
n
t
i
?
e
d
“
i
n
v
a
r
i
o
u
s
e
m
p
i
r
i
c
a
l
s
t
u
d
i
e
s
,
i
n
c
l
u
d
i
n
g
C
a
l
v
o
e
t
a
l
.
(
2
0
0
4
)
,
C
a
v
a
l
l
o
a
n
d
F
r
a
n
k
e
l
(
2
0
0
8
)
a
n
d
R
o
t
h
e
n
b
e
r
g
a
n
d
W
a
r
n
o
c
k
(
2
0
0
6
)
”
1
9
8
5
-
2
0
0
4
,
a
n
n
u
a
l
;
1
7
e
m
e
r
g
i
n
g
m
a
r
k
e
t
s
E
d
w
a
r
d
s
(
2
0
0
4
)
(
1
)
F
A
.
x
(
2
)
D
F
A
/
G
D
P
#
2
5
%
A
n
S
S
o
c
c
u
r
s
w
h
e
n
(
1
)
a
c
o
u
n
t
r
y
r
e
c
e
i
v
e
s
a
n
i
n
?
o
w
o
f
c
a
p
i
t
a
l
l
a
r
g
e
r
t
h
a
n
t
h
e
t
h
i
r
d
q
u
a
r
t
i
l
e
o
f
i
n
?
o
w
s
f
o
r
t
h
e
r
e
g
i
o
n
(
x
)
d
u
r
i
n
g
t
h
e
p
r
e
v
i
o
u
s
t
w
o
y
e
a
r
s
o
f
t
h
e
c
r
i
s
i
s
,
a
n
d
(
2
)
n
e
t
c
a
p
i
t
a
l
i
n
?
o
w
s
d
e
c
l
i
n
e
b
y
a
t
l
e
a
s
t
5
p
e
r
c
e
n
t
o
f
G
D
P
1
9
7
0
-
2
0
0
1
,
a
n
n
u
a
l
;
a
l
l
p
o
s
s
i
b
l
e
c
o
u
n
t
r
i
e
s
E
d
w
a
r
d
s
(
2
0
0
5
)
E
d
w
a
r
d
s
(
2
0
0
4
)
–
–
E
d
w
a
r
d
s
(
2
0
0
6
)
E
d
w
a
r
d
s
(
2
0
0
4
)
–
–
(
c
o
n
t
i
n
u
e
d
)
Table I.
Sudden stops
and currency
crises
307
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
3
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
A
u
t
h
o
r
(
s
)
B
r
i
e
f
d
e
?
n
i
t
i
o
n
D
e
s
c
r
i
p
t
i
o
n
S
a
m
p
l
e
E
d
w
a
r
d
s
(
2
0
0
7
)
D
F
A
/
G
D
P
#
2
3
%
E
d
w
a
r
d
s
c
a
l
l
s
t
h
i
s
a
“
c
a
p
i
t
a
l
?
o
w
c
o
n
t
r
a
c
t
i
o
n
”
(
C
F
C
)
a
n
d
d
i
s
t
i
n
g
u
i
s
h
e
s
t
h
i
s
f
r
o
m
a
n
S
S
s
i
n
c
e
,
a
c
c
o
r
d
i
n
g
t
o
E
d
w
a
r
d
s
,
t
h
e
l
a
t
t
e
r
m
u
s
t
b
e
p
r
e
c
e
d
e
d
b
y
n
e
t
c
a
p
i
t
a
l
i
n
?
o
w
s
1
9
7
0
-
2
0
0
4
,
a
n
n
u
a
l
;
a
l
l
p
o
s
s
i
b
l
e
c
o
u
n
t
r
i
e
s
C
a
v
a
l
l
o
a
n
d
F
r
a
n
k
e
l
(
2
0
0
8
)
(
1
)
D
F
A
#
c
2
2
s
;
(
2
)
D
C
A
$
0
;
(
3
)
D
(
P
C
G
D
P
)
,
0
A
n
S
S
o
c
c
u
r
s
w
h
e
n
(
1
)
t
h
e
F
A
f
a
l
l
s
a
t
l
e
a
s
t
t
w
o
S
D
s
b
e
l
o
w
t
h
e
m
e
a
n
S
D
(
c
)
a
t
t
i
m
e
t
a
n
d
i
s
p
r
e
c
e
d
e
d
b
y
a
F
A
s
u
r
p
l
u
s
,
(
2
)
t
h
e
C
A
i
n
c
r
e
a
s
e
s
a
t
t
i
m
e
t
o
r
t
þ
1
a
n
d
i
s
p
r
e
c
e
d
e
d
b
y
a
C
A
d
e
?
c
i
t
,
a
n
d
(
3
)
p
e
r
c
a
p
i
t
a
G
D
P
(
P
C
G
D
P
)
f
a
l
l
s
a
t
t
i
m
e
t
o
r
t
þ
1
.
T
h
e
m
e
a
n
S
D
(
c
)
i
s
c
a
l
c
u
l
a
t
e
d
b
y
t
a
k
i
n
g
t
h
e
a
v
e
r
a
g
e
o
f
t
h
e
S
D
s
d
u
r
i
n
g
t
h
e
1
9
7
0
s
,
1
9
8
0
s
,
a
n
d
1
9
9
0
s
þ
1
9
7
0
-
2
0
0
2
,
a
n
n
u
a
l
;
a
l
l
p
o
s
s
i
b
l
e
c
o
u
n
t
r
i
e
s
G
a
l
l
e
g
o
a
n
d
J
o
n
e
s
(
2
0
0
5
)
(
1
)
D
F
A
#
m
2
2
s
A
n
S
S
i
s
d
e
?
n
e
d
b
y
a
“
c
a
p
i
t
a
l
?
o
w
w
i
n
d
o
w
”
(
C
a
l
v
o
e
t
a
l
.
,
2
0
0
6
)
w
h
e
n
D
F
A
i
s
a
t
l
e
a
s
t
t
w
o
S
D
s
b
e
l
o
w
i
t
s
m
e
a
n
1
9
9
0
-
2
0
0
3
,
m
o
n
t
h
l
y
;
1
4
e
m
e
r
g
i
n
g
m
a
r
k
e
t
s
G
u
i
d
o
t
t
i
e
t
a
l
.
(
2
0
0
4
)
(
1
)
D
F
A
#
m
2
s
;
(
2
)
D
F
A
/
G
D
P
#
2
5
%
S
S
s
a
r
e
i
d
e
n
t
i
?
e
d
b
y
(
1
)
a
r
e
d
u
c
t
i
o
n
i
n
n
e
t
c
a
p
i
t
a
l
?
o
w
s
b
y
a
t
l
e
a
s
t
o
n
e
S
D
b
e
l
o
w
i
t
s
m
e
a
n
,
a
n
d
(
2
)
t
h
e
r
e
d
u
c
t
i
o
n
i
n
n
e
t
c
a
p
i
t
a
l
?
o
w
s
a
r
e
a
t
l
e
a
s
t
5
p
e
r
c
e
n
t
o
f
G
D
P
1
9
7
4
-
2
0
0
2
,
a
n
n
u
a
l
;
a
l
l
p
o
s
s
i
b
l
e
c
o
u
n
t
r
i
e
s
H
o
n
i
g
(
2
0
0
8
)
(
1
)
D
F
A
#
2
2
s
;
(
2
)
D
C
A
$
0
;
(
3
)
D
(
P
C
G
D
P
)
,
0
A
n
S
S
o
c
c
u
r
s
w
h
e
n
(
1
)
t
h
e
F
A
f
a
l
l
s
a
t
l
e
a
s
t
t
w
o
S
D
s
b
e
l
o
w
z
e
r
o
a
t
t
i
m
e
t
a
n
d
i
s
p
r
e
c
e
d
e
d
b
y
a
F
A
s
u
r
p
l
u
s
,
(
2
)
t
h
e
C
A
i
n
c
r
e
a
s
e
s
a
t
t
i
m
e
t
o
r
t
þ
1
a
n
d
i
s
p
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2
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2
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4
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h
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d
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o
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(
2
0
0
6
)
(
1
)
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C
A
/
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D
P
$
3
%
;
(
2
)
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1
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l
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u
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t
a
l
.
(
2
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1
0
)
(
1
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F
A
#
2
2
s
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(
2
)
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$
0
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6
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3
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3
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m
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r
k
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t
s
(
c
o
n
t
i
n
u
e
d
)
Table I.
JFEP
3,4
308
D
o
w
n
l
o
a
d
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d
b
y
P
O
N
D
I
C
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R
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t
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1
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u
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r
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0
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T
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b
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r
(
2
0
0
9
)
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4
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4
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a
l
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l
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k
y
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2
0
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5
)
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D
P
$
3
%
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5
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G
D
P
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2
.
5
%
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C
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t
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m
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m
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r
k
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u
l
a
(
2
0
1
0
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1
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F
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/
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#
4
%
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1
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9
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2
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3
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l
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1
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m
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.
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l
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s
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n
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n
-
F
D
I
c
a
p
i
t
a
l
?
o
w
s
)
Table I.
Sudden stops
and currency
crises
309
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
3
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
First, the overwhelming majority of papers consider negative changes in net capital
?ows as the main variable of interest and do so using data on a country’s FA from its
balance of payments (BOP) statement. A country’s net FA represents the sum of
purchases/sales of domestic assets by foreigners and purchases/sales of foreign assets
by domestic residents. Negative changes in FA imply that the aggregated ?nancial
?ows are moving away from the country at a faster rate than in the previous period, or,
alternatively, these ?ows are coming into the country at a slower rate than the previous
period. It is important to emphasize that crisis de?nitions considering only negative
changes to FA allow for the possibility of a sudden slowdown of capital in?ows,
despite the conjured image of capital ?ows ceasing to ?ow inward as suggested by the
moniker SSs. The additional constraint that FA be negative when measured in levels
rather than ?rst differences ensures that only episodes of capital out?ows will be
considered (Edwards, 2004; Sula, 2010).
Another commonality shared between many of the SS de?nitions surveyed in Table I
is that the change in a country’s FA (DFA) be negative and less than a particular
threshold involving the mean and/or standard deviation of the DFA series (Calvo et al.,
2004; Bordo et al., 2010; Rothenberg and Warnock, 2007). Speci?cally, the following
type of criterion is used:
DFA
t
# m
DFA
2bs
DFA
ð1Þ
which indicates an SS occurs in a country when the change in its capital ?ows at time
t is negative and at least b standard deviations different from its mean, with the choice
of b tending to take a value between 1 and 2 (Guidotti et al., 2004; Gallego and Jones,
2005). Yet many variations of equation (1) exist. For example, Catao (2007) simply
omits m
DFA
; Rothenberg and Warnock (2007) measure m
DFA
and s
DFA
on a rolling
basis such that all data up to time t is used to compute these statistics; and Cavallo and
Frankel (2008) replace m
DFA
with the mean of the standard deviation of DFA for each
decade of their nearly three-decade long sample.
Several SS de?nitions in Table I follow a different approach. Their de?ning feature is
that a negative DFA must be suf?ciently large as a percent of GDP. Typically this
thresholdranges from3 to5 percent of GDP(Bordoet al., 2010; Catao, 2007). Inthis manner
the reduced capital in?ows or increased capital out?ows during an SS are required to be
economically large which contrasts with equation (1) since the latter requires only that
DFA be large relative to its own history. Indeed, solely using this criterion to indicate
SSs has been favored by some authors, such as Becker and Mauro (2006).
The standard measures of SSs should really be labeled as capital ?ow reversals.
The intuitive concept of SSs involves large capital in?ows that suddenly stop while the
standard measures would include a large increase in capital ?ight in their de?nitions.
Thus, Edwards (2007) interprets a 3 percent drop in FArelative to GDP as a capital ?ow
contraction and distinguishes this from an SS since the latter, according to the author,
must be preceded by capital in?ows. This issue is discussed further in Section 4.
On a related note, some authors require a decline in GDP, as a whole or on a per capita
basis, in order for an SS crisis to occur (Cavallo and Frankel, 2008; Calvo et al., 2004).
The purpose of this restriction is to rule out terms of trade improvement related FA
adjustments which could also look like an SS. This criterion necessarily limits analysis
to a subset of costly SSs, rather than considering the broader scenario of a marked
reduction of capital in?ows (Honig, 2008).
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In order to conduct our analysis, we selected one measure from each of the two
approaches that we have surveyed above. For standard deviation-based SS measures we
adopt the measure used in Calvo et al. (2004) as this is one of the ?rst studies to present a
statistical procedure to identify SSs and has been highly in?uential in the literature.
Their measure aims to capture the “unexpected” and “large” changes in FA, which at the
same time has a large negative effect on a country’s output. The authors use the large fall
in output as an additional criteria to identify those SSs that have negative economic
consequences.
Following this study, we derived annual SS dummies from monthly data. Monthly
capital ?ow series are constructed by netting out monthly exports and imports from
changes in monthly reserves. Then, the SS is de?ned as a phase where year-on-year
change in capital ?ows is at least two standard deviations below its sample mean.
The sample is de?ned as an expanding window with a minimum of 24 months of
previous observations. Once the SS phase is detected, it is converted into a dummy
variable with annual frequency. We impose the additional restriction of negative GDP
growth to identify an SS crisis[1]. We adopt the abbreviation SS1 for this measure in
the remainder of this article.
For SS measures that use thresholds based on GDP, we selected the measure used in
Edwards (2004), as it captures important features of this approach and it is widely cited
in the recent literature. The measure is based on the annual FA balance. An SS is
de?ned as a fall in net capital ?ows that is at least 5 percent of the current year’s GDP.
Also the country should have had positive net capital ?ows in the previous year.
We use the abbreviation SS2 for this measure for the remainder of this article.
To identifyCC, we use the commonlyadopted exchange market pressure (EMP) index.
CC dummies are constructed from changes in an index of EMP, de?ned as a weighted
average of monthly real exchange rate changes, monthly reserve losses and interest rate
changes. There is disagreement in the literature over whether is better to use equal or
precision weights (Willett et al., 2005). This is discussed in Section 4. Precision weights
are inverselyrelatedto the variance of changes of eachcomponent over the sample of each
country. We use the latter measure in our comparison. Annual crises dummies take
the value of 1 if the change in the pressure index exceeds the mean plus X times
the country-speci?c standard deviation where X usually ranges between 1.5 and 3.0,
we use 2.0. We adopt the abbreviationCCfor this measure for the remainder of this article.
3. Examination of empirical regularities
In our analysis, we use annual and monthly data for 25 emerging market countries
for the period of 1990-2003[2]. To illustrate the relationship between SSs and CC,
we present the behaviour of monthly capital ?ows, EMP index and the CA for Mexico
and Thailand, two important emerging market countries which experienced severe
crisis in 1994 and 1997, respectively. In Figures 1 and 2, we see that the ?rst signs of
stress show up in net capital ?ows, rather than in the EMP index. Starting with the
?rst months of 1994 in the case of Mexico, there is a signi?cant increase in the volatility
of capital ?ows – our SS indicators identify the beginning of the SS crisis as mid-1994.
On the other hand, EMP reaches historically high levels at the end of 1994. Finally,
the reversal in the CA follows after mid-1995. Similar patterns are detected in the case
of Thailand in Figure 2. Thus, timing of volatility spikes in these economic variables
should be examined in a systematic manner.
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Figure 1.
The 1994 crisis in Mexico
Net Capital Flows-Mexico
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Figure 2.
The 1997 crisis in Thailand
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The ?rst section of Table II shows how our measures identify some of the well-known
emerging market crises. Both the 1994 Mexican Tequila crisis and the 1997 Asian crisis
are identi?ed yet there are minor discrepancies. For example, while all of the measures
indicate crisis for Thailand, SS1 covers three years 1996-1998, SS2 shows only 1997
and CC covers 1997 and 1998. For Brazil, Argentina and Russia, disagreement across
measures become even greater. The SS measures fail to identify the Brazilian crisis,
while the CC measure completely misses the Argentinean crisis.
The second section of Table II lists the crises that are identi?ed by all of the
measures. As mentioned in the introduction, this list of 12 observations covers most of
the well-publicized crises of the 1990s. But what about all the other crises of the 1990s?
The last section of Table II presents the number of years that are identi?ed as crisis by
our various measures. Out of 344 observations, SS1 identi?ed 47 incidences and SS2
identi?ed 29 incidences. On the other hand, the number of identi?ed CC is 59. When
consecutive crisis years are taken as one episode, SS1 and SS2 produce similar lower
numbers (22 and 26). The number of CC episodes also falls but remains greater than the
SSs (35). Thus, there are many CC that do not overlap with either type of SSs.
Table III presents the correlation coef?cients across the three measures.
The correlation coef?cients are all very close to 30 percent. While based on earlier
analyses, we did not expect very high correlations between the SS and CC measures,
we also ?nd that the two SS measures that should be measuring the same events are not
highly correlated. We also estimate combinations of bivariate probit regressions where
one crisis measure is regressed on the other. There is a 19-41 percent probability to have
the other type of crisis (SS1, SS2 or CC) when you have one of them (Table IV).
Both Tables III and IV con?rm the puzzling nature of SS and CC identi?cation.
The two-way frequencies are presented in Table V. The ?rst panel of Table V
reveals that out of 47 crisis observations identi?ed by SS1 only 15 (32 percent) of them
are also identi?ed by SS2 and half of the crisis identi?ed by SS2 is also identi?ed
SS1 SS2 CC
Major crisis of the 1990s
Mexico 1994, 1995 1994, 1995 1994, 1995
Thailand 1996-1998 1997 1997, 1998
Korea 1997, 1998 1997 1996, 1997
Philippines 1997, 1998 1997, 1998 1997, 1998
Malaysia 1997, 1998 1997, 1998 1997, 1998
Indonesia 1997, 1998 1997 1997, 1998
Brazil 1998, 1999
Argentina 1998-2002 2001
Russia Na 1998 1996-1998
Crisis that all three
measures identify
Indonesia 1997, Korea 1997,
Malaysia 1997-1998, Mexico 1994-1995,
Philippines 1997-1998, Thailand 1997,
Turkey 1994, Turkey 2001, Venezuela 1994
Number of years crisis
identi?ed as crisis 47 29 59
Number of episodes
identi?ed as crisis 22 26 35
Table II.
Measures of emerging
market crisis
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by SS1; 15 out of 29. The next two panels show similar relationships between the SS
measures and the CC measure, with the similar levels of overlap.
We also examined timing relationships between SS1, SS2 and CC episodes and
found that SS1 starting years more frequently precede CC episodes rather than follow
them (Table VI). The relationship is not as strong between SS2 and CC. The temporal
ordering ?nding has important policy implications for early detection of the
approaching crises. The SS1 measure has early warning advantages.
4. Additional complications
In this section, we discuss several other complications that arise in the conceptual
de?nitions and identi?cation of SSs and CC. The ?rst is the inclusion of CARs
to the analysis. The majority of de?nitions surveyed in Table I identify SSs based on
the FA from a country’s BOP. Since BOP identity requires that the CA plus FA plus
changes in reserves equals zero, a sharp reduction in the FA must be accompanied by
an abrupt improvement in the CA (typically referred to as a CAR), unless offset by a
liquidation of international reserves. Furthermore, crisis-related domestic currency
No Yes Total
SS2
SS1 No 283 14 (48%) 297
Yes 32 (68%) 15 (32%)
(52%)
47
Total 315 29 344
CC
SS1 No 261 36 (61%) 297
Yes 24 (51%) 23 (49%)
(39%)
47
Total 285 59 344
CC
SS2 No 271 44 (75%) 315
Yes 14 (48%) 15 (52%)
(25%)
29
Total 285 59 344
Table V.
Two-way frequencies
SS1 ¼ 1 Then SS2 ¼ 1 with 25% probability
CC ¼ 1 with 33% probability
SS2 ¼ 1 Then SS1 ¼ 1 with 41% probability
CC ¼ 1 with 35% probability
CC ¼ 1 Then SS1 ¼ 1 with 29% probability
SS2 ¼ 1 with 19% probability
Table IV.
Probit estimation results
SS1 SS2 CC
SS1 1.00
SS2 0.32 1.00
CC 0.30 0.28 1.00
Table III.
Correlations of crisis
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depreciations potentially link the FA and the CA. These relationships have led to
varying interpretations about how CC and CARs are related to SSs. For instance,
Guidotti et al. (2004) de?ne a CAR conditional on the occurrence of SSs[3], while
Hutchison and Noy (2006) and Komarek and Melecky (2005) de?ne an SS as the joint
occurrence of CC and CARs. Calvo et al. (2004) argue that measures of crisis should be
more closely linked to large and unexpected capital account movements rather than to
measures that are based on exchange rate movements and or CARs. They also show
that SSs generally precede CARs. Edwards (2004) ?nds that 46.1 percent of SSs
coincide with CARs, and 22.9 percent of countries with CARs also experience an SS in
the same year, yet they conclude that these events are not statistically independent.
In contrast, in an earlier study Milesi-Ferretti and Razin (2000) ?nd little coincidence or
precedence between these CC and CARs and they call these two events “distinct.”
A second issue is the source of capital ?ows. The premise taken in much of the
literature on SSs is that these crises are motivated by the actions of foreign investors.
In some instances, researchers’ focus on foreign investors is made explicit. For
example, Edwards (2005) de?nes an SS as “an abrupt and major reduction in capital
in?ows to a country that up to that time had been receiving large volumes of foreign
capital.” On the other hand, some papers do acknowledge the role of domestic investors
during SSs. Calvo and Reinhart (2000) indicate “[. . .] a large negative swing in
the capital account can also be due to a surge in [domestic] capital ?ight.” What these
papers and much of the empirical literature share in common, however, is that SSs
are measured using net capital ?ow data, hence foreign and domestic capital ?ows
are aggregated.
Recently, several papers have argued that domestic investors, as opposed to foreign
investors, are the originators of many SSs (Rothenberg and Warnock, 2007; Cowan et al.,
2008; Cowan and De Gregorio, 2007). Anon-trivial number of SSs, these papers contend,
are not cases in which anemerging market countryis abruptly cut off fromglobal capital
markets; rather, it is access to these very markets that serve as the vehicle for domestic
capital to take ?ight. The possibility of a massive exodus of domestic capital is also
related to the so-called “capital ?ight” literature which interprets abnormal domestic
capital out?ows – often through unrecorded channels and in response to government
restrictions and socioeconomic uncertainty – as a drain on a country’s resources
(Schneider, 2003).
A third issue is that SS measures which are based on the net FA will not re?ect the
changes in the composition of capital ?ows. This may lead to serious bias in identifying
crisis episodes. The concept of an SS, a sharp reduction in capital ?ows, generally refers
to hot money ?ows like portfolio investment and private loan ?ows. It has been widely
accepted that these types of capital ?ows are signi?cantly more reversible than foreign
Frequency
SS1 precedes CC 7
CC precedes SS1 2
SS2 precedes CC 8
CC precedes SS2 6
Note: Starting years of episodes are no more than two years apart
Table VI.
Timing relationship
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direct investment (FDI)[4]. For example, during numerous crisis episodes including
Mexico 1994, Asia 1997, Russia 1998 and Turkey 2001 portfolio and private loan in?ows
hadsharpfalls but FDI continuedto ?owinto these economies. Furthermore, manyof the
emerging market countries receive loans from the IMF or other national governments
during crises. In these instances the decrease in hot money ?ows will be partially offset
by the rise in FDI and of?cial loans, producing a net FA that does not re?ect the true
impact of SSs on the ?nancial markets[5]. These issues can be easily circumvented in
case studies. However, in cross-country analysis they may prevent some of the less
known SSs from being identi?ed.
Finally, the measurement of CC is not straightforward either. One important issue is
the choice of weights for the components of the EMP index[6]. Theoretically, the weights
should be based onthe elasticities of demand and supply inthe foreign exchange market.
Since measuring elasticities is extremely dif?cult in practice, studies use either equal
weights or the so-called precision weights – the inverse of variances of the changes in
exchange rates and reserves as weights in the EMP. In addition to the weighting
problem, the EMP index is measured with or without the inclusion of interest rates and
with replacing the nominal exchange rate by the real exchange rate. Furthermore,
there is no clear theoretical basis for choosing standard deviation thresholds. It should
also be noted here that there are studies that use only the exchange rate movements to
identify crisis. These are labeled currency crashes. The literature on the shortcomings of
CC identi?cation is more mature, yet the problems remain.
5. Conclusion
The severity of recent balance-of-payments crises in the emerging markets and
developing economies have generated enormous interest in understanding the nature of
these crises and for producing appropriate policy recommendations. One of the crucial
issues in this area of research is to develop a sound methodology for crisis identi?cation.
Prominent emerging market crises such as in Mexico 1994-1995, Thailand 1997-1998,
and Argentina 2001-2002 are well known, thus a researcher could use his or her informed
knowledge to de?ne these as CC and/or SSs and distinguish crisis periods from
non-crisis periods. However, identifying crises based on the researcher’s discretion risks
incorporating selection bias into the analysis in favor of more severe episodes. Indeed,
the three crises that we mention here are well known, at least in part, because of the
severe economic recessions and the resulting intense media coverage. In this paper,
we examined the empirical regularities among three types of commonly used crisis
measures. We show that there is substantial difference among the crisis dates identi?ed
bydifferent measures. SSs andCCoverlapless than50 percent of the time andSSs mostly
precede CC. Our results suggest that SSs and CC may be different types of events but
they are not completely independent of one another. More surprisingly, alternative SS
measures showconsiderable disagreement as well. Since they are all created to measure
the same economic phenomena, our results document the sensitivity of these measures
and point out potential problems for the researcher.
Although it is tempting to look for the one best measure of crises, we think that the
proper analysis should focus on how to use these different measures to understand
the nature of the crises. Thus, SS and CC measures should be used as complements,
rather than substitutes. Both types of measures could be useful to understand different
features of the crisis episodes. Further study of their lead-lag relationships and
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possible differences in the determinants of the different types of crises and their effects
are important areas for further research. Whether many of these relationships
were stable over different decades need to be investigated as well, as for example,
the impact of twin de?cits on SSs changed over the last 35 years (Efremidze and
Tomohara, 2011).
One particularly important area for further research is to focus more on
measures of the severity of CC and SSs. Most studies have just used zero-one dummies
for the occurrence of SSs and CC but according to Efremidze et al. (2011) both the
determinants and effects of mild events may differ substantially from those of severe
crises.
Notes
1. See Calvo et al. (2004) for a more detailed explanation.
2. Our sample period captures a period of frequent crises and high degree of capital mobility.
The source of the data is the International Financial Statistics Database produced by
International Monetary Fund (IMF). The emerging markets included in the sample are
selected based on the Economist magazine’s classi?cation. They are: China, Hong Kong,
India, Indonesia, Malaysia, Philippines, Singapore, South Korea, Taiwan, Thailand,
Czech Republic, Hungary, Poland, Russia, Turkey, Egypt, Israel, South Africa, Argentina,
Brazil, Chile, Colombia, Mexico, Peru, and Venezuela.
3. Guidotti et al. (2004, p. 79) identify 313 SS observations (of a total of 3,579) using a variant of
equation (1). Of these observations, they ?nd 265 occurred with a CAR and 48 did not.
“As can be immediately concluded, SSs most likely lead to current account adjustments.”
4. For a survey of studies on composition of capital ?ows (Sula and Willett, 2009) who ?nd that
surges in these types of capital ?ows are more likely to be followed by reversals.
5. One reason for the rise in FDI during crisis is the depreciation of currency and domestic
assets, increasing the pro?tability of some sectors such as FDIs in export industries.
Furthermore, if the market value of a ?rm falls during the crisis, then in?ows may increase
to take advantage of low prices (Krugman, 2000).
6. See Eichengreen et al. (1996) for the application of the EMP index as a crisis indicator and
Willett et al. (2005) for a detailed discussion of the complications.
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About the authors
Dr Levan Efremidze is an Assistant Professor of Economics at the Economics Department of
Claremont Graduate University. He has a PhD in Economics from the Claremont Graduate
University and has a Bachelor’s degree in Economics and Industrial Engineering from
Tbilisi State University. He also works with the Center for Neuroeconomics Studies at Claremont
Graduate University and leads its research group in the experimental asset market bubbles.
Prior to joining the Claremont Graduate University, he was an Economist at the UCLA Anderson
Forecast contributing to the econometric modelling and forecasting of the USA, California,
Los Angeles and San Francisco economies. Previously he was also a Lecturer of Economics at
Pomona College, worked as a Market Specialist and consulted on the design of trading rules at the
Caucasus Stock Exchange, and managed marketing research and operations at ATX and Nissin.
His ongoing research focuses on ?nancial crises, trade and budget de?cits, global imbalances,
asset price bubbles, international capital ?ows, and ?at tax reforms in the transition economies
of the former communist countries. He is a recipient of the Benjamin Franklin and Edmund
Muskie fellowships (under the Fulbright Scholarship program). Levan Efremidze is the
corresponding author and can be contacted at: [email protected]
Dr Samuel M. Schreyer graduated from Claremont Graduate School in 2009 with a PhD in
Economics specializing in the ?elds of international ?nance and money and banking. He has
taught at universities throughout the USA and is currently an Assistant Professor of Economics
at Fort Hays State University. His published research has appeared in journals such as the
Journal of Economic Development covering topics such as in?ation uncertainty, the Taylor Rule,
and international capital ?ows.
Ozan Sula received his PhD from Claremont Graduate University in 2006. Prior to joining
Western in the fall of 2006, he was a Lecturer at California State University – Fullerton. He also
taught at the University of La Verne and Claremont Graduate University. His current research
interests include the behaviour of international capital ?ows, international reserve policies and
the ?nancial crises in emerging markets.
Sudden stops
and currency
crises
321
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doc_254645527.pdf
The purpose of this paper is to examine empirical characteristics of two commonly
mentioned expressions of international financial crisis, “sudden stops” and currency crises.
Journal of Financial Economic Policy
Sudden stops and currency crises
Levan Efremidze Samuel M. Schreyer Ozan Sula
Article information:
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Financial Economic Policy, Vol. 3 Iss 4 pp. 304 - 321
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Sudden stops and currency crises
Levan Efremidze
Claremont Institute for Economic Policy Studies and Center for Neuroeconomics
Studies, Claremont Graduate University, Claremont, California, USA
Samuel M. Schreyer
Department of Economics, Finance, and Accounting,
Fort Hays State University, Hays, Kansas, USA, and
Ozan Sula
Department of Economics, Western Washington University,
Bellingham, Washington, USA and
Claremont Institute for Economic Policy Studies,
Claremont Graduate University, Claremont, California, USA
Abstract
Purpose – The purpose of this paper is to examine empirical characteristics of two commonly
mentioned expressions of international ?nancial crisis, “sudden stops” and currency crises.
Design/methodology/approach – Sudden stop and currency crisis events are identi?ed and
empirical regularities among them are analyzed based on the annual data of 25 emerging market
countries from 1990 to 2003.
Findings – Puzzlingly, these two seemingly close expressions of crises overlap less than 50 percent
of the time and sudden stops more frequently precede than follow currency crises. Also the two
different sudden stop measures are not strongly correlated with each other.
Research limitations/implications – This shows that it can make a great deal of difference what
measure is used and suggests that studies in this area should be sure to check the robustness of their
results to different measures.
Practical implications – The authors think that the proper analysis should focus on how to use
these different measures to understand the nature of the crises. Thus, sudden stop and currency crisis
measures should be used as complements, rather than substitutes.
Social implications – The alarming frequency of the emerging market crises during the last three
decades has motivated a large volume of theoretical and empirical literature on the subject. The
paper’s results advance understanding of these events.
Originality/value – A large body of studies on currency crises coexists with a growing
literature on sudden stops yet a majority of the studies that investigate either one of these
phenomena do not mention the other. The paper adds value by investigating empirical relationships
between them.
Keywords International economics, International ?nance, International investments,
Capital movements, International factor movements, International business, Current account adjustment,
Open economy, Macroeconomics, International trade, Financial markets
Paper type Research paper
1. Introduction
The alarming frequency of the emerging market crises during the last three decades
has motivated a large volume of theoretical and empirical literature on the subject.
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1757-6385.htm
JEL classi?cation – F02, F0, F, F2, F2, F3, F32, F41, F4, G15, G1, G
JFEP
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Journal of Financial Economic Policy
Vol. 3 No. 4, 2011
pp. 304-321
qEmerald Group Publishing Limited
1757-6385
DOI 10.1108/17576381111182891
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The signi?cance of these events is obvious from the sizable output losses the affected
countries suffered. Argentina’s losses in the early 1980s crisis amounted to 55 percent of
its GDP. During the 1997 Asian crises, Thailand, Indonesia, Malaysia and South Korea
experienced on average an 11 percent drop in their per capita real GDP (RGDP).
Increased volatility of international capital ?ows and sharp exchange rate movements
have been a de?ning feature of these crises.
A survey of the vast literature on these events leads to a conceptual puzzle. Most of
the widely publicized emerging market crises are classi?ed as both sudden stops (SSs)
and currency crises (CC). A large body of studies on CC coexists with a growing
literature on SSs yet a majority of the studies that investigate either one of these
phenomena do not mention the other. Implicitly these two types of events are either
assumed to be the same or completely unrelated. Among the small number of studies
that do discuss both, Hutchison and Noy (2006) de?ne an SS as the joint occurrence of
CC and current account reversal (CAR), and Calvo et al. (2004) treats them as separate
yet related events. Calvo et al. (2004) point out the timing difference between SSs and CC
and prefer using SSs to study crises, which are seen as originating from “credit shocks
in international markets.” The study also argues that a measure that is based on the
?nancial account (FA) would identify more crises episodes than CA de?cit-based
measures, because some countries have low volatility in CAs.
In addition to the ambiguities of the crisis concepts, there is the measurement issue. The
term“sudden stop” was ?rst introduced by Dornbusch et al. (1995). It refers to sudden and
large drops incapital in?ows andthe termcomes fromthe banker’s adage: “it isn’t the speed
that kills you, it’s the sudden stop.” While the essence of this description of SSs is broadly
representative of that taken in the literature, there has been less consensus on howto de?ne
these events empirically. This is not entirely surprising. A description of SSs – as with
many macroeconomic phenomena – does not lend itself to a single, precise mathematical
criterion. Similar debates on the appropriate measure to identify CC also exist.
In this study, our goal is to further examine these issues. First, we present a short
survey of the measures of SSs used in the recent literature. There are two major
alternative empirical approaches. In Section 3, we examine the empirical characteristics
of these measures of SSs and their relationships to measures of CC using annual data for
25 emerging market countries from 1990 to 2003. We ?nd that not only do the CC
and SSs often fail to overlap but also the two different SS measures are not strongly
correlated with each other. This shows that it can make a great deal of difference what
measure is used and suggests that studies in this area should be sure to check the
robustness of their results to different measures. In Section 4, we discuss several other
complications that arise in the crisis literature. We conclude that CC and SSs are neither
the same nor unrelated.
2. Measures of SSs
To capture a broad spectrum of conceptual and empirical de?nitions of SSs,
we conducted a keyword search using “sudden stops” in the EconLit database. The
search yielded 30 published and working papers since 2004 which are shown in Table I,
along with a brief de?nition and description of the main crisis or SS measures used in
each paper. A brief examination of this table reveals that myriad criteria have been
used in the recent literature to identify SSs. Nonetheless, there are several facets that
many of these de?nitions have in common.
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(
1
)
D
R
G
D
P
,
0
;
(
2
)
D
F
A
#
m
2
2
s
;
a
n
d
/
o
r
(
3
)
D
F
A
/
G
D
P
#
2
3
%
F
A
i
s
o
b
t
a
i
n
e
d
b
y
s
u
b
t
r
a
c
t
i
n
g
t
h
e
t
r
a
d
e
b
a
l
a
n
c
e
f
r
o
m
c
h
a
n
g
e
s
i
n
r
e
s
e
r
v
e
s
.
A
n
S
S
m
u
s
t
o
c
c
u
r
w
i
t
h
(
1
)
a
d
e
c
r
e
a
s
e
i
n
R
G
D
P
a
t
t
i
m
e
t
a
n
d
/
o
r
t
þ
1
,
a
n
d
e
i
t
h
e
r
o
r
b
o
t
h
o
f
t
h
e
f
o
l
l
o
w
i
n
g
c
o
n
d
i
t
i
o
n
s
:
(
2
)
D
F
A
b
e
a
t
l
e
a
s
t
t
w
o
S
D
s
b
e
l
o
w
i
t
s
m
e
a
n
;
(
3
)
t
h
e
?
r
s
t
y
e
a
r
t
h
a
t
a
d
r
o
p
i
n
F
A
i
s
3
p
e
r
c
e
n
t
o
f
G
D
P
o
v
e
r
a
p
e
r
i
o
d
s
h
o
r
t
e
r
t
h
a
n
f
o
u
r
y
e
a
r
s
1
8
8
0
-
1
9
1
3
,
a
n
n
u
a
l
;
2
0
e
m
e
r
g
i
n
g
m
a
r
k
e
t
s
C
a
l
v
o
e
t
a
l
.
(
2
0
0
6
a
,
b
)
(
1
)
D
F
A
#
m
2
2
s
;
(
2
)
D
(
s
p
r
e
a
d
)
#
m
2
2
s
A
s
y
s
t
e
m
i
c
S
S
(
S
S
S
)
–
t
h
a
t
i
s
,
a
c
r
i
s
i
s
r
e
?
e
c
t
i
n
g
s
h
o
c
k
s
t
o
t
h
e
c
a
p
i
t
a
l
m
a
r
k
e
t
s
–
r
e
q
u
i
r
e
s
t
h
a
t
(
1
)
D
F
A
i
s
a
t
l
e
a
s
t
t
w
o
S
D
s
b
e
l
o
w
i
t
s
m
e
a
n
,
a
n
d
(
2
)
t
h
e
c
h
a
n
g
e
i
n
t
h
e
a
g
g
r
e
g
a
t
e
b
o
n
d
s
p
r
e
a
d
(
e
.
g
.
J
.
P
.
M
o
r
g
a
n
’
s
E
m
e
r
g
i
n
g
M
a
r
k
e
t
B
o
n
d
I
n
d
e
x
s
p
r
e
a
d
o
v
e
r
U
S
T
r
e
a
s
u
r
y
b
o
n
d
s
,
m
e
a
s
u
r
e
d
i
n
l
o
g
s
)
i
s
a
t
l
e
a
s
t
t
w
o
S
D
s
b
e
l
o
w
i
t
s
m
e
a
n
.
A
“
c
a
p
i
t
a
l
?
o
w
w
i
n
d
o
w
”
a
n
d
“
a
g
g
r
e
g
a
t
e
s
p
r
e
a
d
w
i
n
d
o
w
”
i
s
c
o
n
s
t
r
u
c
t
e
d
b
y
m
a
r
k
i
n
g
t
h
e
s
t
a
r
t
/
e
n
d
o
f
e
a
c
h
w
i
n
d
o
w
a
s
t
h
e
?
r
s
t
p
e
r
i
o
d
t
h
a
t
D
F
A
a
n
d
D
(
s
p
r
e
a
d
)
a
r
e
o
n
e
S
D
b
e
l
o
w
t
h
e
i
r
m
e
a
n
b
e
f
o
r
e
/
a
f
t
e
r
(
1
)
a
n
d
(
2
)
a
r
e
s
a
t
i
s
?
e
d
,
r
e
s
p
e
c
t
i
v
e
l
y
.
A
n
S
S
S
o
c
c
u
r
s
w
h
e
n
t
h
e
s
e
w
i
n
d
o
w
s
o
v
e
r
l
a
p
.
T
h
e
m
e
a
n
s
a
n
d
S
D
s
i
n
(
1
)
a
n
d
(
2
)
a
r
e
m
e
a
s
u
r
e
d
o
n
a
r
o
l
l
i
n
g
b
a
s
i
s
(
h
i
s
t
o
r
i
c
a
l
)
,
w
i
t
h
t
h
e
?
r
s
t
t
w
o
y
e
a
r
s
o
f
d
a
t
a
e
x
c
l
u
d
e
d
.
C
a
p
i
t
a
l
?
o
w
w
i
n
d
o
w
s
o
c
c
u
r
r
i
n
g
s
i
x
m
o
n
t
h
s
o
r
l
e
s
s
a
p
a
r
t
f
r
o
m
a
n
o
t
h
e
r
a
r
e
c
o
n
s
i
d
e
r
e
d
t
h
e
s
a
m
e
w
i
n
d
o
w
1
9
9
0
-
2
0
0
1
,
m
o
n
t
h
l
y
;
1
5
e
m
e
r
g
i
n
g
m
a
r
k
e
t
s
a
n
d
1
7
d
e
v
e
l
o
p
e
d
e
c
o
n
o
m
i
e
s
C
a
l
v
o
e
t
a
l
.
(
2
0
0
4
)
(
1
)
D
F
A
#
m
2
2
s
;
(
2
)
D
G
D
P
,
0
A
“
c
a
p
i
t
a
l
?
o
w
w
i
n
d
o
w
”
i
s
c
o
n
s
t
r
u
c
t
e
d
a
s
d
e
s
c
r
i
b
e
d
i
n
C
a
l
v
o
e
t
a
l
.
(
2
0
0
6
)
w
h
e
n
D
F
A
i
s
a
t
l
e
a
s
t
t
w
o
S
D
s
b
e
l
o
w
i
t
s
m
e
a
n
.
A
n
S
S
o
c
c
u
r
s
w
h
e
n
o
u
t
p
u
t
d
r
o
p
s
d
u
r
i
n
g
t
h
e
“
c
a
p
i
t
a
l
?
o
w
w
i
n
d
o
w
.
”
T
h
e
m
e
a
n
a
n
d
S
D
i
n
(
1
)
i
s
m
e
a
s
u
r
e
d
o
n
a
r
o
l
l
i
n
g
b
a
s
i
s
(
h
i
s
t
o
r
i
c
a
l
)
,
w
i
t
h
t
h
e
?
r
s
t
t
w
o
y
e
a
r
s
o
f
d
a
t
a
e
x
c
l
u
d
e
d
1
9
9
0
-
2
0
0
1
,
m
o
n
t
h
l
y
;
1
5
e
m
e
r
g
i
n
g
m
a
r
k
e
t
s
a
n
d
1
7
d
e
v
e
l
o
p
e
d
e
c
o
n
o
m
i
e
s
(
c
o
n
t
i
n
u
e
d
)
Table I.
SS de?nitions
in the literature
JFEP
3,4
306
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
3
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
A
u
t
h
o
r
(
s
)
B
r
i
e
f
d
e
?
n
i
t
i
o
n
D
e
s
c
r
i
p
t
i
o
n
S
a
m
p
l
e
C
a
l
v
o
e
t
a
l
.
(
2
0
0
8
)
C
a
l
v
o
e
t
a
l
.
(
2
0
0
6
)
–
1
9
9
0
-
2
0
0
4
,
m
o
n
t
h
l
y
;
2
1
d
e
v
e
l
o
p
e
d
a
n
d
8
9
d
e
v
e
l
o
p
i
n
g
e
c
o
n
o
m
i
e
s
C
a
l
v
o
e
t
a
l
.
(
2
0
0
6
)
C
a
l
v
o
e
t
a
l
.
(
2
0
0
6
)
–
1
9
9
0
-
2
0
0
1
,
m
o
n
t
h
l
y
;
1
5
e
m
e
r
g
i
n
g
m
a
r
k
e
t
s
a
n
d
1
7
d
e
v
e
l
o
p
e
d
e
c
o
n
o
m
i
e
s
C
a
t
a
o
(
2
0
0
7
)
(
1
)
D
F
A
#
2
2
s
;
a
n
d
/
o
r
(
2
)
D
F
A
/
G
D
P
#
2
3
%
D
F
A
r
e
p
r
e
s
e
n
t
s
d
e
v
i
a
t
i
o
n
s
f
r
o
m
a
l
i
n
e
a
r
t
r
e
n
d
,
r
a
t
h
e
r
t
h
a
n
y
e
a
r
-
o
n
-
y
e
a
r
c
h
a
n
g
e
s
.
A
n
S
S
o
c
c
u
r
s
w
h
e
n
(
1
)
D
F
A
i
s
a
t
l
e
a
s
t
t
w
o
S
D
s
b
e
l
o
w
z
e
r
o
,
a
n
d
/
o
r
(
2
)
D
F
A
i
s
a
t
l
e
a
s
t
3
p
e
r
c
e
n
t
o
f
G
D
P
.
A
n
S
S
b
e
g
i
n
s
w
h
e
n
F
A
a
t
t
a
i
n
s
i
t
s
p
e
a
k
a
n
d
e
n
d
s
w
h
e
n
F
A
s
t
a
r
t
s
r
i
s
i
n
g
r
e
l
a
t
i
v
e
t
o
t
r
e
n
d
w
i
t
h
o
u
t
f
a
l
l
i
n
g
b
a
c
k
t
o
i
t
s
l
o
w
e
s
t
l
e
v
e
l
w
i
t
h
i
n
a
f
o
u
r
-
y
e
a
r
p
e
r
i
o
d
1
8
7
0
-
1
9
1
3
,
a
n
n
u
a
l
;
1
6
c
o
u
n
t
r
i
e
s
C
a
v
a
l
l
o
(
2
0
0
5
)
C
a
v
a
l
l
o
a
n
d
F
r
a
n
k
e
l
(
2
0
0
8
)
–
–
C
o
w
a
n
e
t
a
l
.
(
2
0
0
8
)
(
1
)
D
F
A
#
m
2
s
;
(
2
)
D
F
A
/
G
D
P
#
2
5
%
T
h
e
n
e
t
c
a
p
i
t
a
l
?
o
w
s
e
r
i
e
s
i
s
s
c
a
l
e
d
b
y
a
l
i
n
e
a
r
t
r
e
n
d
o
f
G
D
P
t
o
“
d
i
s
e
n
t
a
n
g
l
e
”
?
u
c
t
u
a
t
i
o
n
s
i
n
c
a
p
i
t
a
l
?
o
w
s
f
r
o
m
?
u
c
t
u
a
t
i
o
n
s
i
n
R
G
D
P
a
n
d
t
h
e
r
e
a
l
e
x
c
h
a
n
g
e
r
a
t
e
.
A
f
t
e
r
d
o
i
n
g
t
h
i
s
,
a
n
S
S
o
c
c
u
r
s
w
h
e
n
(
1
)
t
h
e
s
c
a
l
e
d
D
F
A
i
s
a
t
l
e
a
s
t
o
n
e
S
D
b
e
l
o
w
i
t
s
a
v
e
r
a
g
e
,
a
n
d
(
2
)
t
h
e
s
c
a
l
e
d
D
F
A
i
s
a
t
l
e
a
s
t
5
p
e
r
c
e
n
t
o
f
G
D
P
1
9
7
5
-
2
0
0
4
,
a
n
n
u
a
l
;
3
2
e
m
e
r
g
i
n
g
m
a
r
k
e
t
s
a
n
d
2
1
d
e
v
e
l
o
p
e
d
e
c
o
n
o
m
i
e
s
D
e
b
(
2
0
0
5
)
(
1
)
D
F
A
/
G
D
P
#
2
5
%
(
2
)
C
C
A
n
S
S
o
c
c
u
r
s
w
h
e
n
a
d
r
o
p
i
n
F
A
i
s
a
t
l
e
a
s
t
5
p
e
r
c
e
n
t
o
f
G
D
P
a
n
d
a
C
C
o
c
c
u
r
s
a
t
t
i
m
e
t
o
r
t
þ
1
1
9
7
5
-
1
9
9
9
,
a
n
n
u
a
l
;
a
l
l
p
o
s
s
i
b
l
e
c
o
u
n
t
r
i
e
s
D
u
r
d
u
e
t
a
l
.
(
2
0
0
9
)
–
T
h
e
a
u
t
h
o
r
s
u
s
e
S
S
s
i
d
e
n
t
i
?
e
d
“
i
n
v
a
r
i
o
u
s
e
m
p
i
r
i
c
a
l
s
t
u
d
i
e
s
,
i
n
c
l
u
d
i
n
g
C
a
l
v
o
e
t
a
l
.
(
2
0
0
4
)
,
C
a
v
a
l
l
o
a
n
d
F
r
a
n
k
e
l
(
2
0
0
8
)
a
n
d
R
o
t
h
e
n
b
e
r
g
a
n
d
W
a
r
n
o
c
k
(
2
0
0
6
)
”
1
9
8
5
-
2
0
0
4
,
a
n
n
u
a
l
;
1
7
e
m
e
r
g
i
n
g
m
a
r
k
e
t
s
E
d
w
a
r
d
s
(
2
0
0
4
)
(
1
)
F
A
.
x
(
2
)
D
F
A
/
G
D
P
#
2
5
%
A
n
S
S
o
c
c
u
r
s
w
h
e
n
(
1
)
a
c
o
u
n
t
r
y
r
e
c
e
i
v
e
s
a
n
i
n
?
o
w
o
f
c
a
p
i
t
a
l
l
a
r
g
e
r
t
h
a
n
t
h
e
t
h
i
r
d
q
u
a
r
t
i
l
e
o
f
i
n
?
o
w
s
f
o
r
t
h
e
r
e
g
i
o
n
(
x
)
d
u
r
i
n
g
t
h
e
p
r
e
v
i
o
u
s
t
w
o
y
e
a
r
s
o
f
t
h
e
c
r
i
s
i
s
,
a
n
d
(
2
)
n
e
t
c
a
p
i
t
a
l
i
n
?
o
w
s
d
e
c
l
i
n
e
b
y
a
t
l
e
a
s
t
5
p
e
r
c
e
n
t
o
f
G
D
P
1
9
7
0
-
2
0
0
1
,
a
n
n
u
a
l
;
a
l
l
p
o
s
s
i
b
l
e
c
o
u
n
t
r
i
e
s
E
d
w
a
r
d
s
(
2
0
0
5
)
E
d
w
a
r
d
s
(
2
0
0
4
)
–
–
E
d
w
a
r
d
s
(
2
0
0
6
)
E
d
w
a
r
d
s
(
2
0
0
4
)
–
–
(
c
o
n
t
i
n
u
e
d
)
Table I.
Sudden stops
and currency
crises
307
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
3
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
A
u
t
h
o
r
(
s
)
B
r
i
e
f
d
e
?
n
i
t
i
o
n
D
e
s
c
r
i
p
t
i
o
n
S
a
m
p
l
e
E
d
w
a
r
d
s
(
2
0
0
7
)
D
F
A
/
G
D
P
#
2
3
%
E
d
w
a
r
d
s
c
a
l
l
s
t
h
i
s
a
“
c
a
p
i
t
a
l
?
o
w
c
o
n
t
r
a
c
t
i
o
n
”
(
C
F
C
)
a
n
d
d
i
s
t
i
n
g
u
i
s
h
e
s
t
h
i
s
f
r
o
m
a
n
S
S
s
i
n
c
e
,
a
c
c
o
r
d
i
n
g
t
o
E
d
w
a
r
d
s
,
t
h
e
l
a
t
t
e
r
m
u
s
t
b
e
p
r
e
c
e
d
e
d
b
y
n
e
t
c
a
p
i
t
a
l
i
n
?
o
w
s
1
9
7
0
-
2
0
0
4
,
a
n
n
u
a
l
;
a
l
l
p
o
s
s
i
b
l
e
c
o
u
n
t
r
i
e
s
C
a
v
a
l
l
o
a
n
d
F
r
a
n
k
e
l
(
2
0
0
8
)
(
1
)
D
F
A
#
c
2
2
s
;
(
2
)
D
C
A
$
0
;
(
3
)
D
(
P
C
G
D
P
)
,
0
A
n
S
S
o
c
c
u
r
s
w
h
e
n
(
1
)
t
h
e
F
A
f
a
l
l
s
a
t
l
e
a
s
t
t
w
o
S
D
s
b
e
l
o
w
t
h
e
m
e
a
n
S
D
(
c
)
a
t
t
i
m
e
t
a
n
d
i
s
p
r
e
c
e
d
e
d
b
y
a
F
A
s
u
r
p
l
u
s
,
(
2
)
t
h
e
C
A
i
n
c
r
e
a
s
e
s
a
t
t
i
m
e
t
o
r
t
þ
1
a
n
d
i
s
p
r
e
c
e
d
e
d
b
y
a
C
A
d
e
?
c
i
t
,
a
n
d
(
3
)
p
e
r
c
a
p
i
t
a
G
D
P
(
P
C
G
D
P
)
f
a
l
l
s
a
t
t
i
m
e
t
o
r
t
þ
1
.
T
h
e
m
e
a
n
S
D
(
c
)
i
s
c
a
l
c
u
l
a
t
e
d
b
y
t
a
k
i
n
g
t
h
e
a
v
e
r
a
g
e
o
f
t
h
e
S
D
s
d
u
r
i
n
g
t
h
e
1
9
7
0
s
,
1
9
8
0
s
,
a
n
d
1
9
9
0
s
þ
1
9
7
0
-
2
0
0
2
,
a
n
n
u
a
l
;
a
l
l
p
o
s
s
i
b
l
e
c
o
u
n
t
r
i
e
s
G
a
l
l
e
g
o
a
n
d
J
o
n
e
s
(
2
0
0
5
)
(
1
)
D
F
A
#
m
2
2
s
A
n
S
S
i
s
d
e
?
n
e
d
b
y
a
“
c
a
p
i
t
a
l
?
o
w
w
i
n
d
o
w
”
(
C
a
l
v
o
e
t
a
l
.
,
2
0
0
6
)
w
h
e
n
D
F
A
i
s
a
t
l
e
a
s
t
t
w
o
S
D
s
b
e
l
o
w
i
t
s
m
e
a
n
1
9
9
0
-
2
0
0
3
,
m
o
n
t
h
l
y
;
1
4
e
m
e
r
g
i
n
g
m
a
r
k
e
t
s
G
u
i
d
o
t
t
i
e
t
a
l
.
(
2
0
0
4
)
(
1
)
D
F
A
#
m
2
s
;
(
2
)
D
F
A
/
G
D
P
#
2
5
%
S
S
s
a
r
e
i
d
e
n
t
i
?
e
d
b
y
(
1
)
a
r
e
d
u
c
t
i
o
n
i
n
n
e
t
c
a
p
i
t
a
l
?
o
w
s
b
y
a
t
l
e
a
s
t
o
n
e
S
D
b
e
l
o
w
i
t
s
m
e
a
n
,
a
n
d
(
2
)
t
h
e
r
e
d
u
c
t
i
o
n
i
n
n
e
t
c
a
p
i
t
a
l
?
o
w
s
a
r
e
a
t
l
e
a
s
t
5
p
e
r
c
e
n
t
o
f
G
D
P
1
9
7
4
-
2
0
0
2
,
a
n
n
u
a
l
;
a
l
l
p
o
s
s
i
b
l
e
c
o
u
n
t
r
i
e
s
H
o
n
i
g
(
2
0
0
8
)
(
1
)
D
F
A
#
2
2
s
;
(
2
)
D
C
A
$
0
;
(
3
)
D
(
P
C
G
D
P
)
,
0
A
n
S
S
o
c
c
u
r
s
w
h
e
n
(
1
)
t
h
e
F
A
f
a
l
l
s
a
t
l
e
a
s
t
t
w
o
S
D
s
b
e
l
o
w
z
e
r
o
a
t
t
i
m
e
t
a
n
d
i
s
p
r
e
c
e
d
e
d
b
y
a
F
A
s
u
r
p
l
u
s
,
(
2
)
t
h
e
C
A
i
n
c
r
e
a
s
e
s
a
t
t
i
m
e
t
o
r
t
þ
1
a
n
d
i
s
p
r
e
c
e
d
e
d
b
y
a
C
A
d
e
?
c
i
t
,
a
n
d
(
3
)
P
C
G
D
P
f
a
l
l
s
1
9
8
2
-
2
0
0
4
,
a
n
n
u
a
l
;
a
l
l
p
o
s
s
i
b
l
e
c
o
u
n
t
r
i
e
s
H
u
t
c
h
i
s
o
n
a
n
d
N
o
y
(
2
0
0
6
)
(
1
)
D
C
A
/
G
D
P
$
3
%
;
(
2
)
C
C
A
n
S
S
i
s
d
e
?
n
e
d
b
y
t
h
e
j
o
i
n
t
o
c
c
u
r
r
e
n
c
e
o
f
a
n
i
n
c
r
e
a
s
e
i
n
t
h
e
C
A
b
y
a
t
l
e
a
s
t
3
p
e
r
c
e
n
t
o
f
G
D
P
a
n
d
a
C
C
1
9
7
5
-
1
9
9
7
,
a
n
n
u
a
l
;
2
4
e
m
e
r
g
i
n
g
m
a
r
k
e
t
s
H
u
t
c
h
i
s
o
n
e
t
a
l
.
(
2
0
1
0
)
(
1
)
D
F
A
#
2
2
s
;
(
2
)
D
C
A
$
0
A
n
S
S
c
r
i
s
i
s
i
s
d
e
?
n
e
d
a
s
a
y
e
a
r
i
n
w
h
i
c
h
(
1
)
t
h
e
F
A
d
e
c
r
e
a
s
e
s
b
y
a
t
l
e
a
s
t
t
w
o
S
D
s
,
a
n
d
(
2
)
t
h
e
C
A
i
n
c
r
e
a
s
e
s
a
t
t
i
m
e
t
o
r
t
þ
1
a
n
d
i
s
p
r
e
c
e
d
e
d
b
y
a
d
e
?
c
i
t
1
9
8
0
-
2
0
0
3
,
a
n
n
u
a
l
;
8
3
S
S
s
(
o
c
c
u
r
r
i
n
g
i
n
6
6
n
o
n
-
O
E
C
D
c
o
u
n
t
r
i
e
s
)
J
e
a
n
n
e
a
n
d
R
a
n
c
i
e
r
(
2
0
0
6
)
B
e
c
k
e
r
a
n
d
M
a
u
r
o
(
2
0
0
6
)
–
1
9
7
5
-
2
0
0
3
,
a
n
n
u
a
l
;
3
4
e
m
e
r
g
i
n
g
m
a
r
k
e
t
s
(
c
o
n
t
i
n
u
e
d
)
Table I.
JFEP
3,4
308
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
3
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
A
u
t
h
o
r
(
s
)
B
r
i
e
f
d
e
?
n
i
t
i
o
n
D
e
s
c
r
i
p
t
i
o
n
S
a
m
p
l
e
J
o
y
c
e
a
n
d
N
a
b
a
r
(
2
0
0
9
)
–
J
o
y
c
e
a
n
d
N
a
b
a
r
u
s
e
S
S
s
i
d
e
n
t
i
?
e
d
b
y
e
i
t
h
e
r
F
r
a
n
k
e
l
a
n
d
C
a
l
v
o
(
2
0
0
4
)
o
r
C
a
l
v
o
e
t
a
l
.
(
2
0
0
4
)
1
9
7
6
-
2
0
0
2
,
a
n
n
u
a
l
;
2
6
e
m
e
r
g
i
n
g
m
a
r
k
e
t
s
K
o
m
a
r
e
k
a
n
d
M
e
l
e
c
k
y
(
2
0
0
5
)
D
C
A
/
G
D
P
$
3
%
A
n
S
S
i
s
d
e
?
n
e
d
b
y
a
C
A
R
t
h
a
t
i
s
a
t
l
e
a
s
t
3
p
e
r
c
e
n
t
o
f
G
D
P
1
9
9
3
-
2
0
0
1
,
a
n
n
u
a
l
;
5
9
e
m
e
r
g
i
n
g
m
a
r
k
e
t
s
K
o
m
a
r
e
k
e
t
a
l
.
(
2
0
0
5
)
D
C
A
/
G
D
P
$
2
.
5
%
A
n
S
S
i
s
d
e
?
n
e
d
b
y
a
C
A
R
t
h
a
t
i
s
a
t
l
e
a
s
t
2
.
5
p
e
r
c
e
n
t
o
f
G
D
P
1
9
9
3
-
2
0
0
0
,
a
n
n
u
a
l
;
2
3
e
m
e
r
g
i
n
g
m
a
r
k
e
t
s
a
n
d
d
e
v
e
l
o
p
i
n
g
e
c
o
n
o
m
i
e
s
L
e
v
c
h
e
n
k
o
a
n
d
M
a
u
r
o
(
2
0
0
6
)
B
e
c
k
e
r
a
n
d
M
a
u
r
o
(
2
0
0
6
)
–
1
9
7
0
-
2
0
0
3
,
a
n
n
u
a
l
;
a
l
l
p
o
s
s
i
b
l
e
c
o
u
n
t
r
i
e
s
O
r
t
i
z
e
t
a
l
.
(
2
0
0
7
)
C
a
l
v
o
e
t
a
l
.
(
2
0
0
6
)
–
1
9
9
0
-
2
0
0
6
,
m
o
n
t
h
l
y
;
3
1
e
m
e
r
g
i
n
g
m
a
r
k
e
t
s
R
o
t
h
e
n
b
e
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)
Table I.
Sudden stops
and currency
crises
309
D
o
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l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
3
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
First, the overwhelming majority of papers consider negative changes in net capital
?ows as the main variable of interest and do so using data on a country’s FA from its
balance of payments (BOP) statement. A country’s net FA represents the sum of
purchases/sales of domestic assets by foreigners and purchases/sales of foreign assets
by domestic residents. Negative changes in FA imply that the aggregated ?nancial
?ows are moving away from the country at a faster rate than in the previous period, or,
alternatively, these ?ows are coming into the country at a slower rate than the previous
period. It is important to emphasize that crisis de?nitions considering only negative
changes to FA allow for the possibility of a sudden slowdown of capital in?ows,
despite the conjured image of capital ?ows ceasing to ?ow inward as suggested by the
moniker SSs. The additional constraint that FA be negative when measured in levels
rather than ?rst differences ensures that only episodes of capital out?ows will be
considered (Edwards, 2004; Sula, 2010).
Another commonality shared between many of the SS de?nitions surveyed in Table I
is that the change in a country’s FA (DFA) be negative and less than a particular
threshold involving the mean and/or standard deviation of the DFA series (Calvo et al.,
2004; Bordo et al., 2010; Rothenberg and Warnock, 2007). Speci?cally, the following
type of criterion is used:
DFA
t
# m
DFA
2bs
DFA
ð1Þ
which indicates an SS occurs in a country when the change in its capital ?ows at time
t is negative and at least b standard deviations different from its mean, with the choice
of b tending to take a value between 1 and 2 (Guidotti et al., 2004; Gallego and Jones,
2005). Yet many variations of equation (1) exist. For example, Catao (2007) simply
omits m
DFA
; Rothenberg and Warnock (2007) measure m
DFA
and s
DFA
on a rolling
basis such that all data up to time t is used to compute these statistics; and Cavallo and
Frankel (2008) replace m
DFA
with the mean of the standard deviation of DFA for each
decade of their nearly three-decade long sample.
Several SS de?nitions in Table I follow a different approach. Their de?ning feature is
that a negative DFA must be suf?ciently large as a percent of GDP. Typically this
thresholdranges from3 to5 percent of GDP(Bordoet al., 2010; Catao, 2007). Inthis manner
the reduced capital in?ows or increased capital out?ows during an SS are required to be
economically large which contrasts with equation (1) since the latter requires only that
DFA be large relative to its own history. Indeed, solely using this criterion to indicate
SSs has been favored by some authors, such as Becker and Mauro (2006).
The standard measures of SSs should really be labeled as capital ?ow reversals.
The intuitive concept of SSs involves large capital in?ows that suddenly stop while the
standard measures would include a large increase in capital ?ight in their de?nitions.
Thus, Edwards (2007) interprets a 3 percent drop in FArelative to GDP as a capital ?ow
contraction and distinguishes this from an SS since the latter, according to the author,
must be preceded by capital in?ows. This issue is discussed further in Section 4.
On a related note, some authors require a decline in GDP, as a whole or on a per capita
basis, in order for an SS crisis to occur (Cavallo and Frankel, 2008; Calvo et al., 2004).
The purpose of this restriction is to rule out terms of trade improvement related FA
adjustments which could also look like an SS. This criterion necessarily limits analysis
to a subset of costly SSs, rather than considering the broader scenario of a marked
reduction of capital in?ows (Honig, 2008).
JFEP
3,4
310
D
o
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n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
3
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
In order to conduct our analysis, we selected one measure from each of the two
approaches that we have surveyed above. For standard deviation-based SS measures we
adopt the measure used in Calvo et al. (2004) as this is one of the ?rst studies to present a
statistical procedure to identify SSs and has been highly in?uential in the literature.
Their measure aims to capture the “unexpected” and “large” changes in FA, which at the
same time has a large negative effect on a country’s output. The authors use the large fall
in output as an additional criteria to identify those SSs that have negative economic
consequences.
Following this study, we derived annual SS dummies from monthly data. Monthly
capital ?ow series are constructed by netting out monthly exports and imports from
changes in monthly reserves. Then, the SS is de?ned as a phase where year-on-year
change in capital ?ows is at least two standard deviations below its sample mean.
The sample is de?ned as an expanding window with a minimum of 24 months of
previous observations. Once the SS phase is detected, it is converted into a dummy
variable with annual frequency. We impose the additional restriction of negative GDP
growth to identify an SS crisis[1]. We adopt the abbreviation SS1 for this measure in
the remainder of this article.
For SS measures that use thresholds based on GDP, we selected the measure used in
Edwards (2004), as it captures important features of this approach and it is widely cited
in the recent literature. The measure is based on the annual FA balance. An SS is
de?ned as a fall in net capital ?ows that is at least 5 percent of the current year’s GDP.
Also the country should have had positive net capital ?ows in the previous year.
We use the abbreviation SS2 for this measure for the remainder of this article.
To identifyCC, we use the commonlyadopted exchange market pressure (EMP) index.
CC dummies are constructed from changes in an index of EMP, de?ned as a weighted
average of monthly real exchange rate changes, monthly reserve losses and interest rate
changes. There is disagreement in the literature over whether is better to use equal or
precision weights (Willett et al., 2005). This is discussed in Section 4. Precision weights
are inverselyrelatedto the variance of changes of eachcomponent over the sample of each
country. We use the latter measure in our comparison. Annual crises dummies take
the value of 1 if the change in the pressure index exceeds the mean plus X times
the country-speci?c standard deviation where X usually ranges between 1.5 and 3.0,
we use 2.0. We adopt the abbreviationCCfor this measure for the remainder of this article.
3. Examination of empirical regularities
In our analysis, we use annual and monthly data for 25 emerging market countries
for the period of 1990-2003[2]. To illustrate the relationship between SSs and CC,
we present the behaviour of monthly capital ?ows, EMP index and the CA for Mexico
and Thailand, two important emerging market countries which experienced severe
crisis in 1994 and 1997, respectively. In Figures 1 and 2, we see that the ?rst signs of
stress show up in net capital ?ows, rather than in the EMP index. Starting with the
?rst months of 1994 in the case of Mexico, there is a signi?cant increase in the volatility
of capital ?ows – our SS indicators identify the beginning of the SS crisis as mid-1994.
On the other hand, EMP reaches historically high levels at the end of 1994. Finally,
the reversal in the CA follows after mid-1995. Similar patterns are detected in the case
of Thailand in Figure 2. Thus, timing of volatility spikes in these economic variables
should be examined in a systematic manner.
Sudden stops
and currency
crises
311
D
o
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n
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o
a
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d
b
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P
O
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D
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C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
3
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Figure 1.
The 1994 crisis in Mexico
Net Capital Flows-Mexico
–8,000
–6,000
–4,000
–2,000
0
2,000
4,000
6,000
1
9
9
1
0
1
Months
M
i
l
l
i
o
n
s
o
f
D
o
l
l
a
r
s
EMP Index-Mexico
–60
–40
–20
0
20
40
60
80
100
120
140
1
9
9
1
0
1
Months
E
M
P
I
n
d
e
x
Current Account-Mexico
–2,500
–2,000
–1,500
–1,000
–500
0
500
1,000
1
9
9
1
0
1
Months
M
i
l
l
i
o
n
s
o
f
D
o
l
l
a
r
s
8,000
10,000
1
9
9
1
0
6
1
9
9
1
1
1
1
9
9
2
0
4
1
9
9
2
0
9
1
9
9
3
0
2
1
9
9
3
0
7
1
9
9
3
1
2
1
9
9
4
0
5
1
9
9
4
1
0
1
9
9
5
0
3
1
9
9
5
0
8
1
9
9
6
0
1
1
9
9
6
0
6
1
9
9
6
1
1
1
9
9
7
0
4
1
9
9
7
0
9
1
9
9
1
0
6
1
9
9
1
1
1
1
9
9
2
0
4
1
9
9
2
0
9
1
9
9
3
0
2
1
9
9
3
0
7
1
9
9
3
1
2
1
9
9
4
0
5
1
9
9
4
1
0
1
9
9
5
0
3
1
9
9
5
0
8
1
9
9
6
0
1
1
9
9
6
0
6
1
9
9
6
1
1
1
9
9
7
0
4
1
9
9
7
0
9
1
9
9
1
0
9
1
9
9
2
0
1
1
9
9
2
0
5
1
9
9
2
0
9
1
9
9
3
0
1
1
9
9
3
0
5
1
9
9
3
0
9
1
9
9
4
0
1
1
9
9
4
0
5
1
9
9
4
0
9
1
9
9
5
0
1
1
9
9
5
0
5
1
9
9
5
0
9
1
9
9
6
0
1
1
9
9
6
0
5
1
9
9
6
0
9
1
9
9
7
0
1
1
9
9
7
0
5
1
9
9
7
0
9
1
9
9
1
0
5
JFEP
3,4
312
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a
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d
b
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P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
3
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Figure 2.
The 1997 crisis in Thailand
Net Capital Flows-Thailand
–8,000
–6,000
–4,000
–2,000
0
2,000
4,000
1
9
9
3
0
1
1
9
9
3
0
5
1
9
9
3
0
9
1
9
9
4
0
1
1
9
9
4
0
5
1
9
9
4
0
9
1
9
9
5
0
1
1
9
9
5
0
5
1
9
9
5
0
9
1
9
9
6
0
1
1
9
9
6
0
5
1
9
9
6
0
9
1
9
9
7
0
1
1
9
9
7
0
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1
9
9
7
0
9
1
9
9
8
0
1
1
9
9
8
0
5
1
9
9
8
0
9
1
9
9
9
0
1
1
9
9
9
0
5
1
9
9
9
0
9
Months
M
i
l
l
i
o
n
s
o
f
D
o
l
l
a
r
s
EMP Index-Thailand
–250
–200
–150
–100
–50
0
50
100
150
200
250
300
1
9
9
3
0
1
1
9
9
3
0
5
1
9
9
3
0
9
1
9
9
4
0
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1
9
9
4
0
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1
9
9
4
0
9
1
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9
5
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1
9
9
5
0
5
1
9
9
5
0
9
1
9
9
6
0
1
1
9
9
6
0
5
1
9
9
6
0
9
1
9
9
7
0
1
1
9
9
7
0
5
1
9
9
7
0
9
1
9
9
8
0
1
1
9
9
8
0
5
1
9
9
8
0
9
1
9
9
9
0
1
1
9
9
9
0
5
1
9
9
9
0
9
Months
E
M
P
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d
e
x
Current Account-Thailand
–2,500
–2,000
–1,500
–1,000
–500
0
500
1,000
1,500
1
9
9
3
0
1
1
9
9
3
0
6
1
9
9
3
1
1
1
9
9
4
0
4
1
9
9
4
0
9
1
9
9
5
0
2
1
9
9
5
0
7
1
9
9
5
1
2
1
9
9
6
0
5
1
9
9
6
1
0
1
9
9
7
0
3
1
9
9
7
0
8
1
9
9
8
0
1
1
9
9
8
0
6
1
9
9
8
1
1
1
9
9
9
0
4
1
9
9
9
0
9
Months
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The ?rst section of Table II shows how our measures identify some of the well-known
emerging market crises. Both the 1994 Mexican Tequila crisis and the 1997 Asian crisis
are identi?ed yet there are minor discrepancies. For example, while all of the measures
indicate crisis for Thailand, SS1 covers three years 1996-1998, SS2 shows only 1997
and CC covers 1997 and 1998. For Brazil, Argentina and Russia, disagreement across
measures become even greater. The SS measures fail to identify the Brazilian crisis,
while the CC measure completely misses the Argentinean crisis.
The second section of Table II lists the crises that are identi?ed by all of the
measures. As mentioned in the introduction, this list of 12 observations covers most of
the well-publicized crises of the 1990s. But what about all the other crises of the 1990s?
The last section of Table II presents the number of years that are identi?ed as crisis by
our various measures. Out of 344 observations, SS1 identi?ed 47 incidences and SS2
identi?ed 29 incidences. On the other hand, the number of identi?ed CC is 59. When
consecutive crisis years are taken as one episode, SS1 and SS2 produce similar lower
numbers (22 and 26). The number of CC episodes also falls but remains greater than the
SSs (35). Thus, there are many CC that do not overlap with either type of SSs.
Table III presents the correlation coef?cients across the three measures.
The correlation coef?cients are all very close to 30 percent. While based on earlier
analyses, we did not expect very high correlations between the SS and CC measures,
we also ?nd that the two SS measures that should be measuring the same events are not
highly correlated. We also estimate combinations of bivariate probit regressions where
one crisis measure is regressed on the other. There is a 19-41 percent probability to have
the other type of crisis (SS1, SS2 or CC) when you have one of them (Table IV).
Both Tables III and IV con?rm the puzzling nature of SS and CC identi?cation.
The two-way frequencies are presented in Table V. The ?rst panel of Table V
reveals that out of 47 crisis observations identi?ed by SS1 only 15 (32 percent) of them
are also identi?ed by SS2 and half of the crisis identi?ed by SS2 is also identi?ed
SS1 SS2 CC
Major crisis of the 1990s
Mexico 1994, 1995 1994, 1995 1994, 1995
Thailand 1996-1998 1997 1997, 1998
Korea 1997, 1998 1997 1996, 1997
Philippines 1997, 1998 1997, 1998 1997, 1998
Malaysia 1997, 1998 1997, 1998 1997, 1998
Indonesia 1997, 1998 1997 1997, 1998
Brazil 1998, 1999
Argentina 1998-2002 2001
Russia Na 1998 1996-1998
Crisis that all three
measures identify
Indonesia 1997, Korea 1997,
Malaysia 1997-1998, Mexico 1994-1995,
Philippines 1997-1998, Thailand 1997,
Turkey 1994, Turkey 2001, Venezuela 1994
Number of years crisis
identi?ed as crisis 47 29 59
Number of episodes
identi?ed as crisis 22 26 35
Table II.
Measures of emerging
market crisis
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by SS1; 15 out of 29. The next two panels show similar relationships between the SS
measures and the CC measure, with the similar levels of overlap.
We also examined timing relationships between SS1, SS2 and CC episodes and
found that SS1 starting years more frequently precede CC episodes rather than follow
them (Table VI). The relationship is not as strong between SS2 and CC. The temporal
ordering ?nding has important policy implications for early detection of the
approaching crises. The SS1 measure has early warning advantages.
4. Additional complications
In this section, we discuss several other complications that arise in the conceptual
de?nitions and identi?cation of SSs and CC. The ?rst is the inclusion of CARs
to the analysis. The majority of de?nitions surveyed in Table I identify SSs based on
the FA from a country’s BOP. Since BOP identity requires that the CA plus FA plus
changes in reserves equals zero, a sharp reduction in the FA must be accompanied by
an abrupt improvement in the CA (typically referred to as a CAR), unless offset by a
liquidation of international reserves. Furthermore, crisis-related domestic currency
No Yes Total
SS2
SS1 No 283 14 (48%) 297
Yes 32 (68%) 15 (32%)
(52%)
47
Total 315 29 344
CC
SS1 No 261 36 (61%) 297
Yes 24 (51%) 23 (49%)
(39%)
47
Total 285 59 344
CC
SS2 No 271 44 (75%) 315
Yes 14 (48%) 15 (52%)
(25%)
29
Total 285 59 344
Table V.
Two-way frequencies
SS1 ¼ 1 Then SS2 ¼ 1 with 25% probability
CC ¼ 1 with 33% probability
SS2 ¼ 1 Then SS1 ¼ 1 with 41% probability
CC ¼ 1 with 35% probability
CC ¼ 1 Then SS1 ¼ 1 with 29% probability
SS2 ¼ 1 with 19% probability
Table IV.
Probit estimation results
SS1 SS2 CC
SS1 1.00
SS2 0.32 1.00
CC 0.30 0.28 1.00
Table III.
Correlations of crisis
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depreciations potentially link the FA and the CA. These relationships have led to
varying interpretations about how CC and CARs are related to SSs. For instance,
Guidotti et al. (2004) de?ne a CAR conditional on the occurrence of SSs[3], while
Hutchison and Noy (2006) and Komarek and Melecky (2005) de?ne an SS as the joint
occurrence of CC and CARs. Calvo et al. (2004) argue that measures of crisis should be
more closely linked to large and unexpected capital account movements rather than to
measures that are based on exchange rate movements and or CARs. They also show
that SSs generally precede CARs. Edwards (2004) ?nds that 46.1 percent of SSs
coincide with CARs, and 22.9 percent of countries with CARs also experience an SS in
the same year, yet they conclude that these events are not statistically independent.
In contrast, in an earlier study Milesi-Ferretti and Razin (2000) ?nd little coincidence or
precedence between these CC and CARs and they call these two events “distinct.”
A second issue is the source of capital ?ows. The premise taken in much of the
literature on SSs is that these crises are motivated by the actions of foreign investors.
In some instances, researchers’ focus on foreign investors is made explicit. For
example, Edwards (2005) de?nes an SS as “an abrupt and major reduction in capital
in?ows to a country that up to that time had been receiving large volumes of foreign
capital.” On the other hand, some papers do acknowledge the role of domestic investors
during SSs. Calvo and Reinhart (2000) indicate “[. . .] a large negative swing in
the capital account can also be due to a surge in [domestic] capital ?ight.” What these
papers and much of the empirical literature share in common, however, is that SSs
are measured using net capital ?ow data, hence foreign and domestic capital ?ows
are aggregated.
Recently, several papers have argued that domestic investors, as opposed to foreign
investors, are the originators of many SSs (Rothenberg and Warnock, 2007; Cowan et al.,
2008; Cowan and De Gregorio, 2007). Anon-trivial number of SSs, these papers contend,
are not cases in which anemerging market countryis abruptly cut off fromglobal capital
markets; rather, it is access to these very markets that serve as the vehicle for domestic
capital to take ?ight. The possibility of a massive exodus of domestic capital is also
related to the so-called “capital ?ight” literature which interprets abnormal domestic
capital out?ows – often through unrecorded channels and in response to government
restrictions and socioeconomic uncertainty – as a drain on a country’s resources
(Schneider, 2003).
A third issue is that SS measures which are based on the net FA will not re?ect the
changes in the composition of capital ?ows. This may lead to serious bias in identifying
crisis episodes. The concept of an SS, a sharp reduction in capital ?ows, generally refers
to hot money ?ows like portfolio investment and private loan ?ows. It has been widely
accepted that these types of capital ?ows are signi?cantly more reversible than foreign
Frequency
SS1 precedes CC 7
CC precedes SS1 2
SS2 precedes CC 8
CC precedes SS2 6
Note: Starting years of episodes are no more than two years apart
Table VI.
Timing relationship
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direct investment (FDI)[4]. For example, during numerous crisis episodes including
Mexico 1994, Asia 1997, Russia 1998 and Turkey 2001 portfolio and private loan in?ows
hadsharpfalls but FDI continuedto ?owinto these economies. Furthermore, manyof the
emerging market countries receive loans from the IMF or other national governments
during crises. In these instances the decrease in hot money ?ows will be partially offset
by the rise in FDI and of?cial loans, producing a net FA that does not re?ect the true
impact of SSs on the ?nancial markets[5]. These issues can be easily circumvented in
case studies. However, in cross-country analysis they may prevent some of the less
known SSs from being identi?ed.
Finally, the measurement of CC is not straightforward either. One important issue is
the choice of weights for the components of the EMP index[6]. Theoretically, the weights
should be based onthe elasticities of demand and supply inthe foreign exchange market.
Since measuring elasticities is extremely dif?cult in practice, studies use either equal
weights or the so-called precision weights – the inverse of variances of the changes in
exchange rates and reserves as weights in the EMP. In addition to the weighting
problem, the EMP index is measured with or without the inclusion of interest rates and
with replacing the nominal exchange rate by the real exchange rate. Furthermore,
there is no clear theoretical basis for choosing standard deviation thresholds. It should
also be noted here that there are studies that use only the exchange rate movements to
identify crisis. These are labeled currency crashes. The literature on the shortcomings of
CC identi?cation is more mature, yet the problems remain.
5. Conclusion
The severity of recent balance-of-payments crises in the emerging markets and
developing economies have generated enormous interest in understanding the nature of
these crises and for producing appropriate policy recommendations. One of the crucial
issues in this area of research is to develop a sound methodology for crisis identi?cation.
Prominent emerging market crises such as in Mexico 1994-1995, Thailand 1997-1998,
and Argentina 2001-2002 are well known, thus a researcher could use his or her informed
knowledge to de?ne these as CC and/or SSs and distinguish crisis periods from
non-crisis periods. However, identifying crises based on the researcher’s discretion risks
incorporating selection bias into the analysis in favor of more severe episodes. Indeed,
the three crises that we mention here are well known, at least in part, because of the
severe economic recessions and the resulting intense media coverage. In this paper,
we examined the empirical regularities among three types of commonly used crisis
measures. We show that there is substantial difference among the crisis dates identi?ed
bydifferent measures. SSs andCCoverlapless than50 percent of the time andSSs mostly
precede CC. Our results suggest that SSs and CC may be different types of events but
they are not completely independent of one another. More surprisingly, alternative SS
measures showconsiderable disagreement as well. Since they are all created to measure
the same economic phenomena, our results document the sensitivity of these measures
and point out potential problems for the researcher.
Although it is tempting to look for the one best measure of crises, we think that the
proper analysis should focus on how to use these different measures to understand
the nature of the crises. Thus, SS and CC measures should be used as complements,
rather than substitutes. Both types of measures could be useful to understand different
features of the crisis episodes. Further study of their lead-lag relationships and
Sudden stops
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possible differences in the determinants of the different types of crises and their effects
are important areas for further research. Whether many of these relationships
were stable over different decades need to be investigated as well, as for example,
the impact of twin de?cits on SSs changed over the last 35 years (Efremidze and
Tomohara, 2011).
One particularly important area for further research is to focus more on
measures of the severity of CC and SSs. Most studies have just used zero-one dummies
for the occurrence of SSs and CC but according to Efremidze et al. (2011) both the
determinants and effects of mild events may differ substantially from those of severe
crises.
Notes
1. See Calvo et al. (2004) for a more detailed explanation.
2. Our sample period captures a period of frequent crises and high degree of capital mobility.
The source of the data is the International Financial Statistics Database produced by
International Monetary Fund (IMF). The emerging markets included in the sample are
selected based on the Economist magazine’s classi?cation. They are: China, Hong Kong,
India, Indonesia, Malaysia, Philippines, Singapore, South Korea, Taiwan, Thailand,
Czech Republic, Hungary, Poland, Russia, Turkey, Egypt, Israel, South Africa, Argentina,
Brazil, Chile, Colombia, Mexico, Peru, and Venezuela.
3. Guidotti et al. (2004, p. 79) identify 313 SS observations (of a total of 3,579) using a variant of
equation (1). Of these observations, they ?nd 265 occurred with a CAR and 48 did not.
“As can be immediately concluded, SSs most likely lead to current account adjustments.”
4. For a survey of studies on composition of capital ?ows (Sula and Willett, 2009) who ?nd that
surges in these types of capital ?ows are more likely to be followed by reversals.
5. One reason for the rise in FDI during crisis is the depreciation of currency and domestic
assets, increasing the pro?tability of some sectors such as FDIs in export industries.
Furthermore, if the market value of a ?rm falls during the crisis, then in?ows may increase
to take advantage of low prices (Krugman, 2000).
6. See Eichengreen et al. (1996) for the application of the EMP index as a crisis indicator and
Willett et al. (2005) for a detailed discussion of the complications.
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About the authors
Dr Levan Efremidze is an Assistant Professor of Economics at the Economics Department of
Claremont Graduate University. He has a PhD in Economics from the Claremont Graduate
University and has a Bachelor’s degree in Economics and Industrial Engineering from
Tbilisi State University. He also works with the Center for Neuroeconomics Studies at Claremont
Graduate University and leads its research group in the experimental asset market bubbles.
Prior to joining the Claremont Graduate University, he was an Economist at the UCLA Anderson
Forecast contributing to the econometric modelling and forecasting of the USA, California,
Los Angeles and San Francisco economies. Previously he was also a Lecturer of Economics at
Pomona College, worked as a Market Specialist and consulted on the design of trading rules at the
Caucasus Stock Exchange, and managed marketing research and operations at ATX and Nissin.
His ongoing research focuses on ?nancial crises, trade and budget de?cits, global imbalances,
asset price bubbles, international capital ?ows, and ?at tax reforms in the transition economies
of the former communist countries. He is a recipient of the Benjamin Franklin and Edmund
Muskie fellowships (under the Fulbright Scholarship program). Levan Efremidze is the
corresponding author and can be contacted at: [email protected]
Dr Samuel M. Schreyer graduated from Claremont Graduate School in 2009 with a PhD in
Economics specializing in the ?elds of international ?nance and money and banking. He has
taught at universities throughout the USA and is currently an Assistant Professor of Economics
at Fort Hays State University. His published research has appeared in journals such as the
Journal of Economic Development covering topics such as in?ation uncertainty, the Taylor Rule,
and international capital ?ows.
Ozan Sula received his PhD from Claremont Graduate University in 2006. Prior to joining
Western in the fall of 2006, he was a Lecturer at California State University – Fullerton. He also
taught at the University of La Verne and Claremont Graduate University. His current research
interests include the behaviour of international capital ?ows, international reserve policies and
the ?nancial crises in emerging markets.
Sudden stops
and currency
crises
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