I
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l J
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Adv
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I
J
AAS)
Vo
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15
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1
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ch
20
26
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p
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.
42
~
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1
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we
v
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lec
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m
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o
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th
ly
d
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ta
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m
1
9
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5
we
re
u
s
e
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in
th
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u
to
re
g
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m
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it
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sticity
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ARMACH
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,
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n
d
M
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rk
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h
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MS
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-
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h
y
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ri
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m
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ls
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p
lain
t
h
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c
risis.
M
o
d
e
l
in
terp
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tatio
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i
n
d
ica
tes
th
a
t
th
e
re
will
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e
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o
c
risis
fro
m
M
a
y
2
0
2
5
-
A
p
ril
2
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6
.
K
ey
w
o
r
d
s
:
C
u
r
r
en
cy
cr
is
is
E
ar
ly
war
n
in
g
s
y
s
tem
Ma
r
k
o
v
-
s
witch
in
g
No
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e
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to
-
s
ig
n
al
r
atio
Vo
latilit
y
m
o
d
el
T
h
is i
s
a
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o
p
e
n
a
c
c
e
ss
a
rticle
u
n
d
e
r th
e
CC B
Y
-
SA
li
c
e
n
se
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
Su
g
iy
an
to
Statis
t
ics Stu
d
y
Pro
g
r
am
,
Fac
u
lty
o
f
Ma
th
e
m
atics a
n
d
Natu
r
al
Scien
ce
s
,
Un
iv
er
s
itas
Seb
el
as
Ma
r
et
3
6
I
r
.
Su
tam
i Str
ee
t,
Ken
tin
g
a
n
,
J
eb
r
es,
Su
r
ak
a
r
ta,
C
en
tr
al
J
av
a
5
7
1
2
6
,
I
n
d
o
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esia
E
m
ail:
s
u
g
iy
an
to
6
1
@
s
taf
f
.
u
n
s
.
ac
.
id
1.
I
NT
RO
D
UCT
I
O
N
Fro
m
th
e
1
9
7
0
s
to
th
e
m
id
-
1
9
9
0
s
,
I
n
d
o
n
esia
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ec
o
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d
ed
s
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li
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n
o
m
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wth
,
c
o
n
tr
o
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d
in
f
latio
n
,
an
d
a
h
ea
lt
h
y
ex
ter
n
al
b
alan
ce
.
B
ec
au
s
e
o
f
th
is
s
tr
o
n
g
p
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f
o
r
m
an
ce
,
th
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ld
B
an
k
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ce
ca
lled
I
n
d
o
n
esia
an
ec
o
n
o
m
ic
m
ir
ac
le
[
1
]
.
Ho
wev
er
,
th
is
s
itu
atio
n
ch
an
g
e
d
d
r
asti
ca
lly
wh
en
th
e
Asi
an
Fin
an
c
ial
C
r
is
i
s
s
tr
u
ck
in
1
9
9
7
.
T
h
e
I
n
d
o
n
esian
ec
o
n
o
m
y
ag
ain
f
ac
ed
tu
r
b
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len
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d
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r
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2
0
0
7
-
2
0
0
8
Glo
b
a
l
Fin
an
cial
C
r
is
is
[
2
]
,
an
d
m
o
r
e
r
ec
e
n
tly
,
t
h
e
C
OVI
D
-
1
9
p
an
d
em
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in
2
0
2
0
tr
ig
g
er
e
d
a
g
lo
b
al
r
ec
ess
io
n
th
at
also
af
f
ec
ted
I
n
d
o
n
esia's
f
in
an
cial
s
tab
ilit
y
[
3
]
–
[
7
]
.
Pas
t
f
in
an
cial
s
tab
ilit
y
d
o
es
n
o
t
alwa
y
s
g
u
ar
an
tee
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r
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tect
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n
ag
ain
s
t
f
u
tu
r
e
d
is
r
u
p
tio
n
s
[
8
]
–
[
1
2
]
.
T
h
ese
co
n
d
itio
n
s
h
av
e
p
r
o
m
p
ted
p
o
licy
m
a
k
er
s
to
d
esig
n
an
ea
r
ly
war
n
in
g
s
y
s
tem
(
E
W
S)
u
s
in
g
m
ac
r
o
ec
o
n
o
m
ic
i
n
d
icato
r
s
to
an
ticip
ate
p
o
ten
tial
cr
is
e
s
.
Pre
v
io
u
s
r
esear
ch
h
as
i
d
en
tifie
d
ar
o
u
n
d
f
if
tee
n
k
ey
in
d
icato
r
s
—
s
u
ch
as
ex
p
o
r
t
an
d
im
p
o
r
t
p
er
f
o
r
m
an
ce
,
f
o
r
eig
n
ex
ch
a
n
g
e
r
eser
v
e
a
d
e
q
u
ac
y
,
in
ter
est
r
ate
d
if
f
er
en
tials
,
an
d
m
o
n
etar
y
a
g
g
r
eg
ates
—
th
at
ten
d
to
m
o
v
e
ah
ea
d
o
f
f
in
an
cial
s
tr
ess
[
1
3
]
,
[
1
4
]
.
Am
o
n
g
v
ar
io
u
s
s
elec
tio
n
tech
n
iq
u
es,
th
e
n
o
is
e
-
to
-
s
ig
n
al
r
atio
(
NSR
)
ap
p
r
o
ac
h
h
as
b
ee
n
wid
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o
p
ted
b
ec
au
s
e
lo
wer
NSR
v
alu
es im
p
ly
s
tr
o
n
g
er
p
r
e
d
ictiv
e
p
o
wer
f
o
r
d
etec
tin
g
cr
is
es
[
1
3
]
–
[
1
5
]
.
E
co
n
o
m
ic
a
n
d
f
i
n
an
cial
d
ata
o
f
ten
d
is
p
lay
v
o
latilit
y
clu
s
ter
in
g
,
m
ak
i
n
g
it
n
ec
ess
ar
y
t
o
u
s
e
m
o
d
els
th
at
ac
co
u
n
t
f
o
r
tim
e
-
v
ar
y
in
g
v
ar
ian
ce
.
T
h
e
au
t
o
r
eg
r
ess
iv
e
co
n
d
itio
n
al
h
eter
o
s
k
ed
asti
city
(
AR
C
H)
an
d
g
en
er
alize
d
au
t
o
r
eg
r
ess
iv
e
co
n
d
itio
n
al
h
eter
o
s
k
ed
asti
city
(
GAR
C
H)
m
o
d
els
in
tr
o
d
u
ce
d
b
y
E
n
g
le
[
1
6
]
an
d
Bo
ller
s
lev
[
1
7
]
ar
e
well
s
u
ited
f
o
r
th
is
p
u
r
p
o
s
e
[
1
8
]
.
Yet,
th
ese
m
o
d
els
alo
n
e
ar
e
u
n
ab
le
to
ca
p
tu
r
e
s
tr
u
ctu
r
al
ch
an
g
es
o
r
r
eg
im
e
s
h
if
ts
th
at
f
r
eq
u
e
n
tly
ac
co
m
p
an
y
c
r
is
es.
T
h
e
Ma
r
k
o
v
-
s
witch
in
g
(
MS)
m
o
d
el
p
r
o
p
o
s
ed
b
y
Ham
ilto
n
[
1
9
]
p
r
o
v
id
es
an
alt
er
n
ativ
e
b
y
allo
win
g
th
e
s
y
s
tem
to
s
witch
p
r
o
b
ab
ilis
tically
b
etwe
en
s
tab
le
an
d
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ad
v
Ap
p
l Sci
I
SS
N:
2252
-
8
8
1
4
Ma
r
ko
v
s
w
itch
in
g
a
n
d
n
o
is
e
-
to
-
s
ig
n
a
l ra
tio
a
p
p
r
o
a
c
h
fo
r
ea
r
ly
d
etec
tio
n
o
f c
u
r
r
en
cy
cris
es (
S
u
g
iya
n
t
o
)
43
cr
is
is
s
tates
[
2
0
]
,
[
2
1
]
.
Hy
b
r
id
v
er
s
io
n
s
s
u
ch
as
Ma
r
k
o
v
-
s
witch
in
g
-
g
en
e
r
alize
d
au
to
r
e
g
r
ess
iv
e
co
n
d
itio
n
al
h
eter
o
s
k
ed
asti
city
(
MS
-
GAR
C
H
)
an
d
Ma
r
k
o
v
-
s
witch
in
g
d
y
n
am
ic
co
n
d
itio
n
al
co
r
r
e
latio
n
g
en
er
alize
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au
to
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eg
r
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iv
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co
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d
itio
n
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h
et
er
o
s
k
ed
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city
(
MS
-
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C
-
GA
R
C
H
)
ex
ten
d
th
is
f
lex
ib
ilit
y
,
o
f
f
er
in
g
b
etter
to
o
ls
f
o
r
an
al
y
zin
g
n
o
n
lin
ea
r
f
i
n
an
c
ial
d
y
n
am
ics
[
2
2
]
,
[
2
3
]
.
I
n
r
ec
e
n
t
y
ea
r
s
,
s
ev
er
al
s
tu
d
ies
h
av
e
em
p
l
o
y
ed
s
u
ch
h
y
b
r
id
m
o
d
els
t
o
ex
a
m
in
e
f
in
an
cial
v
u
ln
er
ab
i
lity
in
I
n
d
o
n
esia
a
n
d
o
t
h
er
Asi
an
ec
o
n
o
m
ies
[
2
4
]
–
[
2
9
]
.
No
n
eth
eless
,
o
n
ly
a
f
ew
c
o
m
b
in
es
NSR
-
b
ased
in
d
icato
r
s
elec
tio
n
with
r
eg
im
e
-
s
witch
in
g
v
o
latilit
y
f
r
am
ewo
r
k
s
.
Ad
d
r
ess
in
g
th
is
g
ap
,
th
e
p
r
esen
t
s
tu
d
y
in
te
g
r
ates
b
o
th
ap
p
r
o
ac
h
es
to
co
n
s
tr
u
ct
an
ea
r
ly
-
war
n
in
g
s
y
s
tem
f
o
r
d
etec
tin
g
p
o
t
en
tial
cu
r
r
en
cy
an
d
f
in
an
cial
cr
is
es
in
I
n
d
o
n
esia.
T
h
e
o
b
jectiv
e
is
to
o
f
f
er
a
m
o
r
e
ad
ap
tiv
e
,
d
ata
-
d
r
i
v
en
f
r
am
ewo
r
k
th
at
ca
n
s
u
p
p
o
r
t m
ac
r
o
p
r
u
d
en
tial
p
o
lic
y
d
esig
n
an
d
en
h
an
ce
th
e
c
o
u
n
tr
y
’
s
f
in
an
cial
s
tab
ilit
y
m
o
n
ito
r
in
g
[
3
0
]
–
[
3
3
]
.
2.
RE
S
E
ARCH
M
E
T
H
O
D
T
h
is
s
tu
d
y
s
elec
ts
1
5
m
ac
r
o
ec
o
n
o
m
ic
in
d
icato
r
s
as p
o
ten
tial
cr
is
is
s
ig
n
als b
ased
o
n
th
eir
lo
west NS
R
v
alu
es.
Mo
n
th
l
y
d
ata
f
r
o
m
J
a
n
u
ar
y
1
9
9
0
-
Ap
r
il
2
0
2
c
o
v
er
i
n
g
tr
a
d
e,
r
eser
v
es,
in
ter
est
r
at
es,
ex
ch
an
g
e
r
ates,
m
o
n
ey
s
u
p
p
ly
,
s
to
ck
p
r
ic
es,
o
u
tp
u
t,
an
d
d
o
m
esti
c
cr
e
d
it
p
er
g
r
o
s
s
d
o
m
esti
c
p
r
o
d
u
ct
(
GDP
)
wer
e
o
b
tain
e
d
f
r
o
m
I
n
ter
n
atio
n
al
Fin
an
cial
S
tatis
tic
s
(
I
FS
)
an
d
B
an
k
I
n
d
o
n
esia
(
B
I
)
.
I
n
d
icato
r
s
elec
tio
n
was
b
ased
o
n
ea
c
h
v
ar
iab
le’
s
ab
ilit
y
to
d
etec
t
c
r
is
es
u
s
in
g
th
e
ex
ch
an
g
e
m
ar
k
et
p
r
ess
u
re
(
E
MP)
in
d
ex
,
as
s
h
o
wn
in
(
1
)
,
ca
lcu
lated
as th
e
weig
h
ted
a
v
e
r
ag
e
o
f
ex
ch
a
n
g
e
r
ate
a
n
d
r
ese
r
v
e
ch
an
g
es
[
1
3
]
,
[
3
4
]
–
[
3
6
]
.
=
(
−
−
1
−
1
)
−
(
)
(
−
−
1
−
1
)
(
1
)
W
h
e
r
e
i
s
t
h
e
r
u
p
ia
h
e
x
ch
a
n
g
e
r
a
t
e
a
g
a
in
s
t
t
h
e
U
S
d
o
l
l
a
r
in
m
o
n
t
h
t
,
i
s
t
h
e
f
o
r
e
ig
n
e
x
ch
an
g
e
r
e
s
e
r
v
e
in
m
o
n
th
t
,
i
s
t
h
e
s
t
a
n
d
a
r
d
d
ev
i
a
t
i
o
n
o
f
t
h
e
r
u
p
i
a
h
e
x
ch
a
n
g
e
r
a
t
e
a
g
a
in
s
t
t
h
e
U
S
d
o
l
l
ar
,
d
an
i
s
t
h
e
s
t
a
n
d
ar
d
d
e
v
ia
t
i
o
n
o
f
f
o
r
e
i
g
n
e
x
c
h
an
g
e
r
e
s
er
v
e
s
.
T
h
e
t
h
r
e
s
h
o
ld
v
a
l
u
e
r
ep
r
e
s
en
t
i
n
g
cr
i
s
i
s
c
o
n
d
i
t
i
o
n
s
i
s
c
a
l
c
u
l
a
t
ed
u
s
i
n
g
(
2
)
.
=
̅
+
(
2
)
W
h
er
e
is
s
et
at
1
.
5
; 2
; 2
.
5
;
o
r
3
.
B
ased
o
n
th
is
th
r
esh
o
l
d
,
cr
i
s
is
p
er
io
d
s
ar
e
id
en
tifie
d
th
r
o
u
g
h
(
3
)
.
=
{
1
,
E
MP
> b
0
,
if E
MP
≤
b
(
3
)
W
h
er
e
1
d
en
o
tes a
cr
is
is
,
an
d
0
d
en
o
tes n
o
cr
is
is
[
1
3
]
,
[
3
6
]
,
[
3
7
]
.
Ma
cr
o
ec
o
n
o
m
ic
in
d
icato
r
s
wer
e
tr
an
s
f
o
r
m
e
d
to
im
p
r
o
v
e
th
eir
s
en
s
itiv
ity
to
cr
is
e
s
.
Seaso
n
al
v
ar
iab
les
wer
e
c
o
n
v
e
r
ted
i
n
to
an
n
u
al
g
r
o
wth
r
ates,
wh
ile
n
o
n
-
s
ea
s
o
n
al
v
ar
ia
b
les
wer
e
d
if
f
e
r
en
ce
d
.
Ad
d
itio
n
ally
,
th
e
len
d
in
g
-
to
-
d
ep
o
s
it
r
ate
r
atio
was
lo
g
-
tr
an
s
f
o
r
m
ed
,
an
d
th
e
r
ea
l
ex
ch
a
n
g
e
r
ate
was
s
p
li
t
in
to
tr
en
d
an
d
cy
cle
co
m
p
o
n
en
ts
u
s
in
g
th
e
Ho
d
r
ick
–
Pre
s
co
tt f
ilter
as (
4
)
[
1
5
]
,
[
1
8
]
,
[
3
8
]
–
[
4
0
]
.
=
∑
(
−
)
2
=
1
+
∑
[
(
+
1
−
)
−
(
−
−
1
)
]
2
−
1
=
2
(
4
)
W
h
er
e
is
th
e
tim
e
s
er
ie
s
o
b
s
er
v
atio
n
at
t
,
is
th
e
tr
en
d
co
m
p
o
n
en
t
at
t
,
is
th
e
p
en
alty
ter
m
,
s
et
to
1
2
9
,
6
0
0
f
o
r
m
o
n
th
ly
d
ata.
Sig
n
al
ef
f
ec
tiv
en
ess
was
ev
al
u
ated
u
s
in
g
a
2
4
-
m
o
n
t
h
s
ig
n
a
l
h
o
r
izo
n
.
I
f
a
cr
is
is
s
ig
n
al
o
cc
u
r
s
an
d
a
cr
is
is
f
o
llo
ws
with
in
2
4
m
o
n
t
h
s
,
it
is
class
if
ied
as
a
co
r
r
ec
t
s
ig
n
al
(
A)
;
if
n
o
cr
is
is
f
o
llo
ws,
as
a
f
alse
s
ig
n
al
(
B
)
;
if
th
er
e
is
n
o
s
ig
n
al
an
d
n
o
cr
is
is
,
as
(
D)
;
an
d
if
th
er
e
is
n
o
s
ig
n
al
b
u
t
a
cr
is
is
o
cc
u
r
s
,
as
(
C
)
.
T
h
e
s
ig
n
al
m
atr
ix
is
p
r
esen
ted
in
T
a
b
le
1
,
an
d
th
e
NSR
v
alu
e
is
ca
lcu
lat
ed
as
(
5
)
[
1
5
]
.
=
/
(
+
)
/
(
+
)
(
5
)
T
ab
le
1
.
Sig
n
al
in
d
icato
r
m
at
r
ix
C
r
i
ses
o
c
c
u
r
r
e
d
N
o
c
r
i
ses
o
c
c
u
r
r
e
d
S
i
g
n
a
l
A
B
N
o
s
i
g
n
a
l
C
D
T
h
e
th
r
ee
in
d
icato
r
s
with
t
h
e
l
o
west
NSR
v
alu
es
wer
e
s
elec
ted
f
o
r
f
u
r
th
e
r
m
o
d
ellin
g
.
T
h
e
d
ata
wer
e
d
iv
id
ed
in
t
o
in
-
s
am
p
le
(
J
an
u
ar
y
1
9
9
0
-
Ap
r
il
2
0
2
4
)
an
d
o
u
t
-
of
-
s
am
p
le
(
Ma
y
2
0
2
4
-
A
p
r
il
2
0
2
5
)
p
er
io
d
s
.
S
tatio
n
ar
ity
test
in
g
was p
er
f
o
r
m
ed
u
s
in
g
th
e
a
u
g
m
e
n
ted
Dic
k
ey
-
Fu
ller
(
ADF)
test
,
an
d
if
n
o
n
-
s
tatio
n
ar
ity
was
d
etec
ted
,
a
lo
g
-
r
etu
r
n
tr
an
s
f
o
r
m
atio
n
was a
p
p
lied
in
(
6
)
[
1
8
]
,
[
3
8
]
,
[
4
1
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
5
2
-
8
8
1
4
I
n
t J Ad
v
Ap
p
l Sci
,
Vo
l.
15
,
No
.
1
,
Ma
r
c
h
20
26
:
42
-
54
44
=
−
−
1
(
6
)
Gr
an
g
er
ca
u
s
ality
test
in
g
was
co
n
d
u
cted
to
ex
am
in
e
r
elatio
n
s
h
ip
s
am
o
n
g
th
e
in
d
icato
r
s
[
1
8
]
,
[
4
2
]
,
[
4
3
]
.
T
h
e
o
p
tim
al
lag
len
g
th
was d
eter
m
in
ed
u
s
in
g
Sch
war
z’
s
C
r
iter
io
n
(
SC
)
as in
(
7
)
.
=
−
2
ln
(
)
+
(
)
(
7
)
W
h
e
r
e
T
i
s
t
h
e
n
u
m
b
e
r
o
f
o
b
s
e
r
v
a
t
i
o
n
s
,
k
i
s
t
h
e
n
u
m
b
e
r
o
f
p
a
r
a
m
e
t
e
r
s
e
s
t
i
m
a
t
e
d
i
n
t
h
e
m
o
d
e
l
,
a
n
d
L
i
s
t
h
e
m
a
x
i
m
u
m
l
i
k
e
l
i
h
o
o
d
v
a
l
u
e
o
f
t
h
e
m
o
d
e
l
.
T
h
e
G
r
a
n
g
e
r
c
a
u
s
a
l
i
t
y
t
e
s
t
s
t
a
t
i
s
t
i
c
i
s
f
o
r
m
u
l
a
t
e
d
as
i
n
(
8
)
[
1
8
]
,
[
4
3
]
,
[
4
4
]
.
=
(
−
)
/
/
(
−
)
(
8
)
W
h
er
e
=
=
∑
(
−
̂
)
2
=
1
,
is
th
e
r
esid
u
al
s
u
m
o
f
s
q
u
a
r
es
f
r
o
m
t
h
e
u
n
r
estricte
d
r
eg
r
ess
io
n
,
an
d
is
f
r
o
m
th
e
r
e
s
tr
icted
r
eg
r
ess
io
n
.
I
f
n
o
ca
u
s
al
r
elatio
n
s
h
ip
was
f
o
u
n
d
,
u
n
i
v
ar
iate
au
to
r
eg
r
e
s
s
iv
e
m
o
v
in
g
av
er
ag
e
(
AR
MA
)
(
p
,
q
)
m
o
d
ellin
g
was
p
er
f
o
r
m
e
d
.
T
h
e
o
r
d
er
s
p
an
d
q
wer
e
d
eter
m
i
n
ed
f
r
o
m
th
e
au
t
o
co
r
r
elatio
n
f
u
n
ctio
n
(
AC
F
)
an
d
p
ar
tial
au
to
co
r
r
elatio
n
f
u
n
cti
o
n
(
PAC
F
)
p
lo
ts
,
an
d
th
e
b
est
m
o
d
el
was
s
elec
ted
b
ased
o
n
th
e
Ak
aik
e
I
n
f
o
r
m
atio
n
C
r
iter
io
n
(
AI
C
)
a
s
in
(
9
)
[
1
8
]
.
=
−
2
ln
+
2
(
9
)
W
h
er
e
d
en
o
tes th
e
n
u
m
b
e
r
o
f
v
ar
ia
b
les
,
an
d
d
e
n
o
tes th
e
m
ax
im
u
m
lik
elih
o
o
d
v
alu
e
o
f
th
e
m
o
d
el.
T
h
e
AR
MA
m
o
d
el
was
v
alid
ated
u
s
in
g
th
r
ee
d
iag
n
o
s
tic
test
s
.
A
u
to
co
r
r
elatio
n
with
th
e
L
ju
n
g
-
B
o
x
test
[
4
1
]
,
h
eter
o
s
k
ed
asti
city
with
th
e
L
ag
r
an
g
e
m
u
ltip
lier
test
[
1
6
]
–
[
1
8
]
,
an
d
n
o
r
m
ality
with
th
e
Ko
lm
o
g
o
r
o
v
-
Sm
ir
n
o
v
test
[
2
8
]
.
I
f
h
eter
o
s
k
ed
asti
city
was
d
etec
ted
,
A
R
C
H(
m
)
an
d
GARC
H
(m,
s
)
m
o
d
els
wer
e
ap
p
lied
in
(
10
)
a
n
d
(
1
1
)
[
1
6
]
–
[
1
8
]
.
2
=
0
+
∑
=
1
−
2
(
10
)
2
=
0
+
∑
=
1
−
2
1
+
∑
=
1
−
2
(1
1
)
T
o
ca
p
tu
r
e
ec
o
n
o
m
ic
r
eg
im
e
ch
an
g
es,
th
e
MS
m
o
d
el
was
ap
p
lied
.
C
o
m
b
in
in
g
MS
with
GAR
C
H
p
r
o
d
u
ce
d
th
e
MS
-
GARC
H
m
o
d
el,
wh
ich
was
esti
m
ated
u
s
in
g
m
ax
im
u
m
lik
elih
o
o
d
esti
m
atio
n
(
ML
E
)
as
in
(
1
2
)
[
1
7
]
.
T
h
is
ap
p
r
o
ac
h
a
llo
ws
v
o
latilit
y
to
s
h
if
t
b
etwe
en
r
eg
im
es,
p
r
o
v
id
in
g
a
clea
r
er
id
en
tific
atio
n
o
f
p
er
io
d
s
th
at
m
a
y
s
ig
n
al
th
e
o
n
s
et
o
f
a
cu
r
r
e
n
cy
cr
is
is
.
,
2
=
0
,
+
∑
,
=
1
−
2
+
∑
,
=
1
−
2
(1
2
)
T
h
e
p
r
o
b
ab
ilit
y
o
f
a
cr
is
is
at
tim
e
t
was c
alcu
lated
u
s
in
g
s
m
o
o
th
ed
p
r
o
b
a
b
ilit
y
as in
(
1
3
)
[
2
4
]
.
(
=
|
)
=
∑
(
+
1
=
|
)
×
(
=
|
+
1
=
,
)
=
1
(1
3
)
Fo
r
ec
asti
n
g
o
f
c
r
is
is
p
r
o
b
ab
il
ity
f
o
r
t
h
e
p
er
i
o
d
Ma
y
2
0
2
5
-
Ap
r
il
2
0
2
6
was
p
er
f
o
r
m
ed
u
s
in
g
(1
4
)
[
4
5
]
–
[
4
9
]
.
(
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RE
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1
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3
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2
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3
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m
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3
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th
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is
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(
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a
s
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p
r
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in
(
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.
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is
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M
A
(
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1
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,
as
p
r
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ted
in
(
1
6
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.
Fin
ally
,
f
o
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(
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s
s
h
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wn
in
(
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7
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.
R
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o
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to
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r
r
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(
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g
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o
x
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,
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n
d
h
eter
o
s
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ed
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city
(
L
ag
r
an
g
e
m
u
ltip
lier
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.
T
h
e
f
ir
s
t
two
test
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s
h
o
wed
p
-
v
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>0
.
0
1
,
in
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n
o
r
m
al
ity
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o
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h
e
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ag
r
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m
u
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v
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s
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ir
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(
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(
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c)
Fig
u
r
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1
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T
r
an
s
f
o
r
m
ati
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ter
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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v
Ap
p
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2252
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4
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o
f c
u
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cris
es (
S
u
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iya
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t
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)
47
(
a)
(
b
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(
c)
Fig
u
r
e
2
.
T
im
e
s
er
ies p
lo
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o
f
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a)
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ea
l d
e
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o
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ter
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h
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eser
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d
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x
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h
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r
ate
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
2
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2
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8
8
1
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I
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v
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l.
15
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1
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Ma
r
c
h
20
26
:
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T
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le
3
.
Gr
a
n
g
er
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u
s
ality
test
r
esu
lts
I
n
d
i
c
a
t
o
r
r
e
l
a
t
i
o
n
s
h
i
p
P
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v
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l
u
e
R
e
a
l
d
e
p
o
si
t
i
n
t
e
r
e
s
t
r
a
t
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~
M
2
p
e
r
f
o
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e
i
g
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e
x
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h
a
n
g
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v
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s
0
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6
0
7
9
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2
p
e
r
f
o
r
e
i
g
n
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x
c
h
a
n
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s~
r
e
a
l
d
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o
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t
i
n
t
e
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e
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t
r
a
t
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8
R
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a
l
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t
i
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t
e
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s
t
r
a
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a
l
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h
a
n
g
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r
a
t
e
0
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5
4
9
9
R
e
a
l
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x
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h
a
n
g
e
r
a
t
e~
r
e
a
l
d
e
p
o
si
t
i
n
t
e
r
e
st
r
a
t
e
0
.
8
3
4
3
M
2
p
e
r
f
o
r
e
i
g
n
e
x
c
h
a
n
g
e
r
e
serv
e
s
~
r
e
a
l
e
x
c
h
a
n
g
e
r
a
t
e
0
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6
2
8
9
R
e
a
l
e
x
c
h
a
n
g
e
r
a
t
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~
M
2
p
e
r
f
o
r
e
i
g
n
e
x
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h
a
n
g
e
r
e
s
e
r
v
e
s
0
.
0
9
4
2
3
.
5
.
ARMACH
a
nd
G
ARM
ACH
m
o
dels
Sin
ce
th
e
m
o
d
els
ar
e
b
ased
o
n
AR
MA
,
th
e
co
r
r
esp
o
n
d
in
g
v
o
latilit
y
m
o
d
els
ar
e
AR
MA
-
GAR
C
H
o
r
AR
MA
-
A
R
C
H.
Fo
r
th
e
r
ea
l
d
ep
o
s
it
in
ter
est
r
ate,
th
e
b
est
m
o
d
el
ad
d
r
ess
in
g
h
eter
o
s
k
ed
asti
city
is
AR
MA
-
A
R
C
H
(
1
)
,
with
its
v
ar
ian
ce
eq
u
atio
n
s
h
o
wn
in
(
1
8
).
Fo
r
th
e
M2
p
e
r
f
o
r
eig
n
ex
ch
an
g
e
r
eser
v
es
in
d
icato
r
,
th
e
b
est m
o
d
el
ad
d
r
ess
in
g
h
eter
o
s
k
ed
asti
city
in
th
e
AR
MA
(
2
,
1
)
s
tr
u
ctu
r
e
is
GA
R
C
H
(
1
,
1
)
,
with
its
v
ar
ian
ce
e
q
u
atio
n
s
h
o
wn
in
(
1
9
)
.
Fo
r
th
e
r
ea
l
ex
ch
a
n
g
e
r
ate
in
d
icato
r
,
th
e
b
est
m
o
d
el
a
d
d
r
ess
in
g
h
eter
o
s
k
ed
asti
city
in
th
e
AR
MA
(
2
,
2
)
s
tr
u
ctu
r
e
is
GARC
H
(
1
,
1
)
,
with
its
v
ar
ian
ce
eq
u
atio
n
s
h
o
wn
in
(
20
)
.
Diag
n
o
s
tic
test
s
o
n
th
e
b
est
v
o
latilit
y
m
o
d
els
f
o
r
all
t
h
r
ee
in
d
icato
r
s
s
h
o
wed
p
-
v
a
lu
es
>0
.
0
1
in
th
e
Ko
lm
o
g
o
r
o
v
–
Sm
ir
n
o
v
,
L
ju
n
g
–
B
o
x
,
a
n
d
L
ag
r
an
g
e
m
u
ltip
lier
test
s
,
co
n
f
ir
m
in
g
th
at
t
h
e
r
e
s
id
u
als
ar
e
n
o
r
m
al
,
u
n
co
r
r
elate
d
,
an
d
f
r
ee
f
r
o
m
h
e
ter
o
s
k
ed
asti
city
,
th
u
s
v
alid
atin
g
th
e
m
o
d
els.
2
=
0
.
0043
+
2
.
504
−
1
2
(1
8
)
2
=
0
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+
0
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−
1
2
+
0
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6537
−
1
2
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9
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2
=
0
.
00008174
+
0
.
7261
−
1
2
+
0
.
4648
−
1
2
(
20
)
3
.
6
.
MS
-
ARMACH
a
nd
M
S
-
G
ARMACH
m
o
dels
T
h
e
s
ilh
o
u
ette
test
in
d
icate
d
two
o
p
tim
al
clu
s
ter
s
f
o
r
ea
ch
in
d
icato
r
.
T
h
u
s
,
th
e
ap
p
r
o
p
r
iate
m
o
d
els
ar
e
MS
-
AR
MA
-
A
R
C
H
(
2
,
1
)
f
o
r
th
e
r
ea
l
d
ep
o
s
it
in
ter
est
r
ate
an
d
MS
-
GAR
C
H
(
2
,
1
,
1
)
f
o
r
b
o
th
M2
p
er
r
eser
v
es a
n
d
r
ea
l
e
x
ch
an
g
e
r
at
e,
with
th
eir
v
ar
ia
n
ce
eq
u
atio
n
s
s
h
o
wn
in
(
2
1
)
-
(2
3
).
1
,
2
=
{
0
.
4953
+
0
.
0000
−
1
2
,
1
24
.
1379
+
0
.
0001
−
1
2
,
2
(2
1
)
2
,
2
=
{
0
.
2654
+
0
.
0000
−
1
2
+
0
.
5106
−
1
2
,
1
0
.
4381
+
0
.
0001
−
1
2
+
0
.
8886
−
1
2
,
2
(2
2
)
3
,
2
=
{
0
.
0013
+
0
.
0053
−
1
2
+
0
.
9883
−
1
2
,
1
3
.
6903
+
0
.
7299
−
1
2
+
0
.
0027
−
1
2
,
2
(2
3
)
W
h
er
e
1
,
2
r
ep
r
esen
ts
th
e
v
ar
ian
c
e
eq
u
atio
n
o
f
t
h
e
MS
-
AR
MA
C
H
(
2
,
1
)
m
o
d
el
f
o
r
t
h
e
r
ea
l
d
ep
o
s
it
in
ter
est
r
ate
in
d
icato
r
,
2
,
2
r
e
p
r
esen
ts
th
e
v
ar
ian
ce
eq
u
atio
n
o
f
th
e
MS
-
GARMA
C
H
(
2
,
1
,
1
)
m
o
d
el
f
o
r
th
e
M2
p
e
r
f
o
r
eig
n
e
x
ch
an
g
e
r
eser
v
es
in
d
icato
r
,
an
d
3
,
2
r
ep
r
esen
ts
th
e
v
ar
ian
ce
eq
u
atio
n
o
f
t
h
e
MS
-
GARMA
C
H
(
2
,
1
,
1
)
m
o
d
el
f
o
r
th
e
r
ea
l e
x
ch
an
g
e
r
ate
in
d
icato
r
.
State
1
an
d
State
2
co
r
r
esp
o
n
d
to
th
e
lo
w
-
v
o
latilit
y
an
d
h
ig
h
-
v
o
latilit
y
s
tates,
r
esp
ec
ti
v
ely
.
T
h
e
tr
an
s
itio
n
p
r
o
b
ab
ilit
y
m
atr
ices
f
o
r
th
e
r
ea
l
d
e
p
o
s
it
in
ter
est
r
a
te,
th
e
M2
p
e
r
f
o
r
eig
n
ex
ch
a
n
g
e
r
eser
v
es,
an
d
th
e
r
ea
l
ex
ch
an
g
e
r
ate
in
d
icato
r
s
ar
e
p
r
esen
ted
in
m
at
r
ices
1
,
2
,
an
d
3
,
r
esp
ec
tiv
ely
.
T
h
e
tr
an
s
itio
n
m
atr
ix
r
esu
lts
in
d
icate
th
at
all
th
r
ee
in
d
icato
r
s
p
r
e
d
o
m
in
a
n
tly
r
em
ain
in
lo
w
-
v
o
latilit
y
(
n
o
-
cr
is
is
)
r
eg
im
es.
Fo
r
1
,
s
tab
ilit
y
p
er
s
is
ts
with
a
0
.
9
7
8
1
p
r
o
b
ab
ilit
y
,
w
h
ile
s
h
if
ts
to
h
ig
h
v
o
latilit
y
ar
e
r
ar
e
(
0
.
0
2
1
9
)
.
I
n
2
,
th
e
s
tab
ilit
y
pr
o
b
a
b
ilit
y
is
0
.
8
4
4
1
with
a
0
.
1
5
5
9
c
h
an
ce
o
f
r
is
in
g
v
o
latilit
y
,
an
d
in
3
,
ca
lm
co
n
d
itio
n
s
p
er
s
is
t
w
ith
0
.
8
6
6
0
,
wh
ile
v
o
latilit
y
in
cr
ea
s
es o
cc
u
r
with
0
.
1
3
4
0
.
Ov
er
all,
tr
an
s
iti
o
n
s
ten
d
to
r
ev
er
t
q
u
ick
ly
t
o
s
tab
le
r
eg
im
es.
1
=
(
0
.
9873
0
.
0127
0
.
1772
0
.
8228
)
,
2
=
(
0
.
8441
0
.
1559
0
.
9994
0
.
0006
)
,
3
=
(
0
.
8660
0
.
1340
0
.
8650
0
.
1350
)
3
.
7
.
Det
er
m
ini
ng
cr
is
is
bo
un
da
ries
Usi
n
g
th
e
c
o
m
b
in
e
d
v
o
latilit
y
an
d
MS
m
o
d
el,
s
m
o
o
t
h
ed
p
r
o
b
ab
ilit
y
v
alu
es
wer
e
g
e
n
er
ated
t
o
d
eter
m
in
e
cr
is
is
th
r
esh
o
ld
s
.
C
r
is
is
p
er
io
d
s
wer
e
id
en
tifie
d
f
r
o
m
f
lu
ctu
a
tio
n
s
in
th
ese
p
r
o
b
ab
ilit
ies,
as
s
h
o
wn
in
Fig
u
r
e
3
.
T
h
e
lo
west
s
m
o
o
t
h
ed
p
r
o
b
ab
ilit
y
v
alu
es
in
Fig
u
r
e
3
,
wh
ich
s
h
o
w
in
s
tab
ilit
y
d
u
r
in
g
th
e
f
in
an
cial
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ad
v
Ap
p
l Sci
I
SS
N:
2252
-
8
8
1
4
Ma
r
ko
v
s
w
itch
in
g
a
n
d
n
o
is
e
-
to
-
s
ig
n
a
l ra
tio
a
p
p
r
o
a
c
h
fo
r
ea
r
ly
d
etec
tio
n
o
f c
u
r
r
en
cy
cris
es (
S
u
g
iya
n
t
o
)
49
cr
is
is
p
er
io
d
in
I
n
d
o
n
esia
,
o
b
t
ain
ed
f
r
o
m
t
h
e
E
MP
ca
lcu
lati
o
n
,
a
r
e
s
u
m
m
a
r
ized
in
T
ab
le
4
.
T
ab
le
4
p
r
esen
ts
t
h
e
s
m
o
o
th
ed
p
r
o
b
ab
ilit
y
th
r
e
s
h
o
ld
s
f
o
r
ea
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4
1
3
4
1
3
4
No
-
c
r
i
s
i
s
0
.
7
7
4
8
5
6
6
1
2
No
-
c
r
i
s
i
s
A
p
r
i
l
2
0
2
5
0
.
1
3
4
1
3
4
1
3
4
No
-
c
r
i
s
i
s
0
.
7
7
4
8
5
7
3
8
2
No
-
c
r
i
s
i
s
3
.
9
.
E
a
rly
det
ec
t
io
n o
f
f
ina
ncia
l c
risi
s
in
I
nd
o
ne
s
ia
T
h
e
th
r
ee
in
d
icato
r
s
wer
e
u
s
e
d
to
f
o
r
ec
ast
s
m
o
o
th
ed
p
r
o
b
a
b
ilit
ies
f
o
r
Ma
y
2
0
2
5
-
Ap
r
il
2
0
2
6
as
an
ea
r
ly
war
n
i
n
g
o
f
p
o
ten
tial
f
in
a
n
cial
cr
is
es,
with
r
esu
lts
s
h
o
w
n
in
T
ab
le
8
.
B
ased
o
n
th
ese
t
h
r
ee
in
d
icato
r
s
,
th
e
s
m
o
o
th
ed
p
r
o
b
ab
ilit
y
v
alu
es
a
r
e
lo
wer
th
an
th
eir
r
esp
ec
tiv
e
th
r
esh
o
ld
s
,
in
d
icatin
g
n
o
cr
is
is
in
t
h
e
p
e
r
io
d
f
r
o
m
Ma
y
2
0
2
5
-
Ap
r
il
2
0
2
6
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ad
v
Ap
p
l Sci
I
SS
N:
2252
-
8
8
1
4
Ma
r
ko
v
s
w
itch
in
g
a
n
d
n
o
is
e
-
to
-
s
ig
n
a
l ra
tio
a
p
p
r
o
a
c
h
fo
r
ea
r
ly
d
etec
tio
n
o
f c
u
r
r
en
cy
cris
es (
S
u
g
iya
n
t
o
)
51
T
ab
le
8
.
Fo
r
ec
asted
s
m
o
o
th
ed
p
r
o
b
a
b
ilit
y
v
alu
es f
o
r
th
e
r
ea
l
d
ep
o
s
it in
ter
est r
ate
,
M2
p
e
r
f
o
r
eig
n
e
x
ch
an
g
e
r
eser
v
es,
an
d
r
ea
l e
x
ch
an
g
e
r
a
te
(
Ma
y
2
0
2
5
-
Ap
r
il 2
0
2
6
)
P
e
r
i
o
d
F
o
r
e
c
a
st
(
r
e
a
l
d
e
p
o
s
i
t
i
n
t
e
r
e
s
t
r
a
t
e
)
F
o
r
e
c
a
st
(
M
2
p
e
r
f
o
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g
n
e
x
c
h
a
n
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e
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ser
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s
)
F
o
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c
a
st
(
r
e
a
l
e
x
c
h
a
n
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e
)
S
t
a
t
u
s
M
a
y
2
0
2
5
0
.
0
2
0
6
7
7
5
4
6
0
.
1
4
8
9
8
1
7
4
4
0
.
1
5
2
8
2
2
4
7
7
No
-
c
r
i
s
i
s
Ju
n
e
2
0
2
5
0
.
0
2
0
6
7
6
2
6
9
0
.
1
5
1
3
0
8
3
7
4
0
.
1
5
2
9
1
2
6
7
4
No
-
c
r
i
s
i
s
Ju
l
y
2
0
2
5
0
.
0
2
0
6
7
6
2
8
3
0
.
1
5
0
8
9
5
9
7
0
.
1
5
2
9
0
9
7
7
No
-
c
r
i
s
i
s
A
u
g
u
st
2
0
2
5
0
.
0
2
0
6
7
6
2
8
2
0
.
1
5
0
9
6
9
0
6
5
0
.
1
5
2
9
0
9
8
6
3
No
-
c
r
i
s
i
s
S
e
p
t
e
m
b
e
r
2
0
2
5
0
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2
0
6
7
6
2
8
2
0
.
1
5
0
9
5
6
1
0
4
0
.
1
5
2
9
0
9
8
6
No
-
c
r
i
s
i
s
O
c
t
o
b
e
r
2
0
2
5
0
.
0
2
0
6
7
6
2
8
2
0
.
1
5
0
9
5
8
3
9
7
0
.
1
5
2
9
0
9
8
6
No
-
c
r
i
s
i
s
N
o
v
e
mb
e
r
2
0
2
5
0
.
0
2
0
6
7
6
2
8
2
0
.
1
5
0
9
5
7
9
8
6
0
.
1
5
2
9
0
9
8
6
No
-
c
r
i
s
i
s
D
e
c
e
m
b
e
r
2
0
2
5
0
.
0
2
0
6
7
6
2
8
2
0
.
1
5
0
9
5
8
0
5
4
0
.
1
5
2
9
0
9
8
6
No
-
c
r
i
s
i
s
Jan
u
a
r
y
2
0
2
6
0
.
0
2
0
6
7
6
2
8
2
0
.
1
5
0
9
5
8
0
3
8
0
.
1
5
2
9
0
9
8
6
No
-
c
r
i
s
i
s
F
e
b
r
u
a
r
y
2
0
2
6
0
.
0
2
0
6
7
6
2
8
2
0
.
1
5
0
9
5
8
0
3
6
0
.
1
5
2
9
0
9
8
6
No
-
c
r
i
s
i
s
M
a
r
c
h
2
0
2
6
0
.
0
2
0
6
7
6
2
8
2
0
.
1
5
0
9
5
8
0
3
2
0
.
1
5
2
9
0
9
8
6
No
-
c
r
i
s
i
s
A
p
r
i
l
2
0
2
6
0
.
0
2
0
6
7
6
2
8
2
0
.
1
5
0
9
5
8
0
2
8
0
.
1
5
2
9
0
9
8
6
No
-
c
r
i
s
i
s
4.
DIS
CU
SS
I
O
N
T
h
e
ce
n
tr
al
b
an
k
m
o
n
ito
r
s
th
e
m
o
v
em
e
n
t
o
f
a
n
u
m
b
er
o
f
s
elec
ted
in
d
icato
r
s
in
r
ea
l
tim
e
f
o
r
ap
p
licatio
n
in
th
e
MS
-
AR
MA
-
GARC
H
h
y
b
r
id
m
o
d
el.
A
cr
is
is
war
n
in
g
is
is
s
u
ed
wh
e
n
ev
er
th
e
s
m
o
o
th
e
d
p
r
o
b
a
b
ilit
y
ex
ce
e
d
s
a
ce
r
tain
th
r
esh
o
ld
f
o
r
s
ev
er
al
c
o
n
s
ec
u
tiv
e
m
o
n
t
h
s
.
T
h
e
m
o
s
t
s
ig
n
if
i
ca
n
t
ev
en
ts
,
wh
en
d
etec
ted
with
in
a
m
o
n
t
h
,
p
r
o
m
p
t
s
tak
eh
o
ld
er
s
to
p
r
o
m
p
tly
id
en
tify
th
eir
ca
u
s
es
to
av
o
id
a
cr
is
is
.
T
h
is
s
tu
d
y
also
co
r
r
o
b
o
r
ates
th
e
f
in
d
i
n
g
s
o
f
Su
g
iy
an
t
o
et
a
l
.
[
2
4
]
a
n
d
D
u
et
a
l.
[
2
5
]
an
d
co
m
p
le
m
en
ts
th
em
b
y
a
p
p
ly
in
g
in
d
icato
r
s
elec
tio
n
th
r
o
u
g
h
t
h
e
NR
S
to
th
e
MS
-
A
R
MA
-
GA
R
C
H
h
y
b
r
id
m
o
d
el.
Fu
t
u
r
e
r
ese
ar
ch
co
u
ld
u
s
e
th
e
C
u
r
r
en
cy
C
r
is
is
I
n
d
ex
(
C
C
I
)
o
r
Ma
r
k
et
Pre
s
s
u
r
e
I
n
d
ex
(
MPI
)
to
d
eter
m
in
e
cr
is
is
p
er
io
d
s
.
T
h
e
r
esu
lts
o
f
th
is
s
tu
d
y
p
r
o
v
id
e
a
p
r
ac
tical
f
r
a
m
ewo
r
k
a
n
d
f
o
cu
s
o
n
I
n
d
o
n
e
s
ia,
s
o
th
is
m
eth
o
d
o
l
o
g
y
ca
n
b
e
ex
ten
d
ed
to
o
th
er
d
ev
elo
p
in
g
co
u
n
tr
ies f
ac
in
g
s
i
m
ilar
v
o
latilit
y
p
atter
n
s
.
5.
CO
NCLU
SI
O
N
T
h
e
s
tag
es
in
th
is
r
esear
ch
ar
e
alwa
y
s
b
ased
o
n
d
ata
ch
ar
ac
t
er
is
tics
an
d
th
e
s
elec
tio
n
o
f
m
o
d
el
s
th
at
m
atch
th
ese
ch
ar
ac
ter
is
tics
,
r
esu
ltin
g
in
a
v
e
r
y
g
o
o
d
E
W
S.
Fo
r
ex
am
p
le,
th
e
d
eter
m
in
atio
n
o
f
p
ast
cr
is
is
p
er
io
d
s
is
ca
r
r
ied
o
u
t
u
s
in
g
t
h
e
E
MP
,
an
d
th
e
d
eter
m
in
ati
o
n
o
f
f
u
tu
r
e
cr
is
es
u
s
in
g
s
m
o
o
th
ed
p
r
o
b
a
b
ilit
ies.
Data
f
lu
ctu
atio
n
s
an
d
r
e
g
im
e
s
h
if
ts
a
r
e
an
aly
ze
d
u
s
in
g
a
h
y
b
r
id
MS
-
GARMAC
H.
T
h
e
s
m
o
o
th
ed
p
r
o
b
ab
ilit
ies
in
d
icate
n
o
s
ig
n
s
o
f
c
r
is
is
r
is
k
d
u
r
in
g
th
e
p
er
io
d
f
r
o
m
Ma
y
2
0
2
5
-
Ap
r
il
2
0
2
6
.
Alth
o
u
g
h
th
is
r
esear
ch
f
r
am
ewo
r
k
f
o
cu
s
es
o
n
I
n
d
o
n
esia,
th
e
m
eth
o
d
o
lo
g
y
c
an
b
e
ex
ten
d
e
d
to
o
th
e
r
d
ev
elo
p
i
n
g
c
o
u
n
tr
ies
f
ac
i
n
g
s
im
ilar
v
o
latilit
y
p
atter
n
s
.
ACK
NO
WL
E
DG
E
M
E
NT
S
W
e
wo
u
ld
lik
e
to
e
x
p
r
ess
o
u
r
g
r
atitu
d
e
to
Un
iv
e
r
s
itas
Seb
elas
Ma
r
et
f
o
r
p
r
o
v
id
i
n
g
f
u
n
d
s
to
ca
r
r
y
o
u
t
th
e
r
esear
ch
.
F
UNDING
I
NF
O
R
M
A
T
I
O
N
T
h
e
R
KAT
o
f
Un
iv
er
s
itas
Seb
elas
Ma
r
et
f
u
n
d
s
th
is
r
esear
ch
f
o
r
th
e
2
0
2
5
Fis
ca
l
Yea
r
t
h
r
o
u
g
h
th
e
R
esear
ch
Sch
em
e
Stre
n
g
th
en
in
g
th
e
C
ap
ac
ity
o
f
th
e
R
esear
ch
Gr
o
u
p
(
PKGR
-
UNS)
B
with
R
esear
ch
Ass
ig
n
m
en
t A
g
r
ee
m
en
t N
u
m
b
er
: 3
7
1
/UN2
7
.
2
2
/PT.
0
1
.
0
3
/
2
0
2
5
.
AUTHO
R
CO
NT
RI
B
UT
I
O
NS ST
A
T
E
M
E
N
T
T
h
is
jo
u
r
n
al
u
s
es
th
e
C
o
n
tr
ib
u
to
r
R
o
les
T
ax
o
n
o
m
y
(
C
R
ed
iT)
to
r
ec
o
g
n
ize
in
d
iv
id
u
al
au
th
o
r
co
n
tr
ib
u
tio
n
s
,
r
ed
u
ce
au
th
o
r
s
h
ip
d
is
p
u
tes,
an
d
f
ac
ilit
ate
co
llab
o
r
atio
n
.
Na
m
e
o
f
Aut
ho
r
C
M
So
Va
Fo
I
R
D
O
E
Vi
Su
P
Fu
Su
g
iy
an
to
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
Mu
h
am
m
ad
B
ay
u
Nir
wan
a
✓
✓
✓
✓
✓
I
s
n
an
d
ar
Slam
et
✓
✓
✓
✓
✓
E
tik
Z
u
k
h
r
o
n
a
h
✓
✓
✓
✓
✓
Sy
if
a’
Sals
ab
ila
Gita
Par
ah
ita
✓
✓
✓
✓
✓
✓
Evaluation Warning : The document was created with Spire.PDF for Python.