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Dynamic Interrelationships between Macroeconomic Indicators, Global Stock
Market, and Commodities Prices and Jakarta Composite Index (JCI)
Pro !dler Man"r"n#
Jos"a Pardede MS$%, MSc%
&omora Sitor"s MSc%
!bstracts
Our paper aims to analyze interrelationship between Jakarta Composite Index and
mcroeconomic indicators, global stock market, and commodities prices. We employ ector
!rror Correction "odel #!C"$ to in%estigate whether dynamic linkages exist between our
research %ariables. We &ind that there is co mo%ement between %ariable in our research.
"oreo%er, 'ow Jones, gold price, and oil prices dominantly a&&ect JCI mo%ement in the long(
run.
JEL Classification) C*+, 1-, 1
Keywords) Cointegration, /tock market, Commodities, "acroeconomy, !C"
'% Introd"ction0cademic community has long been interested in the connection between the
&inancial markets and the economy. he recent decline in global economic acti%ity due to
subprime crisis has again intensi&ied study on the relationship between both &inancial indexes
and macroeconomic per&ormance. 0gainst the background o& e&&icient market hypothesis
which denote that asset prices should &ollow a random walk or at least be undpredictable, the
existence o& predictability pattern o& stock market indexes has been considered &ascinating.
he common approach in the dynamic analysis between &inancial market and
macroeonomy used ector 0uto 2egressi%e #02$ to study the e&&ect o& many
macroeconomic inno%ations in the pricing o& &inancial market assets. "ost o& the studies in
this category mainly assume a priori that the direction o& 3indicator power4 comes &rom the
macroeconomy and goes to the &inancial market.
Our paper &ocuses on the determinant &actors o& Jakarta Composite Index #JCI$. JCI is
a modi&ied capitalization(weighted index o& allstocks listed on the regular board o& the
Indonesia /tock !xchange. he index was de%eloped with a base index %alue o& 155 as o&
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0ugust 15, 167+. 0t the end o& year +51+, JCI has gained 1+.5* percents to -+71.78 le%el,
compared to *7+1.66 &rom the pre%ious year.
his study pro%ide se%eral contributions to the literature. 9irst, it estimates a relati%ely
wide range o& multi%ariate n(regime !C" models. he model is applied to a ten(%ariable
%ector that includes ross 'omestic :roduct, Consumer :rice Index, Interest 2ate, 'ow
Jones Index, ;ikkei Index,
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he existing model explains the relationship between macroeconomic %ariables and
stock returns &rom the perspecti%e o& discounted &uture cash &lows earned by in%estor o& the
stock #Chen et al., 1678B :earce and 2oley, 167*B im and Wu, 167?$. In%estors incorporate
the in&ormation into their estimates o& the appropriate discount rate and the expected &low o&
di%idend &rom stocks, which in turn a&&ects stock price 9or instance, with the model :5DEt
!#ct$F#1Gkt$t, changes in systematic shock in&luences the price o& the stock, :5, by the
expected cash &lows, !#ct$, andFor %ia the re>uired rate o& return, kt. =erde and /aettem
#1666$ argues the latter #kt$ is related to both the le%el o& discount rates and the term(structure
spreads across di&&erent maturities, in which the stock market is taken as endogenous.
he macro %ariables that become the proxies &or measures &or aggregate economic
acti%ity in this paper are real gross domestic product #':$, industrial production, real
money supply, real consumption, aggregate &oreign reser%es, interest rate, exchange rate and
energy price #WI oil spot$, and commodity prices #Crude :alm Oil and old$. he ': and
Industrial production mainly a&&ects the economic acti%ity by in&luencing the &uture cash
&lows o& the stock.
he money supply can a&&ect the stock market in se%eral methods. One way is through
the port&olio balance model that works through money supply increase that leads to a
port&olio shi&t &rom non(interest bearing money to &inancial assets including e>uities. In
addition to that, "oney supply &luctuations can a&&ect the stock market through their e&&ects
on in&lation uncertainty. Ananticipated in&lation may directly a&&ect real stock prices
#negati%ely$ through unexpected changes in the price le%el. In&lation uncertainty may also
a&&ect the discount rate thus reducing the present %alue o& &uture corporate cash &lows. 'e9ina
#1661$ argued that high in&lation initially has a negati%e e&&ect on corporate income due to
immediate rising costs and slowly ad=usting output prices, sinking pro&its and there&ore the
share price. "andelker and andon #167$, 0sprem #1676$, and horbecke #166?$ also
showed that real e&&ects o& monetary policy a&&ect the stock price signi&icantly. "ore
speci&ically, :atelis #166?$ propose that money supply shocks in&luence e>uity prices mainly
%ia the risk premium.
We hypothesize that interest rate relate negati%ely with stock prices. "easured as
opportunity cost, the change in nominal interest rate will moti%ate in%estor to substitute
e>uity shares &or other assets in the port&olio. his increase has negati%e e&&ect on stock prices
&rom asset port&olio allocation. uisitions
and buyouts #Wongbangpo and /harma, +55+$
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-
he exchange rate has indirect impact to stock prices. 0ccording to an et al. #+558$,
I& the domestic currency depreciates against &oreign currencies, the export price will go down
and, conse>uently, the %olume o& the countryHs exports will rise, assuming that the demand
&or this product is elastic. "ukher=ee and ;aka #166$, 0chsani and /trohe #+55+$ con&irmed
this positi%e relationship existed in Japan and Indonesia. 0dditionally, 0=ayi and "ougoue
#1668$ also showed that an increase in stock price has a negati%e short(term e&&ect on
domestic currency %alues but in the long term this e&&ect is positi%e, while currency
depreciation has a negati%e short and long(term e&&ect on the e>uity prices.
On the basis o& economic theory, we ha%e two scenarios with regard to the sign o&
relationship between macro %ariables and stock prices, &irst the &lowHH scenario, which is
based on the &lowHH approach to exchange rate determination, and the stockHH scenario,
which is based on the port&olio approach to exchange rate determination. he &lowHH
scenario depends on two well(documented relationships, the relationship between the real
exchange rate and economic acti%ity #see e.g. Cornell, 167*B Wol&&, 1677$, whereby a &all in
the real exchange rate increases the competiti%eness o& domestic goods %ersus &oreign goods
and the le%el o& domestic aggregate demand and outputB and the relationship between
economic acti%ity and stock markets #see e.g. /chwert, 1665B 2oll, 166+B Cano%a and
'e;icolo, 166$.
he stockHH scenario depends on the port&olio approach to exchange rate
determination. 0gents allocate their wealth amongst alternati%e assets including domestic
money, domestic bonds and e>uities, and &oreign securities. he role o& the exchange rate is
to balance the asset demands and supplies. hus, any change in the demand &or and supply o&
assets will alter the e>uilibrium exchange rate. 9or example, a rally in the A/ stock market
will cause Indonesia stock market to rise as a result o& intensi%e trading between Indonesia
and A/ companies. hat in turn will increase wealth and the demand &or each o& the assets in
Indonesia #wealth e&&ect$. he excess demand &or IndonesiaHs currency will cause interest
rates to go up and a substitution &rom &oreign securities to domestic assets resulting in an
appreciation o& the domestic real exchange rate. he economic currency exposure &or
indi%idual &irms will depend on the currency structure o& its exports, imports and &inancing.
'e%aluation can either raise or lower a &irmHs stock prices depending whether the &irm is an
exporting or importing &irm.
% Commodities Prices and Stock Prices
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he linkage between oil price and &inancial markets appears to be natural. "ussa
#+555$ argues that oil price %olatility in&luence economic acti%ity, corporate earnings,
in&lation and monetary policy. hus, an increase in the oil price has implications &or asset
prices and &inancial markets. Chen et al. #1678$ also used oil prices as a measure o& economic
risk in the A./. stock market. 9or Japanese market, uities. Asing
17 national e>uity markets data, 9erson and
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9urthermore, empirical studies between the well(de%eloped e>uity markets and the
0sian markets are well documented 0ggarwal and 2i%oli #1676$ and Cheung and "ak
#166+$, who obser%ed the day(to(day co(mo%ement o& the A./. market and %arious 0sian
markets. his market interdependence relationship has also been examined using daily data
by other researchers #e.g., "aldanado N /aunders, 1671B !rrunza N 2osenberg, 167+B
:hilippatos et al., 167*, !un and 2esnick, 167-$.
he idea that macroeconomic %ariables explained stock prices has &aced many
contrary arguments recently. /ome researchers argued that stock market mo%ements since the
mid 1665s could not be explained by economic &undamentals. his idea has emerged a&ter
A/, !uropean and 0sian markets witnessed unprecedented highs in the mid 1665s but then
&ollowed by sharp re%erse in the +555s as a result o& excessi%e speculation. 0ccording to
Carlson and /argent #166?$ and /hiller #+55$, the e>uity prices during the second part o& the
1665s in the A/ does not happen due to the change in &undamental %alues such as pro=ected
earnings growth or di%idends but because o& exogenous shocks andFor market irrational
beha%ior. ee #166, 1667$ and Chung and ee #1667$ supported this %iew and argued that
&undamental %ariables like discount rates, earnings, di%idends and industrial production did
not explain price mo%ements. In this case, stock return %ariation a&ter 1665s may not be
explained by the notion that stock market is the main indicator o& real economic acti%ity
#9ama, 1665$.
@inswanger #+555, +551, +55-$, gi%ing support to the stock(market bubble hypothesis
to explain the breakdown in the linkage between stock returns and real economic acti%ity in
the A/ in the second part o& the 1665s was.
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companies listed in the exchange comprise o& a large number o& export(dependent industry
that rely on international business cycles and, there&ore, ha%e high &luctuations in their
pro&itability.
/econd, a small e>uity market is also prone with manipulation opportunities that do
not exist in more de%eloped markets, and may draw speculators inside, thereby increasing the
possibilities o& the market reacting inappropriately to new in&ormation. he obser%ation could
be explained by both trading noise and the substantial commodity price risk.
he !C" &ramework we used in this study has ad%antages and shortcomings in
modelling the relationship between stock prices and macroeconomic %ariables. Wen et al.
#+51+$ argues that this model is e&&ecti%e because they are &lexible and are able to control &or
serial correlation in asset returns, and it is easy to obser%e the interactions between %ariables
%ia an impulse(response &unction. uestions, we use these &ollowing data as &ollows)
No. Variable Description Source
1 LJCI Jakarta Composite Index Bloomberg2 LD! "eal ross Domestic !roduct Badan !usat Statistik
# LC!I Consumer !rice Index Bank Indonesia
$ I" !olic% "ate &SBI'BI "ate( Bank Indonesia
) LDJI Do* Jones Index Bloomberg
+ LN,- Nikkei 22) Index Bloomberg
L/SI /an Seng Index Bloomberg
0 LCL Coal prices at t3e Ne* Sout3 4ales Bloomberg
5 LC! Coal prices at 6ala%sia Bloomberg
17 LLD old !rice Bloomberg
11 LIL Crude il &4est 8exas Intermediate( !rice Bloomberg12 L"99" "eal 9::ecti;e 9xc3ange "ate Index C9IC
8able 1
"esearc3 Variables
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+%% Descripti*e !nalysis
he Jakarta Composite Index #JCI$ is a modi&ied capitalization(weighted index o& all
stocks listed on the regular board o& the Indonesia /tock !xchange. he index was de%eloped
with a base index %alue o& 155 as o& 0ugust 15, 167+. 0s o& today, it includes more than -55
companies listed in the stock exchange.
.i#"re '% Jakarta Composite Index (JCI) *s% .orei#n Stock Market
9rom the &igure abo%e, it shows that, by 'ecember +51+ Jakarta Composite Index
#JCI$ grows by approximately eight times o& its original le%el in January +555. JCI grows up
16 in the past 1+ months to a historically high -6?7 points le%el. his is the most signi&icant
increase compare to other ma=or stock indexes such as 'ow Jones Index #'JI$, ;ikkei ++
Index #;M$, and
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.i#"re % Jakarta Composite Index (JCI) *s% Global Commodities
We take monthly obser%ations on the mo%ements o& JCI along with lobal
Commodities #Oil, C:O, old and Coal$ in &igure +, and &ind that these indexes are mo%ing
along together %ery closely, &or period spanning January +555 to 'ecember +51+, with JCI
and old as the ones that seems to ha%e the highest correlation, especially a&ter the period o&
global sub(prime mortgage crises. JCI and Oil also seems to ha%e a %ery signi&icant
relationship due to the &act that Indonesia is oil(producing countries and &ormer members o&
O:!C. Indonesia, a resource(based economy #the main producer o& C:O and Coal$, is highly
in&luenced by the &luctuation o& C:O and Coal prices in both short(run and long(run.
7
277
$77
+77
077
1777
Dec
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.i#"re +% Jakarta Composite Index (JCI) *s% Macro $conomy
he data &rom &igure * shows that Indonesia market capitalization to ': is still
below 5, and there&ore indicates that Indonesia stock market is under%alued. It is worth
noting that Indonesia ': growth a%erages around .* between January +555 and
'ecember +51+ and are bene&iting &rom the low le%el o& in&lation. 0s shown in the &igure,
o%er the period o& last decade, the central bank has consistently ad=ust the policy rate to
smooth business cycles and shield the economy &rom external shocks &rom global markets.
+%+% Methodolo#y
his study use ector 0utoregression #02$ model to capture the linear
interdependencies among multiple time series %ariables we describe in section +. 02 was
introduced by Christopher /ims #1675$. 0 uni%ariate autoregression is a single(e>uation,
single %ariable linear model in which the current %alue o& a %ariable is described by its own
lagged %alues. 0 02 is an n(e>uation, n(%ariable linear model in which each %ariable is in
turn explained by its own lagged %alues, plus current and pre%ious %alues o& the remaining n(
1 %ariables.
0ccording to /tock and Watson #+551$, this &ramework pro%ides a systematic way to
capture rich dynamics in multiple time series. In determining the %ariables included in 02,
7
17
27
#7
$7
)7
+7
7
)
17
1)
27
2)
Jan
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economic theory and institutional knowledge is re>uired to sol%e the identi&ication as shown
in &igure -.
.i#"re /% &elationships 0etween -ariables
he standard in 02 is to analyze the results &rom ranger(causality tests, impulse
responses and &orecast error %ariance decompositions. In our study the computation o& this
statistics are done using !%iews so&tware.
o identi&y the best 02 model, we &ollowed standard identi&ication procedures in
&igure . We identi&y the problem in our study, create rele%ant hypothesis, and collect data.
hen, we check the stationarity o& our data and per&orm cointegration to select between 02
or !C" models.
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1+
.i#"re 1% -!& Identiication
Problem
Identification
START
Hypothesis
Data
Collection
Unit Root Test
(ADF Test)
Data Stasionary
Transformation
Cointe!ration
Test
"ariables
Cointe!rated
"AR #odel
$stimation
"$C #odel
$stimation
Inno%ation
Acco&ntin!
$'D
'o
es
'o
es
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1*
+%+%'% 2nit &oot 3est!stimation o& time series econometric models will yield an un(meaning&ul conclusion,
when the data contains a unit root #not stationary$. ;on(stationary series will result in a
spurious regression. his condition is characterized by high coe&&icients o& determination,
+
R , and signi&icant t statistics, but the economic interpretation o& this relationship would be
misleading #!nders, +55-$.
/uppose tYis a stationary time series i& it satis&ies the &ollowing conditions)
1. # $t yE Y = time(independent
+. ( )+
+# $t Y t yVar Y E Y = =
time(independent
*. co%# , $k t t k Y Y += time(independent
/tationary tests can be conducted by %arious methods such as graphics, correlogram
and the unit root test. he unit root test is employed in this research. wo methods o&
unit root test that are commonly used are the 0'9 #0ugmented 'ickey 9uller$ test
and the :: #:hillips :eron$ est.
+%+%% !"#mented Dickey ."ller 3esthe rationale o& 'ickey 9uller test is to examine whether, or not, a time series is a
random walk.
Consider 02#1$ model
( )1 1 *.1t t tY Y = + +
I& 1 1 = , then the model abo%e becomes random walk. 2andom walk is one &orm o&
non(stationary time series. In e>uation #*.1$ subtract 1tY to both sides, thus,
( )1 1 1
1
1
1
#*.+$
where, 1
t t t t
t t t
Y Y Y
Y Y
= + +
= + +
=
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9rom the e>uation abo%e, I can the make hypothesis
5 1
1 1
< ) 1
< ) 1
=
ue is by making regression between 1andt tY Y .
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1
P 1
'ickey 9uller.Pstd. error# $
p
i
i
p
i
i
=
I& absolute %alue o& statistic is larger than absolute
%alue o& 'ickey 9uller critical %alue then re=ection o& null hypothesis implies that time series
data is stationary, meanwhile i& absolute %alue o& statistic is smaller than absolute %alue o&
'ickey 9uller critical %alue then null hypothesis is not re=ected.
+%+%+% 0i*ariate -!& system with order p02 with order p o& bi%ariate system or two %ariables
ty
1
+
=
t
t
can be de&ined as
t 1 t 1 p t p t... = + + + +y y y e #*.-$
where
1
+
=
is two(dimension %ector,
11, 1+,
+1, ++,
, 1, +,...,
= =
i i
i
i i
i p is ( )+ + coe&&icient matrix and
e1
+
t
t
e
e
=
is a white noise %ector. In other words)
1$ te has zero mean, ! "tE e = 4
+$ te has constant %ariance, t ,T
t eE e e t
= # $
*$ and et se are not correlated, &or t s% .
!>uation #*.1+$ can be written as &ollows)
11, 1+, 111,1 1+,1 11,+ 1+,+1 1 1 1 + 11
+1, ++, ++1,1 ++,1 +1,+ ++,++ + 1 + + ++
... p p t pt t t t
p p t pt t t t
yy y y e
yy y y e
= + + + +
wo(dimension random %ector t 1 t t 1..., , , ,... +y y y is a stochastic process %ector. 0
stochastic process %ector is stationary i&
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1$ ! "! , t= $ty
+$ ( ) ( )( (co%# , $ ! R # $, t dan 5,1,+,...= = & $ = t t h t t h h hyy y y y
Implication o& the second property is that &or hD5, ty has the same co%ariance matrix,
that is ( ) ( ) R ,t t hE t = # $ yy y .
+%+%/% Cointe#ration 3estJohansenHs cointegration test is based on the 02#p$ model o& non(stationary
%ariables. 9or simpler Johansen test procedure, 02#1$ model will be used. 2emember that
02 #1$ model is noted in matrix notation)
1 1t t tY Y = ' +
In JohansenHs cointegration test, analysis o& %ariables is not only &ocused on the result
o& 02 e>uation system #Impulse 2esponse 9unction and ariance 'ecomposition are the
most commonly used, as pre%iously disucssed$, but also considered a stepping stone &or the
next cointegration test, whereby reparameterization need to be done &rom 02#1$ model to
"odel ector !rror Correction #!C"#1$$.
he ranger theorem ensures the existence o& an error correction representation in a
cointegrated regression. @ased on this theorem, e>uation 02#1$ can be represented in the
&orm o& !C" as &ollows)
1 1
1 1 +
where)
and
t t t
t t
Y Y
Y Y Y I
= ' +
= ' = '
his !C" #1$ &orm contains in&ormation about short(run and long(run changes stated by
parameter and . his "atrix will be &urther used to determine whether regression
system is cointegrated. his is the core o& Johansen test procedure in analyzing the
cointegration relationship between obser%ed %ariables.
9or instance, a component o& %ector 5t is a &irst order integration or written as I#1$,
then ''''Yt-1 is a linear combination o& %ariable Yt-1 I#1$. In order to estimate all combination
i& ' '
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1?
possibilities &rom Mt(1 which results in close correlation with Yt-1, a stationary element,
Johansen uses '''' matrix characteristics as &ollows)
'% I& 2ank#''''$D5, then, there is no cointegration between %ariables% I& 2ank#''''$Dm #m ) the number o& %ariables in 02 model$, then all %ariables are
cointegrated
+% I& 5 S 2ank#''''$ Sm, then 2ank #'''') states the number o& %ariables that arecointegrated between 5 and m.
"atrix '''' can be decomposed to ''''= Twhere is speed o& ad=utsmentandis long(run
coe&&icient matrix so that T5t6'up to m(1 combinations is a cointegrated relationship which
ensures that 5t reaches long(run e>uilibrium. 9urther, 2ank #T
$ can be determined by
calculating eigen%alue &rom T.
+%+%1% Imp"lse &esponse ."nction0n impulse response &unction aims to obser%e the e&&ects o& a one standard de%iation
shock to one o& the inno%ations on current time %alues and the &uture %alues o&
endogenous %ariables included in the model.
!nders #+55-$ illustrates impulse response by employing bi%ariate 02 as &ollows)
1 11 11 1+
5+ ++ +1 ++
(
=
= +
i
t t i
it t i
y ey a a
y ey a a
he residual can be expressed as)
( )1 11+
+ ++11+ +1
1111
=
t t
t t
e be bb b
hen, by combining two e>uations abo%e we can ha%e "o%ing 0%erage
representation as &ollows)
11 +1
1+ ++
1 11
5+ ++
# $ # $
# $ # $
t t i
it t i
i i
i
y y
iy y
(
=
= +
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he &our coe&&icientsB 11 1+ +1 ++# $, # $, # $ dan # $ i i i i are called impulse response
&unctions.
+%+%7% 3he Cholesky Decompositionhe Cholesky decomposition or commonly known as %ariance decomposition
pro%ides in&ormation about the relati%e importance o& each %ariable in the 02 system
according to the shocks. he Cholesky 'ecomposition method is another strategy to describe
the dynamic system contained in 02 by collecting estimates o& error %ariance o& a %ariable,
or, the di&&erence amount between the %ariance be&ore and a&ter shock. @oth shocks
originated &rom that %ariable itsel& and shocks o& other %ariables. ariance decomposition is
used to predict the %ariance percentage contribution o& each %ariable due to changes in certain
%ariables in the 02 system.
!nders #+55-$ demonstrates mathematically the mechanism o& %ariance
decomposition by &irst building n(step ahead &orecast error as &ollows)
11 11 1 11 1 1+ 1+ 1 1+ 1#5$ #1$ ... # 1$ #5$ #1$ ... # 1$t n t t n yt n yt n yt zt n zt n zt y E y n n + + + + + + + + = + + + + + + +
he %ariance o& the n(step ahead &orecast error %ariance o& t ny + can be obtained
+ + + + + + + + +
11 11 11 1+ 1+ 1+# $ #5$ #1$ ... # 1$ #5$ #1$ ... # 1$y y zn n n = + + + + + + +
@ecause all+# $jk i are nonnegati%e then %ariance o& the &orecast error increases as
&orecast horizon n increases. :roportions o&+# $y n due to shocks in *yt and *t
se>uences are
+ +
11 11 1 11 1
+
#5$ #1$ ... # 1$
# $
y yt n yt n yt
y
n
n
+ + + + + +
and
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! "+ 1+ 1+ 1 1+ 1+
#5$ #1$ ... # 1$
# $
z zt n zt n zt
y
n
n
+ + ++ + +
/% &es"lts !nalysis/%'% 2nit &oot 3est
he test employed is 0ugmented 'ickey 9uller #0'9$ test.
he model used in 0'9 test is)
1 1
+
p
t t i t i t
i
Y Y Y +=
= + + +
9rom the abo%e mode, a hypothesis can be &ormulated)
;on(stationary data B stationary data
5
5
< ) 1
#;on stationary data$
< ) 1
#/tationary data$
p
i
i
p
i
i
=
uare 2esidual
k lag length
> number o& regressor k 1 number o& estimated parameter
!!R k"IC k T #
T
!!R k!C k T # T
T
= +
= +
=
= = + =
'etermination o& optimal lag used by the researcher in order to estimate a short run
e>uation is based on 0kaike In&ormation Criterion #0IC$. he criterion o& optimal lag
in&ormation can be seen in able + below.
Intercept8rend ?
Intercept
None Intercept8rend ?
Intercept
None
1 LJCI 7.5255 7.175+ 7.501# 7.7777 7.7777 7.7777
2 LD! 7.5551 7.)+55 1.7777 7.7777 7.7771 7.2)#)
# LC!I 7.077 7.$0+ 1.7777 7.7777 7.7777 7.#10#
$ I" 7.)01 7.10#5 7.#202 7.7777 7.7777 7.7777
) LDJI 7.$#+7 7.$011 7.1 7.7777 7.7777 7.7777
+ LN,- 7.11)$ 7.$2$2 7.#22# 7.7777 7.7777 7.7777
L/SI 7.+51 7.$#52 7.07$7 7.7777 7.7777 7.7777
0 LCL 7.$#$ 7.7)15 7.0)5 7.7777 7.7777 7.7777
5 LC! 7.+))) 7.#0+1 7.017 7.7777 7.7777 7.7777
17 LLD 7.5+2 7.7111 7.5550 7.7777 7.7777 7.7777
11 LIL 7.+15$ 7.1)) 7.0)2+ 7.7777 7.7777 7.7777
12 L"99" 7.)771 7.11)5 7.02#5 7.7777 7.7777 7.7777
No. Variable
Le;el =irst Di::erence
@nit "oot 8est &! Value(
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+1
3able +% Comparison between se*eral model selection criterion
0ccording to able * abo%e, it can be seen that the optimal lag based on 0IC is lag 7.
/%+% Cointe#ration 3esthe purpose o& cointegration test is to assess similarities o& mo%ement and
relationship stability between %ariables in a long(run. When a data series contains a unit root
and integrated to the same order, cointegration test can be per&ormed to assess the existence
o& cointegration. In this research, the JohansenHs Cointegration est method is employed. 0n
in&luential relationship can be seen &rom the cointegration that exists between %ariables.When a cointegration exists between %ariables, this implies that in&luential relationship
occurs throughout %ariables and in&ormation is parallelly distributed.
he JohansenHs Cointegration est indicates that a cointegrating %ector exists, or at least a
linear independent combination exists &rom the %ariables contained in the model. he
conse>uence is that alternati%e hypothesis which states the presence o& cointegration
relationship can be accepted.
$ag $og$ $% F&" 'IC SC
7 1770.701 N 2.119
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++
3able /% 3race 3est &es"lts
his cointegration in able -. test is based on trace test, since the %ariables do not
ha%e normal distributions. he Johansen Cointegration est o& %ariables indicates the
existence o& ele%en cointegration e>uations. ;ot all %ariables are stationary in le%elB
there&ore, there is a cointegration among %ariables. hus, estimation model by !C" can
generate stationary estimation and errors. Cointegration test result indicates that research
%ariable has long(term relation. It can be concluded that the next step o& analyzing short(run
analysis between research %ariable in long(term can be executed.
/%/% on#6&"n ModelIn the long(run #with the use o& cointegrating %ectors interpretation$, the &ollowing
model in able - can be constructed. We also compute error correction %ariable &rom the
cointegrating relationship between the %ariables.
None A 7.)$)$ 1701.#+) ##$.50# 7t most 1 A 7.$52$ 0+#.2001 20).1$2) 7
t most 2 A 7.+5)00 ++1.#20# 2#5.2#)$ 7
t most # A 7.)$511 $0.)0# 15.#75 7.7771
t most $ A 7.)27)) #1.75)$ 1)5.)25 7
t most ) A 7.#+1 2+#.+70 12).+1)$ 7
t most + A 7.##+7+# 15$.)252 5).)#++ 7
t most A 7.2)22#0 1#$.#2# +5.01005 7
t most 0 A 7.227721 52.25$#+ $.0)+1# 7
t most 5 A 7.1077+ )+.71)7+ 25.57 7
t most 17 A 7.1)01$5 2).00$1 1).$5$1 7.771
t most 11 7.77$$1 7.+)$225 #.0$1$++ 7.$10+
@nrestricted Cointegration "ank 8est &8race(
8race
Statistics
7.7)
Critical Value !rob.AA9igen;alue
/%pot3esi>ed
No. o: C9&s(
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+*
3able 1% -$CM on#6&"n Model
$JCI(*1) $GD&(*1)
C
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+-
/%1% Gran#er Ca"sality 3est
3able 7% Gran#er Ca"sality 3est &es"lts
U ;ull hypothesis re=ected at 1 signi&icance le%el
UU;ull hypothesis re=ected at signi&icance le%el
UUU ;ull hypothesis re=ected at 15 signi&icance le%el
he ranger(causality test is conducted to study the lead(lag relationships between
JCI, macroeconomic %ariables, global stock markets and commodity prices. he results are
reported in able 8 abo%e. "acroeconomic %ariables, namely, C:I and ': are &ound to
be the most important %ariables in determining the JCI per&ormance when they were
considered in pairs with the JCI using the ranger causality test. he results also indicate that
the pricesH &luctuation o& global commodity such as old, Oil, C:O and Coal does not
signi&icantly a&&ect the per&ormance o& JCI. 9urthermore, the mo%ement o& global stock
markets such as 'ow Jones, ;ikkei and
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+
/%7% Imp"lse &esponse ."nction0n impulse response &unction states the e&&ect o& one standard de%iation shock to one
o& the inno%ations on current time %alues and &uture %alues o& endogenous %ariables. 0 shock
&rom endogenous %ariable directly in&luences the %ariable itsel&, which then in&luences other
endogenous %ariables through the dynamic structure o& 02 and !C. I29 pro%ides
direction and magnitude o& the e&&ect between endogenous %ariables as it demonstrates the
in&luence o& one(standard de%iation endogenous %ariable shock on other endogenous
%ariables and the %ariable itsel&. here&ore, with new in&ormation coming up, any shock that
occur in a %ariable, will a&&ect the %ariable itsel& and other %ariables in a system. Impulse
2esponse 9unction on research %ariables &or 15 upcoming period is presented below.
.i#"re 7% Imp"lse &esponse ."nction
o obtain additional insights into the mechanism o& transmissions o& stock market
mo%ements, we now examine the pattern o& dynamic responses o& JCI to inno%ation &rom
each %ariable. 0s can be seen &rom the table abo%e, the impulse response o& the JCI to a ':
+,-.
+,-/
,--
,-/
,-.
)- 0- 1- /- 2- 3- 4- .- 5- )--
Response of 67CI to 68DP
+,-.
+,-/
,--
,-/
,-.
)- 0- 1- /- 2- 3- 4- .- 5- )--
Response of 67CI to 6CPI
+,-.
+,-/
,--
,-/
,-.
)- 0- 1- /- 2- 3- 4- .- 5- )--
Response of 67CI to RAT$
+,-.
+,-/
,--
,-/
,-.
)- 0- 1- /- 2- 3- 4- .- 5- )--
Response of 67CI to 6D9:
+,-.
+,-/
,--
,-/
,-.
)- 0- 1- /- 2- 3- 4- .- 5- )--
Response of 67CI to 6'I;;$I
+,-.
+,-/
,--
,-/
,-.
)- 0- 1- /- 2- 3- 4- .- 5- )--
Response of 67CI to 6HA'8
+,-.
+,-/
,--
,-/
,-.
)- 0- 1- /- 2- 3- 4- .- 5- )--
Response of 67CI t o 6C9A6
+,-.
+,-/
,--
,-/
,-.
)- 0- 1- /- 2- 3- 4- .- 5- )--
Response of 67CI to 6CP9
+,-.
+,-/
,--
,-/
,-.
)- 0- 1- /- 2- 3- 4- .- 5- )--
Response of 67CI to 6896D
+,-.
+,-/
,--
,-/
,-.
)- 0- 1- /- 2- 3- 4- .- 5- )--
Response of 67CI to 69I6
+,-.
+,-/
,--
,-/
,-.
)- 0- 1- /- 2- 3- 4- .- 5- )--
Response of 67CI t o 6
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+8
implies that JCI responds most strongly to ': on month 15 and +5 when the ': shocks
occurs. his may re&lect a low degree o& economic and &inancial integration and the &ree
in&ormation between the real and capital sectors. o some extent, JCI also reacts to ':
without lag. @ecause the two %ariables operate with a lag, this result is as expected.
9urther examination o& &igure abo%e re%eals some interesting patterns. 0s can be seen
&rom the &igure, JCI responds positi%ely to 'ow Jones and old while JCI responds
negati%ely to
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+?
/%9% -ariance Decomposition3able 9% -ariance Decomposition o JCI &es"lts
able ? reports the %ariance decomposition results &or the e&&ect o& %arious shocks
in%ol%ed in the !C" model on the JCI. 0ll !C" models include 7 lags. he numbers
reported indicate the percentage o& the &orecast error in each %ariable that we can attribute to
each o& the structural inno%ations at di&&erent horizons #&rom 1 month to 155 months$. We
report the percentages &or selected &orecast horizons o& multiple ten.
he decomposition results show that e%en in the long(run #i.e the 155(month &orecast
horizon$, ':, C:I and Interest 2ate contribute a relati%ely small share to the %ariation o&
JCI. On the other hand, 'ow Jones and Oil :rices are the shocks that produce the highest
%ariation in the JCI. In the short run #e.g., 1(month &orecast horizon$, JCI, 'ow Jones, and Oil
:rices produces -?, 1?, and +5 o& the %ariation o& JCI, respecti%ely. !xtending the
&ocus to the long(run #e.g. 155(month &orecast horizon$, JCI, 'ow Jones, and Oil :rices
generates 16, ++, and 17 o& the %ariation o& JCI.
/%:%.orecastin#
his study computed multistepLahead &orecast o& JCI by iterating &orward the reduced
&orm !C". @ecause the ultimate test o& a &orecasting model is out(o&(sample per&ormance,
this study &ocus on out(&o(sample per&ormance o& JCI &rom period January +51* to 'ecember
+51*. 0ccording to the &orecast, JCI will increase steadily to **1. in June +51* and ?+6.7
in 'ecember +51*.
!eriod S.9. LJCI LD! LC!I "89 LD4 LNI,,9I L/N LCL LC! LLD LIL L"891 7.7)$50 177 7 7 7 7 7 7 7 7 7 7 7
17 7.#1$2$2 $.+$)1) 7.)1#5)$ 1.0#52 7.+)$)$2 1.252$# 1.75#$1) 1.$7#275 1.$21$ 2.5)#2) 2.#+$02 27.+1#) 2.7#0)+
27 7.$+52# 2$.$7$+ 1.2$$$$ 1.$5#) 7.0770 21.001 1.$0$#55 $.777+ 1.557)70 12.1+$5 +.227521 21.)$12 1.277#0
#7 7.))))0 22.2#7#) 1.17$52 2.#+21 7.+7+1+2 2#.1)+1+ 1.2#0$10 ).07$)+ 2.5272## 11.215+ 5.22$+22 15.152$1 7.00775)
$7 7.+$271 22.1)5+ 7.05+)0 2.1)0$72 7.))$20 22.71 7.5017$ +.07#20# #.$#0# 5.5111 17.+1##2 15.)20+) 7.7)#70
)7 7.11+2 21.$7)52 7.57$751 2.2+22 7.)7$+01 21.$0$22 7.07)27$ .)$2+22 #.522 17.77#) 17.507+$ 15.)++1 7.)52+#
+7 7.#20+ 27.$$251 7.0$75#$ 2.)1)$$5 7.$)57)) 21.0057 7.+5175+ .5#+7++ #.05272) 17.#17+$ 11.215$ 15.25)# 7.)7+22
7 7.0#27+5 27.770#2 7.$5)#) 2.+1+)2# 7.$#1#0) 22.2)20 7.)50))) 0.1#7) #.50$0) 17.727+1 11.+01+ 15.70+25 7.$#0+5+
07 7.00027$ 15.172 7.+00++$ 2.+1)10 7.$10+11 22.27#0) 7.)2)+2 0.#07705 $.1#2$12 5.++2 12.7)71) 15.7$+$+ 7.#57)51
57 7.5#55$ 15.)### 7.+$52 2.+$2))5 7.$7)75 22.12$0+ 7.$+5 0.+7)7 $.21$#$) 5.+1#$2 12.225## 15.77)+ 7.#)$$0
177 7.50015 15.2550 7.+12#2 2.+5#$20 7.#57$2 22.15#) 7.$2)2+0 0.))#$) $.2#5++1 5.212 12.#)272 10.5$)#2 7.#2222)
C3olesk% rderingE LJCI LD! LC!I "89 LD4 LNI,,9I L/N LCL LC! LLD LIL L"89
Variance Decomposition o: LJCI
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+7
.i#"re 9% JCI .orecast &es"lts
1% Concl"sions remarksOur paper pro%ides the latest examination o& the e&&ect o& macroeconomic %ariables,
global index, and commodity prices on JCI. Asing the !C" methodology, we compute 15
di&&erent structural shocks &or the JCI.
he results show that the impact o& JCI, 'ow Jones, and Oil price &actor shocks play a
signi&icant role in explaining the ad=ustments in JCI. 9urther, the ranger temporal causality
tests suggest a strong role &or idiosyncratic C:I, 'ow, ':,
8/14/2019 Dynamic Interrelationships Between Macroeconomic Indicators Global Stock Market and Commodities Prices and Ja
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+6
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