dynamic interrelationships between macroeconomic indicators global stock market and commodities...

8/14/2019 Dynamic Interrelationships Between Macroeconomic Indicators Global Stock Market and Commodities Prices and Ja

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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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.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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1-

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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17

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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16

! "+ 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(


21/34

+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


22/34

++

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(


23/34

+*

3able 1% -$CM on#6&"n Model

$JCI(*1) $GD&(*1)

C


24/34

+-

/%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


25/34

+

/%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


26/34

+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


27/34

+?

/%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


28/34

+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, ':,


29/34

+6

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dynamic interrelationships between macroeconomic indicators global stock market and commodities...

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