001195-full-apcbss-2015-kuala-lumpur-srinivasan v.3.docx

7/26/2019 001195-full-apcbss-2015-kuala-lumpur-srinivasan v.3.docx

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CO-MOVEMENT OF OIL PRICE, EXCHANGE

RATE AND STOCK INDEX OF MAJOR OIL

IMPORTING COUNTRIES: A WAVELET

COHERENCE APPROACH

M.Thenmozhi*

Indian Institute of Technology Madras, India

N. Srinivasan*

Indian Institute of Technology Madras, India

ABSTRACT

The study examines the co-movement between oil price and macroeconomic indicators such as

exchange rate and stock indices of major oil importing countries. e differ from previous studiesby examining the co-movement using wavelet coherence analysis and examine the macroeconomic

dynamics of major oil-importing countries during all the economic cycles across different

fre!uencies. e use nominal price rather than real price to make the results more meaningful fortraders and institutional investors. "ur analysis is based on #$%& observations covering the period

&$$#-%'for fifteen major oil importing countries. avelet (oherence analysis indicates a high

coherence between oil price and macroeconomic indicators across all the countries during thefinancial crisis. The nominal exchange rates tend to have negative relationship with benchmark oil

prices except exchange rate of )apan in the long run and exchange rate of *outh +orea in the

medium run. *tock indices tend to have positive relationship with benchmark oil prices in both longand medium run. * is leading the oil price, whereas **/$, 0ikkei &&/, 0I1T2, +"*I, 345,

(4(, I65, 1T**I, 1T*MI6, 45, T*, 57 %$$, 89'/ and 68 &$ are lagging the oil pricein the long run. In the medium term, except for 0I1T2, oil price is leading the stock market index.

"verall, the results indicate that the oil price and stock indices of the major oil-importing countries

are correlated in long and medium term, but not in short term. The lead:lag relationship betweenoil price and macroeconomic indicators are observed to change across fre!uency and time. hile

exchange rate offers diversification benefits, stock market indices provide no diversification

avenues since the pattern of co-movement of stock market indices and oil prices are similar acrossall oil importing countries. The results have implications for individual traders and institutional

investors while designing their portfolio for short, medium and long term time hori;ons.

JEL Classii!a"i#$s:


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(rude oil is actively included in the portfolio of various hedge funds and variability in oil

price has significant linkage with various macroeconomic indicators of a country. "il

price has influence on a country?s economy and thus on inflation and interest rate. "ilprices affect the stock prices either directly by influencing future cash flows of acompany or indirectly by affecting the interest rate that is used to discount the future cash

flows of a company. (hange in stock prices and exchange rates also impacts oil prices.

6ut, the nature and extent of relationship between oil price and macroeconomic

indicators may vary from time to time and understanding the pattern of relationshipacross time and fre!uency hori;on becomes essential for traders and investors.

LITERATURE REVIEW

revious studies that examined the relationship between the crude oil and exchange

rates@stock indices indicate inconsistency in results. 8ong-run e!uilibrium exists between

the crude oil price and exchange rate A4mano and Ban 0orden, %CCD> (haudhuri and

3aniel, %CCD> (hen and (hen, &$$E> "riavwote and riemo, &$%&F. *ome argued thatincrease in oil prices is associated with the appreciating exchange rate A4mano and Ban

0orden, %CCD> 6enassy-9uere et al., &$$E> 0arayan et al., &$$D> 6asher et al., &$%&>

6eckmann and (;udaj, &$%#F. 6ut other studies argued that increase in oil prices is

associated with the depreciating exchange rate Aang and u, &$%&F. hile bi-directional causality exists between oil price and exchange rates after crisis A3ing Bo,

&$%&F, at large time hori;ons A6enhmad, &$%&F and at higher time scales ATiwari et al.,

&$%#F, Iwayemi and 1owowe A&$%%F find no impact of oil price on exchange rates.

1ew studies A(ong et al., &$$D> ark and Gatti, &$$DF found that, oil priceshocks have no impact on the real stock returns and during crisis oil shocks do not affect

stock market phases A)amma;i and 4loui, &$%$F. However, Miller and Gatti A&$$CF and

)amma;i A&$%&F found that stock market reacts negatively to increase in oil price in the

long-run. There is also evidence that increase in emerging market stock prices increases

oil prices A6asher et al., &$%&F,but the impact of oil price shocks on stock prices foremerging countries is mixed partly in contrast to developed stock markets. Moreover, the

stock returns of large oil producing and consuming countries have relatively strong

dependence with oil price A*ukcharoen et al., &$%'F and the dependence betweencommodity and stock market is time varying and symmetrical A3elatte and 8ope;, &$%#F.

Traders and institutional investors have varied investment hori;ons and different

risk profiles and co-movement between oil prices and macroeconomic indicatorsbecomes important to assess the risk profile of countries and the market movements.

hile traders would prefer analysis based on nominal prices, most of the studies have

focused on real prices.

Most of the existing literature examines the co-movement using traditional timeseries models such as "8*, B4G@ B(M, co-integration and detrended correlation

analysis, which look into the time scale of the variables. However, co-movement between

variables may vary across time and the effect could change at different time hori;ons.

Bery few studies have used wavelet analysis to capture the co-movement dynamics acrosstime and fre!uency scales AGua and 0unes, &$$C> Tiwari et al ., &$%#>8oh,

&$%#F.Moreover, existing studies have examined the relationship between oil prices and

exchange rates@ stock indices for one country@ few countries, which are mostly developed

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L,NF with uniform time stepst

, is the convolution ofxn with the scaled and

normali;ed wavelet, is defined as

wnx ( s )=

t

sn'=1

N

xn'0 [(n'n) ts].

A%F

e define the wavelet power aswn

x (s )2

. The complex argument of

wnx (s ) can be interpreted as the local phase. *ince the wavelets are not completely

locali;ed in time, (T has edge effect which is addressedby introducing (one ofInfluence A("IF, which eliminates edge effects. The statistical significance of wavelet

power is assessed relative to the null hypotheses that the signal is generated by a

stationary process with a given background power spectrum A pk F.e follow the

procedure used by Torrence and (ompo A%CCDF for data generation using Monte-(arlo

simulation process and the corresponding distribution for the local wavelet power

spectrum at each time nand scales

as followsJ

wnx

(s)2

x2


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S (s1 wnx ( s )2) . S (s1wn

y (s )2).

S (s1wnxy ( s))2

Rn

2 ( s)=

A#F

e follow avelet (oherence as defined by Torrence and ebster A%CCCF,

where * is a smoothing operator and Rn2 (s ) is the value of avelet s!uared

coherency. The numerator and the denominator explain the s!uared absolute value of the

smoothed cross-wavelet spectrum and the smoothed wavelet power spectra, respectively.

Da"a

This study uses daily time series data of various benchmark crude oil prices such as the

TI, 6rent and "( basket crude oil spot price . The "( basket crude is used for

4sian countries> 6rent oil price is used for urope and TI oil price is used for the 7*.

e identify top fifteen oil importing countries based on the I4 crude oil importstatistics%. The data comprises stock indices of oil-importing countries such as the 7*

A*5F, (hina A**/$F, )apan A0+2F, India A0I1T2F, *outh +orea A+"*IF,


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In the avelet coherence plot, the hori;ontal axis represents time and the

vertical axis shows time scales in days as fre!uencies. Time scale % denotes &:' days>

Time scale & denotes ':D days, Time scale # denotes D:% days and so on. The timescales are described as high, medium and low fre!uency, where high represents short-rundynamics, medium represents medium-run and low represents long-run dynamics of the

two series.NIn the phaseO, i.e., arrows pointing to the right, denotes that variables have

positive effect, i.e., increase in oil prices increases the exchange rate@stock index. N"ut of

the phaseO, i.e., 4rrows pointing to the left, denotes that variable has negative effect, i.e.,an increase in oil prices decreases exchange rate@ stock index. The direction of the arrows

Aup@downF determines whether it?s leading or lagging the other series. Gight and up

denotes series is lagging> right and down denotes series is leading. 8ikewise, left and up

denotes the series is leading> 8eft and down denotes that series is lagging.

R%la"i#$si* 4%"'%%$ B%$!/a(5 Oil P(i!%s a$) E6!a$+% Ra"%s

The association between the 7*37G and oil price A1igure %.%F is found to be high inboth medium and short term. It is also observed from the graph that during the crisis

period the coherency is high and 7*37G is leading the oil price in medium term,

whereas in long term, 7*37G is lagging the oil price. 4nti-cyclical effect is observedin all the three fre!uencies.

The correlation between the 7*3(02 and oil price is found to be low A1igure

%.&F. "nly during the second half of &$$D, a high coherency is observed and during the

same period, 7*3(02 is leading the oil price.4 significant negative relationship between the 7*3I3G and oil price is found

A1igure %.#F during crisis in both medium and long term. High coherency is witnessed

during the crisis period, and 7*3I3G is leading the oil price in the long term. 4nti-

cyclical effect is observed in both medium and long term.1igure %.' shows a high coherency between the 7*3I0G and oil price during

crisis in both medium and long term. 3uring the crisis period, 7*3I0G is lagging the oil

price in the long term, whereas in the medium term 7*3I0G is leading the oil price.

The correlation between the 7*3)2 and oil price is found to be high A1igure%./F in the long term. 4 high coherency and cyclical effect is observed during crisis and

during the same period 7*3)2 is leading the oil price.

The correlation between the 7*3+G and oil price is found to be high during

all times A1igure %.F. It is also observed that, during crisis, 7*3+G is lagging the oilprice in the long term, whereas it is leading the oil price in medium term. 4nti-cyclical

effect is observed in the long term.

4 high coherency between the 7*3*


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from &$$E to &$%$. 4nti-(yclical effect is observed in the long term and 7*3T3 is

lagging the oil price in long term.

FIGURE 7: BENCHMARK OIL PRICES AND EXCHANGE RATES 2WTC3

FIGURES

FIGURE 787: BRENT OILVs8 USDEUR FIGURE 789: OPEC OIL Vs8 USDCN0

FIGURE 78: OPEC OIL Vs8 USDIDR FIGURE 78;: OPEC OIL Vs8 USDINR

7


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FIGURE78


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FIGURE78>: OPEC OILVs8 USDSGD FIGURE 78?: OPEC OIL Vs8 USDTR0

FIGURE78@: OPEC OILVs8 USDTWD

Note: Figure 1 presents the wavelet coherency plot between oil price and Exchange rate. Wavelet-

squared coherencies are indicated by contour, the ! signi"icance level is denoted by a dashed

blac# line contour and the area outside this line is the boundary a""ected $one. %he area a""ected byedge e""ects are denoted by the cone o" in"luence and the area outside the cone o" in"luence has no

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statistical signi"icance. %he color code "or coherency ranges "ro& blue 'close to $ero( to red 'closeto one(, where blue re"ers to low coherency and red re"ers to high coherency.

3uring financial crisis period, oil price is highly correlated with all the

exchange rates. The (hinese and *ingapore exchange rates have minimal coherency with

oil price in all fre!uencies except during crisis. *outh +orea and Taiwan exchange rates

are highly correlated with oil price at all times. 4 portfolio of smaller hori;on with oil asan asset can lead to better diversification by adding 7*3(02, 7*3I3G, 7*3I0G,

7*3)2, 7*3*


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The correlation between the 45 and oil price A1igure &.%%F is found in both

long and medium term. The correlation in medium term is high during &$$C and &$%$. In

the long term, high correlation is observed from &$$ to &$%& and 45 is lagging oilprice. 4 positive cyclical effect is observed from the graph.

4 high coherency is witnessed between the T* and oil price A1igure &.%&F in

the long term, particularly from &$$ to &$%&. 3uring this period, the T* is lagging

oil price in the long term.

The correlation between the 57 %$$ and oil price A1igure &.%#F is found to behigh in the long term, particularly during the period &$$ to &$%%. In the long term, 57

%$$ is lagging the oil price.

High coherency between the 89 '/ and oil price A1igure &.%'F is found in both

long and medium term. The correlation in medium term is high during crisis &$$E:&$$D.In the long term, high correlation is observed from &$$ to &$%$ and 89 '/ is lagging oil

price.

High coherency is witnessed between the 68 &$ and oil price A1igure &.%/F in

short, medium and long term. The high correlation in the long term is observed from&$$:&$%% and 68&$ is lagging oil price. 4 positive cyclical effect is also observed.

FIGURE 9: BENCHMARK OIL PRICES AND STOCK INDICES 2WTC3

FIGURES

FIGURE 987: WTI OILVs8 SPX FIGURE 989: OPEC OIL Vs8 SSE


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FIGURE 98: OPEC OIL Vs8 NK0 FIGURE 98;: OPEC OIL Vs8 NIFT0

FIGURE 98


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FIGURE 98>: BRENT OIL Vs8 CAC FIGURE 98?: BRENT OIL Vs8 IBEX

FIGURE 98@: OPEC OILVs8 FSSTI FIGURE 987: BRENT OIL VS8

FTSEMIB

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FIGURE 9877: BRENT OIL Vs8 AEX FIGURE 9879: OPEC OIL Vs8 TWSE

FIGURE 987: OPEC OILVs8 XU7 FIGURE 987;: OPEC OIL Vs8 L;

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001195-full-apcbss-2015-kuala-lumpur-srinivasan v.3.docx