syahira - mba

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NOOR SYAHIRA SURYA NOORDIN

OIL PRICE SHOCK

AND MALAYSIAN

SECTORAL STOCK MARKET RETURN


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Oil Price Shock and Malaysian Sectoral

Stock Market Return

Noor Syahira Surya Noordin

Bachelor of Law (Honours)

University of Kent at Canterbury

United Kingdom

1999

Submitted to the Graduate School of Business

Faculty Business and Accountancy

University of Malaya, in partial fulfillment

of the requirements for the Degree of

Master of Business Administration

July 2009


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ABSTRACT

This research is aimed at studying the linkages between the movements of the oil prices

with the Malaysian stock market return. Economic theories have established that oil price

causes chain reaction effects on the real economic activities. Also, oil price changes and

shocks is said to be one of the factors that influence the performance of the stock market. In

this paper, Vector Autoregresssion (VAR) approach was used to determine the impact of

the oil price changes on each Industry sector listed on Bursa Malaysia [formerly known as

Kuala Lumpur Stock Exchange (KLSE)] by way of analysing the trend of the return on the

Industry Indices, for the period 1 January 2003 to 8 April 2009. Daily data were used for

the analysis. The result failed to show any significant impact of the stock market return on

the eight (8) sectors in Bursa Malaysia given the shocks in the global crude oil price.

Granger causality test also shows uni-directional causality from oil price to the market

return on each respective sector .


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AKNOWLEDGEMENT

In the name of Allah, the Beneficent, the Merciful

I would like to express my deepest and sincere gratitude to my supervisor, Dr Gucharan

Singh for his detailed, constructive comments and kind words. He had also selflessly

sacrificed his Sundays to review and discuss the concept as well as the framework of this

thesis.

This thesis would not have been possible if it had not been for Ms Marliana Abbas, Dr

Ahmad Rafdi and Mr Aidil Zulkifli, who had provided assistance in numerous ways that

eventually led to the completion of this paper. I would like to thank all of them for their

help and I am truly grateful for their friendship and selflessness.

I wish to express my warm and sincere thanks to all the lecturers, course-mates and staff of

Graduate Business School, University Malaya for all the assistance given. I also sincerely

thank Dr. M. Abessi for his advice during the initial stages of this paper.

I dedicate this thesis to my parents, Datuk Dr Noordin Razak and Datin Siti Aminah

Ahmad who had unwearyingly poured their love and support in cheering me on to complete

the daunting task of completing this MBA programme.


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TABLE OF CONTENTS

ABSTRACT......................................................................................................................... II

AKNOWLEDGEMENT....................................................................................................III

LIST OF TABLES ........................................................................................................... VII

LIST OF FIGURES ........................................................................................................... IX

LIST OF APPENDICES .....................................................................................................X

LIST OF SYMBOLS AND ABBREVIATIONS ...............................................................X

INTRODUCTION................................................................................................................ 1

1.1 BACKGROUND OVERVIEW .......................................................................................... 1

1.1.1 OIL AS SOURCE OF ENERGY AND ITS PRICE MOVEMENTS ....................................... 1

1.2 PROBLEM STATEMENT ................................................................................................ 3

1.3 RESEARCH OBJECTIVES............................................................................................... 4

1.4 SCOPE OF THE STUDY.................................................................................................. 4

1.5 LIMITATIONS TO THE STUDY ....................................................................................... 5

1.6 SIGNIFICANCE OF THE STUDY...................................................................................... 6

LITERATURE REVIEW ................................................................................................... 8

2.0 INTRODUCTION ........................................................................................................... 8


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2.1 OIL AND THE ECONOMY.............................................................................................. 8

2.2 WORLD OIL PRICES................................................................................................... 10

2.3 OIL AND MALAYSIAN ECONOMY .............................................................................. 12

2.4 MALAYSIAN

OIL

MARKET

LANDSCAPE

.................................................................... 14

2.6 OIL, STOCK MARKET AND INDUSTRY SECTORS’ STOCK RETURNS............................ 17

2.5 CONCLUSION............................................................................................................. 25

DATA AND METHODOLOGY ...................................................................................... 27

3.0 INTRODUCTION ......................................................................................................... 27

3.1 THE DATA................................................................................................................. 27

3.2 RESEARCH METHODOLOGY ...................................................................................... 28

3.2.1 Initial Regression Process ............................................................................... 28

3.2.2 Unit Root Test ................................................................................................. 28

3.2.3 Cointegration ................................................................................................... 29

3.2.4 Vector Autoregressive...................................................................................... 30

3.2.5 Impulse Response Function and Variance Decomposition.............................. 32

3.2.6 Granger Causality ........................................................................................... 34

RESEARCH FINDINGS................................................................................................... 36

4.0 INTRODUCTION ......................................................................................................... 36

4.1 INITIAL FINDINGS ON THE RELATIONSHIP BETWEEN OIL PRICES AND MARKET

RETURNS ...............................................................................................................................


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4.2 STATIONARY TEST – UNIT ROOT TESTS ..................................................................... 37

4.3 COINTEGRATION ....................................................................................................... 39

4.4 VAR, IRF AND VD RESULT AND ANALYSIS ............................................................. 42

4.5 GRANGER

CAUSALITY

.............................................................................................. 53

4.6 CONCLUSION............................................................................................................. 56

SUMMARY AND CONCLUSION .................................................................................. 58

5.1 INTRODUCTION ......................................................................................................... 58

5.2 SUMMARY AND CONCLUSION ................................................................................... 58

5.3 FURTHER RESEARCH ................................................................................................. 60

REFERENCES................................................................................................................... 62

APPENDIX I…………………………………………………………………………….. 71

APPENDIX II……………………………………………………………………………. 72


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LIST OF TABLES

Table 1.1 : Total number of Listed companies in each Sector Index

Table 2.1 : List of Malaysian Major Exports (2008)

Table 2.2 : List of Malaysian Oil Exports and Imports (2008)

Table 4.1 : Initial Regression on the Relationship between Returns on Sectors,KLCI and Oil Prices

Table 4.2 : Unit Root: Level Term

Table 4.3 : Unit Root: First Difference

Table 4.4 : Co-Integration Rank of variable LNKLCON, LNKLCI and LNOILP

Table 4.5 : Co-Integration Rank of variable LNKLCSU, LNKLCI and LNOILP

Table 4.6 : Co-Integration Rank of variable LNKLFIN, LNKLCI and LNOILP

Table 4.7 : Co-Integration Rank of variable LNKLIND, LNKLCI and LNOILP

Table 4.8 : Co-Integration Rank of variable LNKLPLN, LNKLCI and LNOILP

Table 4.9 : Co-Integration Rank of variable LNKLPRO, LNKLCI and LNOILP

Table 4.10 : Co-Integration Rank of variable LNKLPRP, LNKLCI and LNOILP

Table 4.11 : Co-Integration Rank of variable LNKLSER, LNKLCI and LNOILP

Table 4.12 : Co-Integration Rank of variable LNKLTEC, LNKLCI and LNOILP

Table 4.13 : VAR Estimates for Construction Sector

Table 4.14 : VAR Estimates for Consumer Products Sector

Table 4.15 : VAR Estimates for Finance Sector


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Table 4.16 : VAR Estimates for Industrial Sector

Table 4.17 : VAR Estimates for Plantation Sector

Table 4.18 : VAR Estimates for Industrial Products Sector

Table 4.19 : VAR Estimates for Trading and Services Sector

Table 4.20 : VAR Estimates for Technology Sector

Table 4.21 : Granger Causality Test for Construction Sector

Table 4.22 : Granger Causality Test for Consumer Products Sector

Table 4.23 : Granger Causality Test for Finance Sector

Table 4.24 : Granger Causality Test for Industrial Sector

Table 4.25 : Granger Causality Test for Plantation Sector

Table 4.26 : Granger Causality Test for Industrial Products Sector

Table 4.27 : Granger Causality Test for Trading and Services Sector

Table 4.28 : Granger Causality Test for Technology Sector


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LIST OF FIGURES

Figure 2.1 : World crude oil price from 1.1.2003 to 31.12.2008

Figure 2:2 : Malaysian Retail Fuel Price Movement 2003 – 2008

Figure 4.1 : Movements of the indices from 31 December 2002 to 8 April 2009

Figure 4.2 : Impulse Response for Construction Sector

Figure 4.3 : Impulse Response for Consumer Products Sector

Figure 4.4 : Impulse Response for Finance Sector

Figure 4.5 : Impulse Response for Industrial Sector

Figure 4.6 : Impulse Response for Plantation Sector

Figure 4.7 : Impulse Response for Industrial Products Sector

Figure 4.8 : Impulse Response for Trading and Services Sector

Figure 4.9 : Impulse Response for Technology Sector

Figure 4.10 : Variance Decomposition for Construction Sector

Figure 4.11 Variance Decomposition for Consumer Products Sector

Figure 4.12 Variance Decomposition for Finance Sector

Figure 4.13 Variance Decomposition for Industrial Sector

Figure 4.14 Variance Decomposition for Plantation Sector

Figure 4.15 Variance Decomposition for Industrial Products Sector

Figure 4.16 Variance Decomposition for Trading and Services Sector

Figure 4.18 Variance Decomposition for Technology Sector


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LIST OF APPENDICES

Appendix I : List of companies in each Sector Index

Appendix II : Normal Distribution at first difference diagrams

LIST OF SYMBOLS AND ABBREVIATIONS

USD : US Dollars

MYR : Malaysian Ringgit

KLSE : Kuala Lumpur Stock Exchange (now known as Bursa Malaysia)

GNP : Gross National Product

GDP : Gross Domestic Product

CPI : Consumer Price Index


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CHAPTER 1

INTRODUCTION

1.1 Background Overview

1.1.1 Oil as Source of Energy and its Price Movements

Oil represents the most important macroeconomic factor in the world economy. It is one

of the main natural resources that fuel energy in the world. The price of oil and

petroleum products are determined by the international market based on the forces of

demand and supply. When there is a large increase in oil prices in the world market, it

affects the price of petroleum products across the world including Malaysia.

The price of oil has generally been relatively stable over the years with prices of crude

oil floating at an average of USD 30 to USD 40 from mid- 1980s to 2004.

Notwithstanding that, there are a number of price fluctuations during certain periods due

to the several factors such as oil price shock in 1975 and Gulf War in 1992.

July 2008 saw the price peaked to USD 147 per barrel. Many theories have been put

forward that contribute to the escalating crude oil price, which include increase in world

demand especially by the United States, China, India and other developing countries,

decline in petroleum reserves, unstable geopolitics conditions in several Organization of

the Petroleum Exporting Countries (OPEC countries), tension in the Middle East as

well as oil price speculations in the futures trading market.

Market analysts and even people on the streets believes that the fuel price hike

experienced by the world at large in late 2007 until mid-2008 posed as a challenge


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especially to businesses that rely heavily on fuel as a source of energy. In fact, other

businesses were also indirectly affected including the consumers where they were of the

notion that the increase in the price of the retail petroleum products could cause a

general increase in the price of goods and services in the market.

Due to the large increase in the world crude oil price, Malaysia who has been enjoying

relatively cheap fuel prices over the years had to succumb to a substantial price increase

in petroleum products, including petrol and diesel. In June 2008, Malaysian

Government increased the petrol price from RM 1.92 per liter to RM 2.70 per liter,

almost a 41% price hike. Diesel was increased by over 60% from RM 1.58 to RM 2.58

per liter. The hefty price hike was a result of the ballooning increase in the crude oil

prices which was hovering at a production rate of more than $100 per barrel in May

2008. The crude oil price increase had forced the Malaysian government to eventually

pass the escalating cost to the consumers. The new price provoked strong reactions from

both the corporations and consumers.

In August 2008, the world crude oil price began to decline. Consequently, Malaysian

government effectively reduced the retail price of both petrol and diesel seven times

between 23 August 2008 and 16 December 2008 from RM 2.70 per litre and eventually

rested at RM 1.90 per liter for petrol, whilst diesel price dropped from MYR 2.58 to

MYR 1.70 per liter. The decision was made by the Government in line with lower

global crude oil price and also due to the public and political pressures.

There have been numerous studies in both finance and energy literature on the

relationship between the oil price shock and the economy as well as the stock market in

many developed countries. However, studies focusing on developing countries or


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emerging markets are rather limited and there has yet to be any study conducted on the

Malaysian stock market.

This study aims at investigating whether the movements of oil prices have an impact on

the Malaysian stock market. In addition to that, it is also aimed at looking into the

impact of the oil price movements in individual industry sectors in Malaysia.

1.2 Problem Statement

Over the centuries, oil has been regarded as the major source of energy. Businesses rely

on petroleum products to fuel energy especially for transportations and the running of

machineries for their day to day operations and activities.

Due to the immense oil price hike in the world which affected the Malaysian retail fuel

prices in mid-2008, businesses and companies feared that the price hike would

negatively impact their businesses and the consumers feared that there would be a

general increase in prices of consumer goods and services.

The question that arises is whether the oil price shock will affect the performance of the

return on the stock market that will eventually translate the performance on the

businesses. Many analyst has predict that given the state of volatility in the oil price

movement in recent years, the movement in the stock market return will be also

affected. However, the question remains on how this price movement will affect the

stock market return for individual sectors in Bursa Malaysia. Given various opinions by

analyst in Malaysia and a lack of empirical studies, this research will aim to answer this

question.


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1.3 Research Objectives

This study aspires to validate the public and investors perceptions that the oil price hike

does essentially affect the stock market return. It will also analyse the industry sectors in

Malaysia stock market movement that will be affected by the movement in oil price by

world crude oil price. Thus, the objectives of this research are as follows:

(a) To determine the effect of global oil price shock on the growth in the stock

market return.

(b) To validate the theories and perceptions that oil price movements will affect

stock returns on the sectors in Bursa Malaysia, regardless of whether fuel is

directly or indirectly used as main input in the business operations and value

chain.

1.4 Scope of the Study

This study is focused at analysing the impact of oil price movements on individual

industry sector listed on the Bursa Malaysia stock market. This will be done by studying

the indices, which are regarded as the indicator that represents the entire group of which

it represents. Each index comprises companies that have large market capitalization. A

portfolio encompassing all possible securities would be too broad to measure. Hence,

proxies such as stock indices have been developed to serve as indicators of the overall

market's performance.

For the purpose of this analysing the industry sectors, concentration is on the daily

indices of the following respective industry sector:


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Table 1.1: Total number of listed companies in each Sector Index

No Sectors No of companies

1 Construction 42

2 Consumer Products 86

3 Finance 40

4 Industrial 26

5 Plantation 40

6 Industrial Products 152

7 Property 87

8 Trading/Services 144

9 Technology 22

Source: Author’s compilation

The detailed list of the companies is as shown in Appendix I.

1.5 Limitations to the Study

Despite objective of determining and recognizing the relationship between the oil price

changes and industry sectors’ stock returns, this study is subject to several limitations.

Firstly, the study only considered the industry sector indices in Bursa Malaysia. It does

not study individual company listed on the stock exchange or private companies. Thus,

the research does not indicate which company that was badly affected by the oil price

crisis.

Secondly, the industry indices comprise of companies from an array of principal

activities and business. For example, the Trading/Services Index comprise companies

involve in a diversity of business that vary from transportation to media. Thus, it cannot


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be established for certain how the oil price shock impact which sector specific.

Thirdly, there are other factors that can be attributed to the volatility of the stock

market. These factors include political stability. Malaysia experienced political turmoil

in the years 2003 to 2008 where there was a change of Prime Minister and cabinet line-

up in October 2003. The 12th general election was held in March 2008 and the ruling

party had lost many parliamentary seats as well as control over several states. This

could also be one of the reasons that impede investors’ confidence.

Finally, the United States was experiencing an economic financial crisis due to sub-

prime issue during the periods of 2007 and 2008. This could also impact investors’

confidence especially when the Malaysian export market and several financial

institutions can be said to be partly affected. The deteriorating investor confidence could

negatively affect the Malaysian stock market.

1.6 Significance of the Study

This study analyses the impact of the oil price changes on various industry indices listed

on the Malaysian stock market. The result of this study will assist the management of

the companies in Malaysia to be sufficiently prepared for any recurrence of oil price

crisis that will impact the world economy. The companies may redirect their business

model in preparation for the economic volatility. The company may also have a head-

start in making decisions in operational and capital expenditure by using best industry

practices without the need to reinvent the wheel. The success of every business entity

depends very much on its strategic plan.


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Secondly, the policy makers may formulate measures in times of economic turmoil due

to oil price shocks which may include adopting both monetary and fiscal policies to

spur economic growth, stabilize inflation and unemployment rate.

Thirdly, the outcome of this study will assist investment decision making of equity

investors in choosing counters or companies on the Malaysian stock exchange during

economic downturn due to oil price crisis. In addition to that, it will also assist the

private equity companies and venture capitalist in choosing the industry sector they opt

to invest in.

The rest of the paper is organized as follows:

Chapter 2 lays out a review of past relevant literatures, which covers the importance of

oil to the economy in general, the price movements of oil over the years, an overview of

oil landscape in Malaysia and the impact of oil price shock on the stock market. Chapter

3 outlines the research model as well as the types of data collected and used for the

study. The data analysis and findings are presented and discussed in Chapter 4, whilst

the discussion and conclusion of the findings are laid out in Chapter 5.


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CHAPTER 2

LITERATURE REVIEW

2.0 Introduction

Oil is one of the most vital macroeconomic factors. There have been numerous studies

and discussions on the relationship of macroeconomic factors and its volatility impact

on the world economy. The interrelation between economic variables, in particular oil

has received considerable exposure.

2.1 Oil and the Economy

Oil plays a major role in determining the economic condition of a country, be it

developed or developing country. A large number of researches have been conducted on

developed country such of U.S and found that its economy had been largely affected by

oil price shocks. Using a seven-variable VAR system, Hamilton (1983) examined the

impact of oil price shocks on the U.S economy from 1949 to 1972. He found that oil

price increase was the main cause of U.S post World War II recession. He also found a

statistically significant correlation between oil shock and that there is an asymmetric

relationship between oil prices and economic activity. Granger causality test was

conducted and found that changes in oil prices Granger-caused changes in GNP

whereby oil price increases are much more influential than oil price decreases. Various

researches supported Hamilton’s findings, which include Burbridge and Harrison

(1984) and Gisser and Goodwin (1986).

With regards to developing countries, as modernization combs through a country and

transform it into a more urban landscape, the need for oil and energy increase. This is


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especially if the country leaps into industrial production as one of its main source of

income. Basher & Sadorsky (2006) investigated the relationship between oil price risk

and emerging stock market returns and found that oil price has more impact on

emerging markets than on developed countries. Emerging economies tend to be more

energy intensive than more advanced economies and are therefore more exposed to

higher oil prices. Eryigit (2009) concurred with the findings and also found that the oil

price changes affect emerging market economies more than the developed markets.

They opined that this was because, developed countries are more aware of the danger in

air pollution and tend to switch to alternative technologies that do not use oil as the

main source of energy. Another contributing factor was because developed countries

had moved their production sites to developing or under-developed countries.

Energy, financial markets and the economy are all explicitly linked together on a

country's path of economic growth (Sadorsky, 2006). In a more recent study,

McSweeney and Worthington (2008) revealed that crude oil as an energy source is a

vital component that determines the condition of the world economy. Its price

movement impacts all industry sectors whether directly or indirectly, be it in banking,

energy, retailing or transportation industries.

Cologni and Manera (2009) however found that the role of oil shocks in explaining

recessions has decreased over time in G7 countries. The change in the relationship

between oil prices and real activity in recent years from earlier decades is attributed to

several causes including improvements in energy efficiency and in the conduct of

monetary and fiscal authorities.


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2.2 World Oil Prices

Basher & Sadorsky (2006) regarded oil as lifeblood of modern economics. It was stated

that if there is an increase in oil demand but there are no increases in supply, there will

be an increase in oil prices. This is the basis of the power of demand and supply.

Hamilton (2009) had conducted a study to investigate the factors that caused oil prices

to rise so spectacularly in 2007 to 2008 and subsequently declined even more

dramatically afterwards, which he had presented the paper at the Brookings Conference.

One of the factors was the high in demand that is associated with high growth and

stagnating supply. He revealed that the world real GDP increased by 9.4% between

2003 and 2005. That growth in world income was the primary cause behind an increase

in world petroleum consumption of 5 million barrels per day between 2003 and 2005, a

6% increase over the two years. The next two years (2006 and 2007) saw even faster

economic growth (10.1% cumulative two-year growth), with Chinese oil consumption

alone increasing 870,000 barrels per day. Yet between 2005 and 2007, global oil

production stagnated. The fall in the oil prices was a result of fall in demand. For

example, between the third quarter of 2007 and the third quarter of 2008, U.S.

petroleum consumption fell by 8.8%. The said drop in U.S. petroleum consumption

unambiguously represented the combined effects of lower income and price-induced

changes in use.

According to the statistics recorded by the United States Department of Energy, the

crude oil price has been maintained at the range of USD 25 per barrel from the mid-

1980s to 2003. In late 2003, the price increased to USD 30 per barrel and reached USD

60 per barrel by August 2005. Eventually, it peaked at USD 147 in July 2008. As stated


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in Chapter 1, many factors were theorized to have contributed to the escalating crude oil

price.

Set below is the diagram that shows the movement of world crude oil price from

January 2003 to December 2008.

Figure 2.1:

World crude oil price from 1.1.2003 to 31.12.2008

Source: United States Department of Energy -http://tonto.eia.doe.gov/dnav/pet/hist/wtotusaw.htm

Higher energy or fuel costs have been speculated and theorized to drive up operating

costs of companies and most industries braced for erosion in earnings. There will be a

definite increase in transportation costs and import costs and this will ultimately cause a

spiral effect of increase in overall prices of goods and services. This is because the

operators or suppliers will pass on the costs to the consumers. The high fuel prices will

also pose as deterrent factor for consumers to spend. This is due to the fact that


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consumers may find ways to find cheaper alternatives and change their lifestyles to save

a portion of what is left of their disposable income.

2.3 Oil and Malaysian Economy

There has not been much study done with regards to oil price shock and the Malaysian

economy. Malaysia which can be regarded as an emerging economy endowed with rich

natural resources including oil. It is a significant exporter of petroleum in the South East

Asia region.

Past researchers have found that developing countries tend to demand oil more than

developed countries. Like most developing countries, Malaysia had started out as an

agro-based economy after its independence in 1957. By mid-1980s, manufacturing had

overtaken agriculture as the main contributor towards the country’s GDP.

Manufacturing has long been recognised for its role as an “engine of growth” in the

development process. The rapid expansion of manufacturing sector contributes to the

high demand for oil in developing countries.

Malaysia is both an exporter and importer of oil. Crude petroleum accounts for the third

major exports of an average of 18 million metric tons annually. This can be seen in

Table 2.1 and Table 2.2 below.

Based on the UN Data and Malaysian Department of Statistics’ records, Malaysia is

also an importer of oil with an average of 7.9 million metric tons per year. In other

words, Malaysia is the net exporter of oil in the world.


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Table 2.1:

List of Malaysian Major Exports (2008)

Table 2.2:

List of Malaysian Oil Exports and Imports (2008)

Year IMPORTS EXPORTS

2003 7,861 18,288

2004 7,885 18,354

2005 7,685 18,596

2006 8,197 19,401

Source: http://data.un.org

The reason as to why the country opts to export its oil rather than consuming it for

own use is because of the premium quality of the oil produced. The oil extracted from


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the Malaysian soil has low sulphur content of less than 0.5%. It made more economical

sense to sell it and make profits for the country and in return buy a different quality of

oil with lower costs. Notwithstanding the fact that Malaysia in one of the exports for oil,

it is still relatively a small player in the industry with not more than 1% market share.

Hence, Malaysia is not in a position to influence the world oil price and therefore has to

succumb to the world oil price changes.

Global oil prices have risen markedly over the past 18 months. Cities that are highly

dependent on petroleum for urban transportations are likely to be most adversely

affected by the rising oil prices. Malaysia especially is highly dependent on petrol and

diesel as source of energy for transportation. The majority of the people rely heavily on

own personal vehicles due to the poor public transportation services in the country.

The petrol and diesel price hike fueled inflationary pressures that affected an array of

sectors of the economy, from consumers to the automotive, plantation and banking

sectors as well as highway industry. Malaysian Consumer Price Index (CPI) for January

to July 2008 increased by 4.4 per cent to 109.8 compared with that of 105.2 in the same

period last year. Compared with that of the same month in 2007, the CPI increased by

8.5% from 105.7 to 114.7 and when compared with the previous month, the CPI

increased by 1.1%. Among the contributing factors for this increase were the substantial

rise in the electricity tariff announced by the Government commencing 1 July 2008 and

the knock-on effect from the price increase of petrol and diesel (DOS, 2008).

2.4 Malaysian Oil Market Landscape

Since 1983, the government of Malaysia has strived to ensure that the prices of

petroleum products such as petrol, diesel and gas were kept low. The government has


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been maintaining relatively low fuel prices compared to other countries in the South

East Asia region.

Both the supply and prices of these items have been declared as controlled items under

the Control of Supplies Act 1961. An Automatic Pricing Mechanism was introduced in

1983 to stabilise the fuel prices and to maintain it at low retail prices by way of subsidy

and exempting sales taxes on the items. The retail prices will only be changed at the

discretion of the government, if the difference in price exceeds the threshold of the tax

and subsidy.

Subsidy is a common method used by developing countries to spur economic growth

and to curb inflation. To ease the burden on consumers and motorists, Malaysian

government resorted to fuel subsidies. Malaysia has been subsidising diesel since

October 1999 and petrol since June 2005. For the year 2007, fuel subsidy cost the

government RM 8.77 billion with crude oil price averaging USD 79.00 per barrel.

Malaysia in general does not experience retail price fluctuations due to the forces of

consumers’ demand and producers’ supply as experienced by other countries. Australia

for example, its petrol price fluctuates in accordance to consumer behaviour. Mitchell

et. al (2000) found that retail petrol price has some relationship with the weather

whereby Australians prefer to use motor vehicles on sunny days and this consequently

increases the demand for petrol.

The prices of petrol and diesel in Malaysia were reviewed from time to time by the

government by using the price of the world crude oil as a benchmark. When the world

oil prices began to increase, the government announced that it would have to review the


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fuel price subsidy if the world crude oil price hit above USD 100 per barrel. The crude

oil price hit USD 94.00 per barrel late 2007.

In 2008, the fuel subsidy rose to RM 18.31 billion based on the assumption that price

remained at USD 105 per barrel. However, the first week of June 2008 saw the crude oil

price reached USD 127 per barrel. Hence, the government had under-budgeted the

subsidy allocation. With that, Malaysian Government was no longer able to sustain the

subsidy over the petrol and diesel price, and therefore was forced pass the costs to

consumers.

In June 2008, the nation was stunned when the Malaysian Government increased the

petrol price from RM 1.92 per liter to RM 2.70 per liter, almost a 41% price hike. Diesel

was increased by over 60% from RM 1.58 to RM 2.58 per liter. The hefty price hike

was a result of a ballooning increase in the crude oil prices which was hovering at a

production rate of more than $100 per barrel in May 2008. The crude oil price increase

had forced the Malaysian government to eventually pass the escalating cost to the

consumers. The new price provoked strong reactions from both the corporations and

consumers.

In August 2008, the world crude oil price began to decline. Consequently, Malaysian

government effectively reduced the retail price of both petrol and diesel seven times

between 23 August 2008 and 16 December 2008 from RM 2.70 per liter and eventually

rested at RM 1.90 per liter for petrol, whilst diesel price dropped from RM 2.58 to RM

1.70 per liter. The decision was made by the Government in line with low global crude

oil price and also due to the public and political pressures.


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Set below is the diagram that shows the movement of Malaysian retail petrol and diesel

prices from 1 January 2003 to 31 December 2008.

Figure 2.2:

Malaysian Retail Fuel Price Movement 2003 - 2008

2.6 Oil, Stock Market and Industry Sectors’ Stock Returns

Economic theories and past researchers have found that there is a linkage between oil

price and the stock market whereby oil price shock affects the macroeconomic and

ultimately equity returns. This is because, oil price shocks adversely affect real output

and thereby have an adverse effect on corporate profits where oil is used as an input.

Jones (2004, pp. 24) stated:

Source: Malaysia Energy Database and Information System


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“ Ideally, stock values reflect the market's best estimate of the future profitability of

firms, so the effect of oil price shocks on the stock market is a meaningful and useful

measure of their economic impact. Since asset prices are the present discounted value

of the future net earnings of firms, both the current and expected future impacts of an

oil price shock should be absorbed fairly quickly into stock prices and returns without

having to wait for those impacts to actually occur ’.

Like any other goods and services, the simple demand and supply rule is also applicable

for oil prices. If there is a demand surplus for oil, this leads to higher oil prices. Basher

& Sadorsky (2006) states that if price of oil increases, it will act similar to inflation tax.

Accordingly two scenarios will emerge, firstly consumers strive to find cheaper

alternative energies, and secondly, costs of non-oil producing companies will increase

and this increases risks and uncertainties which in turn will negatively affect the stock

prices and reduces wealth and investment. Basher & Sadorsky (2006) linked the

relationship of oil shock and stock prices by assessing the impact on non-oil producing

companies which were not able to fully pass the increasing costs to consumers, and they

found that reduction of profits and dividends due to increase on costs were the key

drivers that affected stock prices.

Cong, Wei, Jiao and Ying Fan (2008) investigated the interactive relationships between

oil price shocks and the Chinese stock market using multivariate vector auto-regression.

China’s role in the world oil market is said to have become more important. Since 2003,

China has taken the place of Japan to be the second world oil consumer. They found

that oil price shocks do not show statistically significant impact on the real stock returns

of most Chinese stock market indices, except for manufacturing index and some oil

companies. Some “important” oil price shocks depressed oil company stock prices.


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Increase in oil volatility may have increased speculations in mining index and

petrochemicals index, which raise their stock returns.

Nandha & Faff (2008) conducted a study that examined the impact of oil price changes

on 35 industry sectors based on the standard FTSE Global Classification System. They

found that oil price changes have a negative impact on equity returns from all industries,

with the exception of mining, and oil and gas. They were of the opinion that the broad

oil price impacted across industries, because crude oil has a huge array of by-products,

which find applications from aviation fuel through to shampoo and shoes. Moreover,

higher oil prices might have an impact on interest rates and discourage consumer

confidence, creating indirect channels for reflecting higher oil prices into equity prices.

Their analysis had demonstrated that oil price increases and decreases have a symmetric

impact on the equity markets.

Huang (1996) however found evidence that suggested that oil futures returns do lead to

individual oil company stock return but oil future returns do not have impact on general

market indices. His study had examined the link between daily oil future returns and

daily United States stock return.

Those countries which are highly dependent on oil such as the Gulf States may feel the

direct impact of oil prices. They may be at an advantage due to increase in the world

price. For Gulf Cooperation Council (GCC) countries, oil exports largely determine

their foreign earnings and their governments’ budget revenues and expenditures. The

risk from price changes plays a key role in the development of these countries and their

financial markets. In a study conducted by Maghyereh and Al-Kandari (2007), they

disputed prior findings which stated that oil prices and GCC stock markets are not


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related which is not justifiable given the importance of oil prices on the economy of

these countries. They argued that the findings of previous studies failed to detect the

relationship because only linear linkages were examined.

Maghyereh and Al-Kandari (2007) had used alternative tests whereby they had adopted

application of rank tests for a nonlinear cointegration relationship between oil price and

the stock markets in GCC countries. Their empirical analysis supported that oil price

impacts the stock price indices in GCC countries in a nonlinear fashion. This analysis is

consistent with some authors, such as Mork (1989), Mork et al. (1994), and Hamilton

(1996, 2000). The significance of the result of their study had given insight to the policy

makers of the GCC countries, whereby they should therefore keep an eye on the effects

of changes in oil price levels on their own economies and stock markets. For individual

and institutional investors, the nonlinear relationship between oil and stock markets

implies predictability in the GCC stock markets.

Using multivariate vector autoregressive model (VAR analysis) and monthly time-series

data to test the dynamic relations between macroeconomic variables and stock returns in

Greece, Papapetrou & Hondroyiannis (2001) had found a positive oil price shock

depresses real stock return. The macroeconomic variables such as industrial production,

interest rate, exchange rate and oil prices were examined to study the causal relationship

between the economic activity movements and the performance of the stock market of

Greece. The major finding of the research was the domestic macroeconomic activity

affects the performance of the domestic stock market. It was also found that oil price

changes are also important in explaining stock price movements. The initial test

conducted to examine for the presence of unit root based on Dickey Fuller, was to

investigate the degree of integration of the variables used in the empirical analysis.


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The Johansen maximum likelihood approach was also applied to test for cointegration

among the variables.

Jones and Kaul (1996) studied the reaction of the stock markets to oil shocks during the

postwar period in Canada, Japan, United Kingdom and United States of America. They

had used current and future changes of the real cash flows and/or changes in expected

returns in conducting their research. Their findings was that, for United States of

America and Canada, the reaction of stock prices to oil shocks can be completely

accounted for by the impact of these shocks on real cash flows alone. Whereas, in

United Kingdom and Japan, changes in the oil prices seem to cause larger changes in

stock prices than can be justified by subsequent changes in real cash flows or by

changing expected returns. It was also found that there is a negative relation between

changes in oil price and stock returns. In other words, their findings indicate that oil

price changes have a detrimental impact on output and real stock returns in all four

countries.

The findings by Jones and Kaul were concurred by Sadorsky (1999) where the dynamic

interaction between oil prices and other economic variables including stock returns.

Four-variable unrestricted VAR framework using monthly time series data for the

period 1947 to 1996 were used. The four-variables include the natural logarithms of

industrial production as a measure of output, 3-month T-bill rate as interest rates, real

oil prices as an oil measure.

Sadorsky had the sample period split at 1986. He had investigated the oil price shock

prior to 1986 and after 1986. He had used variance decomposition and found that oil

price shocks account for a larger portion of the stock return forecast error variance in


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the second sub-sample (1986 – 1996). He indicated that this suggested that there might

have been a change in oil price dynamics. Upon analyzing the data, Sadorsky

acknowledged that the real oil price had been fairly stable price trend, started to increase

in the mid-1970’s and marked a major decline in 1986. The early 1990’s marked oil

price decreases, possibly due to the Gulf War.

Faff and Brailsford (1999) studied the sensitivity of oil price factor and the Australian

stock market during the period of 1983 to 1996. They had hypothesize that there are

four industries in which oil price changes are expected to have a net impact on revenue

of the companies. The industries are gold, solid fuels, oil and gas, and diversified

resources. They found that there is significant positive oil price sensitivity in the Oil and

Gas and Diversified Resources industries. They had also found that negative oil price

sensitivity is be greatest in industries with a relatively high proportion of their costs

devoted to oil-based inputs such as Transportation industries. However, they had

predicted the negative sensitivity may be due to the fact that the companies may have

passed on higher fuel costs to their customers by increasing prices of their goods and

services.

The findings of positive and negative effect of oil price changes on specific industry

basis implied that analysis at the aggregate market level may hide industry sector

effects. Although the oil prices may impact the industry sector, other factors contributed

to the well being of the industry. Countries that are rich in natural resources and not

dependent on imports of these resources may be at an advantage over those countries

that lack such resources. Markets with different concentrations of particular natural

resources and industrial sectors may experience differential aggregate effects (Faff and

Brailsford, 1999). In conducting their studies, the data that was used were


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continuously compounded monthly returns of 24 Australian companies, over the period

of July 1983 to March 1996. The companies were grouped based on Australian Stock

Exchange (ASX) industry groupings. Returns are calculated from the Price Relatives

File of the Center for Research in Finance (CRIF) at the Australian Graduate School of

Management. The proxy for the market portfolio used is a value-weighted domestic

index supplied by CRIF and a value-weighted global index supplied by Morgan

Stanley.

Jones and Kaul (1996) concurred with Faff and Brailsford whereby they discovered that

Canada which has a relatively high proportion of natural resource companies has the

weakest overall negative relationships between oil price shocks and stock returns. Japan

however, has a very low natural resources based. Hence, it was found that the overall

negative relationship between oil price shocks and stock return is strongest for Japan.

Eryigit (2009) also found that the price changes of oil or energy affect emerging

economies’ markets more than developed markets. He had studied the impact of oil

prices changes in both US Dollars and Turkish Lira on sub-sector indices in Istanbul

Stock Exchange. He had found that oil price changes are statistically significant positive

effects on trading and services, consumer products, industrial products, manufacturing

and financial sector covering insurance but do not have significant impact on

transportation and other financial sectors.

Grammenos and Arkoulis (2002) studied the relationship of global macroeconomic

factors, which includes oil prices with a specific industry that is shipping stock returns

internationally for the period 1989 to 1998. They had included 36 shipping companies

which are listed on 10 stock exchanges worldwide. The objective of the research is to


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examine the long-run impact of several sources of global risk on international shipping

stock returns. They had formed a multi factors model using macroeconomic factors of

namely, exchange rates, global inflation, changes in oil prices, industrial production

growth and laid up tonnage, a factor specific to the shipping industry. In deriving the

returns of a company, the risk free interest rates were taken into account. It was found

that oil price and laid up tonnage are negatively related to shipping stock returns,

whereas the exchange rate exhibited a positive relationship. No significant relationship

was detected regarding the global measures of inflation and industrial production.

Choe (2002) studied how oil price shocks on another perspective i.e. oil price impact on

large and small firms between the periods of 1950 to 2000. The companies were divided

into sizes depending on the market capitalization of the companies. The empirical

results showed that the oil price shocks in general affect the stock returns of large firms

more than the smaller firms. The asymmetric analyses also showed that positive oil

price shocks have more significant effect on both large and small firms than do negative

shocks. Thus, in conducting this research, the market capitalization of the sample

companies is an essential consideration.

Sadorsky (2008) agreed with findings by Choe when he investigated the empirical

relationship between firm size, oil prices, and stock prices. The empirical results

showed that increases in firm size or oil prices reduce stock price returns. They also

found that changes in oil prices have an asymmetric effect on stock prices. Increases in

oil prices have a greater effect on stock returns than decreases in oil prices. It is also the

case that when asymmetric oil price changes are considered, the effect of firm size

shows up most pronounced for medium-sized firm. He had given an example of being

the middle child in a family; it is tough being a medium-sized firm. Medium-sized


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firms do not enjoy the production efficiency and financial leverage of large firms nor do

they have the flexibility and responsiveness of small firms. Thus, medium-sized firms

are more likely to be more adversely affected, in terms of stock prices, by changes in oil

prices.

Notwithstanding the impact of increase in oil prices on the economy and industries, the

increase in crude oil prices poses as advantage to alternative energy companies. This is

because the industries will switch to more lucrative alternatives as source of energy.

Past researchers find that rising oil prices are good for the financial performance of

alternative energy companies. Henriques and Sadorsky (2008) studied the alternative

energy and oil and how they impact the financial performance of the alternative

companies. However, they argued that, although it is widely accepted that rising oil

prices are good for the financial performance of alternative energy companies, there are

no measurement on just how sensitive the financial performance of alternative energy

companies are to changes in oil prices. They then developed four (4) variable vector

autoregression models and estimated in order to investigate the empirical relationship

between alternative energy stock prices, technology stock prices, oil prices, and interest

rates. His findings showed that technology stock prices and oil prices each individually

Granger cause the stock prices of alternative energy companies. He also found that a

shock to technology stock prices has a larger impact on alternative energy stock prices

than does a shock to oil prices.

2.5 Conclusion

Although the majority of past researchers studied the relation of macroeconomic factors

including oil prices and the economic activity and the general stock market or oil


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price shock on the returns of selective companies, to date no research have been

conducted on the specific impact of global crude oil change on Malaysian financial

market or specifically on the Malaysian stock market performance. The objective of this

research is to study the impact of global crude oil prices on various industries in

Malaysia which aims to become developed countries by the year 2020.


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CHAPTER 3

DATA AND METHODOLOGY

3.0 Introduction

In this research, we shall examine the impact of oil price changes and shocks on the

sector returns on the KLCI and its component in the main board. In doing so, the

following chapter will outline the data and methods in carrying out the examination.

3.1 The Data

The first step in developing a VAR model is to make a choice of variables that are

essential for analysis. The key variables; sector indices from the main board oil prices,

and KLCI indices are used in this research. Sector indices comprise of index from nine

(9) different sectors namely construction, consumer product, finance, industrial,

plantation, industrial product, property, trading and services and technology. These

indexes are obtained from the main board in Bursa Malaysia [previously known as

Kuala Lumpur Stock Exchange (“KLSE”)].

Crude oil commodity prices is classified under world oil prices which is the average real

oil price obtained from three main benchmark oil prices used in world trade, namely

West International or WTI, Brent of Europe and Dubai of Middle East. The data were

obtained from Bloomberg through these channels.

This Kuala Lumpur Composite Index is derived from 100 companies that Bursa

Malaysia has chosen from a cross section of the total listed companies in Malaysia. This

Index is taken to be representative of Malaysian stock market's performance and thus


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provides us with a benchmark that reflects the growth of Malaysia's economy. The data

were obtained from Bursa Malaysia and Bloomberg.

3.2 Research Methodology

3.2.1 Initial Regression Process

The initial regression process to check on the significance of the impact of oil price is

modeled following Eryigit (2009). He uses a simple OLS regression to check on the

viability of the model. The sector indices (Y i ) are calculated from the difference logs of

the indices. Likewise, oil price (lnoilp) is calculated from the log difference of the world

oil prices at Malaysia ringgit (RM) and KLCI (lnklci) is calculated from the difference

log of KLCI respectively.

The purpose of this regression of the 5-days daily data is to provide an overall picture of

the relationship between the 9 sector indices and oil price shocks and KLCI. The

regression is stated as follows:

ln( yit ) − ln( y

it −1) = α i + β 1t ln(klci)it − ln(klci)it −1[ ]+ β 2t ln(oilp)it − ln(oilp)it −1[ ]+ ε i (3.1)

where: yit = index of sector i at time t

klci = index of Bursa Malaysia

oilp = Daily oil price (in RM)

3.2.2 Unit Root Test

Granger & Newbold (1974) asserted that for any time series to be used in econometrics

application, the time series must be stationary, whereby the notion of a spurious


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regression which they argued “ produces statistically significant results between series

that contain a trend and are otherwise random” was introduced. In other words,

regression in which the variables are non-stationary can lead to spurious result where

variables may share the same time trend even though they are not really related.

The unit roots test that is used in this study is Augmented Dickey-Fuller (ADF) test.

This is to determine whether the sample series (or its first or second difference) is

stationary. To confirm the unit root property of the sample data, Phillips-Perron (PP)

test shall also be employed to confirm the results of ADF test. The null hypothesis of

unit root test is the series contain a unit root. If the t-statistics is smaller than the critical

value, the null hypotheses is rejected i.e. the data has no unit root and is stationary.

3.2.3 Cointegration

Variables will be deemed to be cointegrated if they have a long term, or equilibrium

relationship between them. This means that the variables move together is similar trend

and do not wander off in opposite directions for very long without coming back to a

mean distance eventually. Gujarati in his book quoted Granger (1986) as saying:

“ A test for cointegration can be thought as a pre-test to avoid ‘spurious regression’

situations”.

If residuals are stationary, the two variables are said to be cointegrated and there is a

long run relationship between the two variables. However, if residuals are random walk,

the variables are not cointegrated. The notion of cointegration arose out of the concern

about spurious or nonsense regressions in time series. If economic time series variables

behave individually as nonstationary random walks, it often produces empirical


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results in which the R2 is quite high, but the Durbin-Watson statistic is quite low. The

results may be misleading and often result in misinterpretation.

Johansen’s (1988) cointegration testing framework is used in this study to determine the

absence or the presence of the cointegrating relationship among all test variables.

Although there exists a number of cointegration tests, such as the Engle and Granger

(1987) method and the Stock and Watson (1988) test, Johansen’s test has a number of

desirable properties, including the fact that all test variables are treated as endogenous

variables. This test is based on the null of no cointegration between oil prices and the

considered series. If the series are found to be cointegrated, Granger causality tests can

be implemented. If there is existence of a cointegrating relationship between two

variables, it means that at least one of the two variables Granger-causes the other.

3.2.4 Vector Autoregressive

Vector Autoregressive (VAR) is frequently used, although with considerable

controversy, for analysing the dynamic impact of different types of random disturbances

on systems of variables. The VAR is a linear model used for forecasting, impulse

responses and variance decomposition. The VAR technique is appropriate because its

ability to characterise the dynamic impact and/or structure of the model as well as its

ability to avoid imposition of excessive identifying restrictions associated with different

economics and finance theory. In other words, VAR does not require any explicit

econometric theory to be estimated.


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A system of vector autoregressive is written as:

A( L) Z (t ) = ε (t ) (3.2)

where Z (t ) is a n × 1 vector or covariance stationary non-deterministic variables. A( L)

is a n × n matrix polynomial in the lag operator, that is:

A( L) = 1 − ι 1 L − ... − ι n Ln (3.3)

ε (t ) is a n × 1 vector of random shocks or innovations with zero means and covariance

matrix Σ. The elements of Σ are assumed to have properties that of cov(ε it ,ε it − s ) = 0 for

i = 1,...,n and for t = s , and cov(ε it ,ε

jt − s) = 0 for i = j and t = s , i = 1,..., n

The VAR model specified here focuses on three variables for the 9 sectors: sector

market return ( ∆ yi ), KLCI market return ( ∆klci ) and oil price ( ∆oilp ). All variables

are in the log form. A general VAR formation is as follows:

∆ yi = c0 + b yy, j j = 0

p

∑ ∆ yit −1− j + b yk , j ∆klcit −1− j + j = 0

p

∑ b yo, j ∆oilpt −1− j j = 0

p

∑ (3.4)

∆klci = c0 + bky, j j =0

p

∑ ∆ yit −1− j + bkk , j ∆klcit −1− j + j = 0

p

∑ bko, j ∆oilpt −1− j j = 0

p

∑ (3.5)

∆oilp = c0

+ boy, j

j = 0

p

∑ ∆ y

it −1− j + b

ok , j∆klci

t −1− j +

j = 0

p

∑b

oo, j∆oilp

t −1− j j = 0

p

∑ (3.6)

The optimal lag order is chosen based on SIC. From the highest possible lag order, we

perform sequential testing to find minimum SIC values. SIC is given by:


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SIC = n loge

t

2

n

+ k log(n) (3.7)

where et 2 = sum square of residuals , k = number of parameter

The optimal lag chosen is subjected to the residual test to ensure the nonexistence of

serial autocorrelation. Number of lags should be long enough to capture the dynamics of

the system but not too long in order to save degree of freedom. The optimal lag order

will also used in the Granger Causality test.

3.2.5 Impulse Response Function and Variance Decomposition

To interpret the estimated coefficients of a VAR, impulse response function (IRF) and

variance decomposition (VD) are used. IRF allows us to analyse dynamics behaviour

while VD shows us the relative importance of each shock. The impulse response

functions give the dynamic response of each endogenous variable to a shock in the

system that is by generating a moving average representation of the system. The VAR

equation of (3.2) has a moving average representation:

Z (t ) = A( L)[ ]−1ε (t )

= B( L)ε (t )

= Bsε (t − s)s=0

∞

∑

(3.8)

where the normalisation of A(L),B is an identity matrix.

Rewriting the moving average representation of equation (3.8) in term of othogonalised

innovations yield the following equation:


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Z (t ) = H sv(t − s)s =0

∞

∑ (3.9)

The ith equation of system (4.9) is:

Z it

= hij (s)vij (t − s)

s = 0

k

∑ (3.10)

The term hij (s)s = 0

k

∑ represents the impulse response function of Z i with respect to an

innovation in Z j .

An impulse response function traces and the response of an endogenous variable to a

change in one of the innovations. Simulations for each of the aggregates are solved in

response to a 1 percent innovation of the respective aggregate. In other words, the IRF

is able to trace out the dynamic effect adjustments for the purpose of comparative

stability of the index market return, KLCI market return and oil prices.

IRF is also useful in providing the means to analyse the dynamic behaviour of the target

variables. If the innovations are not correlated with each other, interpretation is

straightforward. For a series with a unit root, the IRF never dies out; however, for a

trend stationary series, the IRF does die out. In any event, whether an individual time

series is trend stationary or has a unit root, the relative magnitude of the IRF across

different time horizons indicates the extent of the persistence of shocks to the individual

series.

The variance decomposition (VD) of a VAR gives information about the relative

importance of the random innovation. Various software has various calculations on


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VD but the main component is the forecast error of the variable for different forecast

horizons. The source of the forecast error is variation in the current and future values of

the innovations. One period ahead, all of the variation in a variable comes from its own

innovation. Again, this composition of variance depends critically on the ordering of

equations.

3.2.6 Granger Causality

The general definition of Granger Causality is defined as follows:

“The Granger approach to the questions whether X causes Y is to see how much the

current Y can be explained by past values of Y and then to see whether adding lagged

values of X can improve explanation. Y is said to be Granger-Caused by X if X helps in

the prediction of Y, equivalently if the coefficients on the lagged Xs are statistically

significant”

(E-views Guide)

In other words, the variable X does not ‘Granger’ cause Y if and only if the past values

of X do not explain Y. In terms of equation, in a regression of Y on other variables

(including its own past values), if we include past or lagged values of X, and it

significantly improves the prediction of Y, then we can conclude that X Granger causes

Y. The same applies if Y Granger causes X.

Granger causality test requires the null hypothesis of no causality being tested on a joint

test that the coefficients of the lagged causal variable are significantly different from

zero. The null hypothesis is that X does not Granger causes Y in the first regression


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and that Y does not Granger causes X in the second regression. There are four possible

causal relationships:

1. Independence is suggested when the set of X and Y coefficients are not

statistically significant in both regressions.

2. Unidirectional causality from X and Y exist if the estimated coefficients on the

lagged Y in equation (3.8) are statistically different from zero as a group (i.e.

Σα i =0) and the set of estimated coefficients on the lagged X in equation (3.9) is

not statistically different from zero (i.e. Σδ j =0).

3. Unidirectional causality from Y to X is indicated if the set of the lagged X in

equation (3.8) are statistically different from zero as a group (i.e. Σα i =0) and

the set of estimated coefficients on the lagged Y in equation (3.9) is not

statistically different from zero (i.e. Σδ j =0).


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CHAPTER 4

RESEARCH FINDINGS

4.0 Introduction

This chapter presents the results of the Ordinary Least Square (OLS) of the nine (9)

sector sample data, unit root test, Johansen cointegration test, and temporal relationship

results. This chapter will also show whether there are any granger causality.

4.1 Initial Findings on the relationship between oil prices and market returns

The results of the OLS for each sectors market return, market return for the KLCI and

oil prices are shown in Table 4.1 below.

Based on the result produced by E-Views, it is found that high R2 of all the regression

which ranging from 0.33 to 0.68 for all the sectors. In other words, around 33% to 68%

variation in the changes in the market return for each sectors are explained by the oil

prices and market return. In the regression, the return to KLCI is highly significant for

all sectors at 5% level. The oil price, on the other hand is only significant at 5% level

for plantation and industrial product and both of it is positively related to the sectors.


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Table 4.1: Initial Regression on the Relationship between Return on Sectors, KLCI an

** significant at 5% level

∆ ln klcon

∆ ln klcsu

∆ ln klfin

∆ ln klind

∆ ln klp ln

∆ ln klpro

∆ ln klprp

∆

Constant-0.00

(0.41)

0.00

(0.04)

-0.00

(0.88)

0.00

(0.29)

0.00

(0.09)

-0.00

(0.19)

-0.00

(0.22) (

∆ ln klci 1.29

(0.00)**

0.58

(0.00)**

1.05

(0.00)**

0.81

(0.00)**

1.08

(0.00)**

0.81

(0.00)**

0.96

(0.00)** (0

∆ lnoilp 0.02(0.13)

0.00(0.58)

0.00(0.90)

-0.01(0.33)

0.03(0.00)**

0.01(0.03)**

0.001(0.89) (

R2 0.65 0.58 0.80 0.70 0.56 0.68 0.59

F-stat1405.52

(0.00)

1059.55

(0.00)

3135.80

(0.00)

1766.64

(0.00)

496.18

(0.00)

1652.01

(0.00)

1113.71

(0.00)

75

(

DW 2.06 2.06 1.99 2.14 1.77 2.08 1.81


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4.2 Stationary Test – Unit root tests

Before identifying the potential long-run relationship among the variables included in

the model, the ADF and PP tests of unit root are conducted to verify the order of

integration of the time series involved. The lag length of the test (m) is set at 5 days.

The sample data collected for the purpose of this study have been plotted in Figures 4.1

from 31 December 2002 to 8 April 2009, for a total of 1544 observations. As can be

seen from the graphs, the movements of the indices are volatile with a majority of it

being at the peak between the ranges of 1200th

to 1500th

observations except for

technology sector, which illustrated a downward trend.

Figure 4.1:

Movements of the indices from 31 December 2002 to 8 April

2009

4.8

5.0

5.2

5.4

5.6

5.8

6.0

2 50 5 00 7 50 1000 1250 1500

LNKLCON

5.0

5.2

5.4

5.6

5.8

6.0

2 50 5 00 7 50 1000 1250 1500

LNKLCSU

8.4

8.6

8.8

9.0

9.2

9.4

2 50 5 00 7 50 1000 1250 1500

LNKLFIN

7.0

7.2

7.4

7.6

7.8

8.0

8.2

2 50 5 00 7 50 1000 1250 1500

LNKLIND

7.2

7.6

8.0

8.4

8.8

9.2

2 50 5 00 7 50 1000 1250 1500

LNKLPLN

4.0

4.2

4.4

4.6

4.8

5.0

2 50 5 00 7 50 1000 1250 1500

LNKLPRO

6.0

6.2

6.4

6.6

6.8

7.0

7.2

2 50 5 00 7 50 1000 1250 1500

LNKLPRP

4.4

4.6

4.8

5.0

5.2

5.4

2 50 5 00 7 50 1000 1250 1500

LNKLSER

2.0

2.5

3.0

3.5

4.0

4.5

2 50 5 00 7 50 1000 1250 1500

LNKLTEC

4.4

4.8

5.2

5.6

6.0

6.4

2 50 5 00 7 50 1000 1250 1500

LNOILP

6.4

6.6

6.8

7.0

7.2

7.4

2 50 5 00 7 50 1000 1250 1500

LNKLCI


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The test results for the time series variables on both levels and first difference are shown

in Table 4.2 and 4.3 respectively.

Table 4.2

Unit Root: Level Term

ADF PPVariables

T-Stat Lag-length T-Stat Bandwidth

lnklcon -1.40 1 -1.43 6

lnklcsu -1.65 1 -1.51 2

lnklfin -0.78 1 -2.06 3

lnklind -1.27 1 -1.39 4

lnklpln -0.95 1 -0.99 9

lnklpro -0.50 1 -0.57 10

lnklprp -1.15 1 -0.89 7

lnklser -0.69 1 -0.65 5

lnkltec -1.48 1 -1.29 6

* Represents significance level at 5%

Table 4.3

Unit Root: First Difference

ADF PPVariables

T-Stat Lag-length T-Stat Bandwidth

lnklcon -35.86* 0 -36.04* 4

lnklcsu -36.09* 0 -36.08* 2

lnklfin -34.84* 0 -34.93* 5

lnklind -36.97* 0 -36.97* 0

lnklpln -32.89* 0 -33.00* 4

lnklpro -34.88* 0 -35.25* 9

lnklprp -17.89* 2 -33.23* 3

lnklser -35.30* 0 -35.31* 3

lnkltec -24.36* 1 -35.59* 3

* Represents significance level at 5%


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The number of the lags for included was determined using the automatic selection of

Schwarz Information Criterion (SIC). As for the application of the PP test, the

bandwidth will have to be chosen as the parameter needed for estimating the residual

spectrum at frequency zero. The Newey–West (1994) data-based automatic bandwidth

parameter method was chosen for this study.

For each of the series, the levels of the series are considered first. The ADF test result

indicate that null hypothesis of a unit root ( H 0 :δ = 0 ) cannot be rejected in any series

at 5% level, thereby indicating that all series are non stationary on levels. This result has

been confirmed by PP test, which indicates consistency with the ADF test.

Subsequently, the ADF and PP tests are computed using first difference of the same

variables. The results of both tests indicate that all series are individually significant at

the 5% level, thus suggesting that null hypothesis of a unit root is rejected and that

series is stationary. Since all the series are found to be stationary at first difference, it is

concluded that the log of each sectors, log of KLCI (lnklci) and log of oil prices (lnoilp)

are integrated at order one, I(1).

4.3 Cointegration

After establishing the order of integration, i.e. all the series are I(1), the Johansen

cointegration test is therefore applied on these series to examine whether or not

cointegration exist among the variables for each sector. Since there three variables in

each of the sector, there can be at most two cointegrating vectors (r), so r could be equal

to 0, 1 or 2. Given the data used in the study are daily times series, up to 5 lags have

been included for the cointegration test. The results of Johansen test for cointegration is

presented in Table 4.4 to 4.12 for each sector.


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Based on the value of Trace and Eigenvalue statistics, the null hypothesis of no

cointegrating vector (r=0) cannot be rejected at 5% level for construction, consumer

product, finance, industrial plantation, industrial products, trading and services and

technology sector. As for property sector, the null hypothesis of one cointegrating

vector (r=1) cannot be rejected. The selection of the optimal lag length is based on VAR

for the Johansen procedures, the Schwanz Information Criteria (SIC), for a system of

equation are used.

Table 4.4: Co-Integration Rank of variable LNKLCON, LNKLCI and LNOILP

Trace Eigenvalue

Ho H1 Statistics 95% Ho H1 Statistics 95%

r = 0 r ≥ 1 27.45 29.79 r = 0 r≥ 1 17.76 21.13

r ≤ 1 r ≥ 1 9.69 15.49 r≤ 1 r ≥ 1 5.73 14.26

r ≤ 2 r ≥ 2 3.96 3.84 r≤ 2 r ≥ 2 3.97 3.84

* indicates significant at 5% level

Table 4.5: Co-Integration Rank of variable LNKLCSU, LNKLCI and LNOILP

Trace Eigenvalue


r = 0 r ≥ 1 19.36 29.80 r = 0 r≥ 1 14.25 21.13

r ≤ 1 r ≥ 1 5.11 15.50 r≤ 1 r ≥ 1 5.07 14.26

r ≤ 2 r ≥ 2 0.05 3.84 r≤ 2 r ≥ 2 0.05 3.84


Table 4.6: Co-Integration Rank of variable LNKLFIN, LNKLCI and LNOILP

Trace Eigenvalue


r = 0 r ≥ 1 27.00 29.80 r = 0 r≥ 1 13.74 21.13

r ≤ 1 r ≥ 1 13.26 15.50 r≤ 1 r ≥ 1 9.59 14.26

r ≤ 2 r ≥ 2 3.67 3.84 r≤ 2 r ≥ 2 3.67 3.84



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Table 4.7: Co-Integration Rank of variable LNKLIND, LNKLCI and LNOILP

Trace Eigenvalue


r = 0 r ≥ 1 23.99 29.80 r = 0 r≥ 1 14.54 21.13

r ≤ 1 r ≥ 1 9.45 15.50 r≤ 1 r ≥ 1 7.35 14.26

r ≤ 2 r ≥ 2 2.09 3.84 r≤ 2 r ≥ 2 2.09 3.84


Table 4.8: Co-Integration Rank of variable LNKLPLN, LNKLCI and LNOILP

Trace Eigenvalue


r = 0 r ≥ 1 24.02 29.80 r = 0 r≥ 1 16.99 21.13

r ≤ 1 r ≥ 1 7.02 15.50 r≤ 1 r ≥ 1 6.83 14.26

r ≤ 2 r ≥ 2 0.19 3.84 r≤ 2 r ≥ 2 0.19 3.84


Table 4.9: Co-Integration Rank of variable LNKLPRO, LNKLCI and LNOILP

Trace Eigenvalue


r = 0 r ≥ 1 20.42 29.80 r = 0 r≥ 1 13.12 21.13

r ≤ 1 r ≥ 1 7.304 15.50 r≤ 1 r ≥ 1 6.09 14.26

r ≤ 2 r ≥ 2 1.21 3.84 r≤ 2 r ≥ 2 1.21 3.84


Table 4.10: Co-Integration Rank of variable LNKLPRP, LNKLCI and LNOILP

Trace Eigenvalue


r = 0 r ≥ 1 32.46* 29.79 r = 0 r≥ 1 22.16* 21.13

r ≤ 1 r ≥ 1 10.30 15.49 r≤ 1 r ≥ 1 7.34 14.26

r ≤ 2 r ≥ 2 2.96 3.84 r≤ 2 r ≥ 2 2.96 3.84



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Table 4.11: Co-Integration Rank of variable LNKLSER, LNKLCI and LNOILP

Trace Eigenvalue


r = 0 r ≥ 1 22.86 29.80 r = 0 r≥ 1 14.81 21.13r ≤ 1 r ≥ 1 8.05 15.50 r≤ 1 r ≥ 1 5.21 14.26

r ≤ 2 r ≥ 2 2.84 3.84 r≤ 2 r ≥ 2 2.84 3.84


Table 4.12: Co-Integration Rank of variable LNKLTEC, LNKLCI and LNOILP

Trace Eigenvalue


r = 0 r ≥ 1 21.19 29.79 r = 0 r≥ 1 14.79 21.13

r ≤ 1 r ≥ 1 6.40 15.49 r≤ 1 r ≥ 1 6.41 14.26

r ≤ 2 r ≥ 2 0.00 3.84 r≤ 2 r ≥ 2 0.00 3.84


4.4 VAR, IRF and VD Result and Analysis

Based on the unit root and the cointegration tests, we did find that all the variables are

integrated at order one, I(1) and there is no cointegration for most of the sectors except

for property sector. Given this, to estimate VAR, the variables are required to be

transformed in a first difference. It is desirable for the variables to do so in VAR models

to gain an “asymptotic efficiency” of the VAR.

The estimates of the VAR for all the sectors (excluding the property sector) and the

respective t-values are presented in table 4.13 to 4.20. The property sector is excluded

from the analysis due to the fact that cointegration does exist in the model. Although


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the estimate of individual coefficient in VAR does not have straightforward

interpretation, a glance at the table generally shows that most of the past values of oil

prices do contribute in explaining the sector market returns.

Table 4.13: VAR Estimates for Construction Sector

∆ lnklcon ∆ lnklci ∆ lnoilp

∆ lnklcon(-1) 0.026[0.61]

0.04[ 1.70]

-0.03[-0.54]

∆ lnklci(-1) 0.16[ 1.69]

0.06[ 1.50]

0.079[ 0.79]

∆ lnoilp(-1) 0.04[ 2.24]*

0.05[ 4.51]*

-0.028[-1.08]

Notes: Figures in parentheses in the t-statistics

*significant at 5% level

Table 4.14: VAR Estimates Consumer for Product Sector

∆ lnklcsu ∆ lnklci ∆ lnoilp

∆ lnklcsu(-1) -0.02[-0.4]

0.03[ 0.55]

-0.26[-2.17]*

∆ lnklci(-1) 0.10[ 3.26]*

0.10[ 2.69]*

0.19[ 2.04]*

∆ lnoilp(-1) 0.03

[ 3.17]*

0.05

[ 4.57]*

-0.03

[-1.07]Notes: Figures in parentheses in the t-statistics


Table 4.15: VAR Estimates for Finance Sector

∆ lnklfin ∆ lnklci ∆ lnoilp

∆ lnklfin(-1) 0.06[1.13]

0.06[1.29]

-0.16[-1.43]

∆ lnklci(-1) 0.06

[0.96]

0.05

[0.97]

0.21

[ 1.55] ∆ lnoilp(-1) 0.05

[3.86]*0.05

[4.57]*-0.03[-1.10]

Notes: Figures in parentheses in the t-statistics*significant at 5% level


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Table 4.16: VAR Estimates for Industrial Sector

∆ lnklind ∆ lnklci ∆ lnoilp∆ lnklind(-1) -0.13

[-2.90]-0.08[-1.60]

-0.00[-0.04]

∆ lnklci(-1) 0.22[4.92]*

0.18[3.97]*

0.04[0.36]

∆ lnoilp(-1) 0.04[4.0]*

0.05[4.54]*

-0.03[-1.10]


Table 4.17: VAR Estimates for Plantation Sector

∆ lnklcpln ∆ lnklci ∆ lnoilp

∆ lnklpln(-1) 0.11[ 3.04]

-0.01[-0.61]

-0.06[-1.02]

∆ lnklci(-1) 0.09[ 1.55]

0.14[ 3.70]*

0.10[ 1.15]

∆ lnoilp(-1) 0.15[ 9.33]*

0.05[ 4.61]*

-0.03[-1.03]


Table 4.18: VAR Estimates for Industrial Product Sector

∆ lnklpro ∆ lnklci ∆ lnoilp

∆ lnklpro(-1) 0.02[0.48]

0.08[1.71]

0.11[1.02]

∆ lnklci(-1) 0.11[2.57]*

0.06[1.30]

-0.05[-0.50]

∆ lnoilp(-1) 0.04[3.88]*

0.05[4.48]*

-0.03[-1.16]



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Table 4.19: VAR Estimates for Trading and Services Sector

∆ lnklser ∆ lnklci ∆ lnoilp

∆ lnklser(-1) 0.19[2.28]

0.25[3.09]

0.356[1.89]

∆ lnklci(-1) -0.09[-1.09]

-0.12[-1.48]

-0.31[-1.62]

∆ lnoilp(-1) 0.03[2.77]*

0.045[4.54]*

-0.03[-1.14]


Table 4.20: VAR Estimates for Technology Sector

∆ lnkltec ∆ lnklci ∆ lnoilp

∆ lnkltec(-1) 0.089[2.87]

0.03[1.38]

-0.17[-2.35]

∆ lnklci(-1) 0.11[3.61]*

0.06[2.54]

0.04[0.79]

∆ lnoilp(-1) 0.03[0.63]

0.06[3.08]*

0.16[2.14]

Notes: Figures in parentheses in the t-statistics


The estimated coefficient of a VAR, however, is difficult to interpret. Hence, the

impulse response functions (IRF) and variance decomposition (VD) of the system were

analyzed to draw conclusion about the VAR. The impulse response functions of one

innovation measures the effect of one standard deviation shock today on current and

future values of endogenous variables. Meanwhile, the variance decomposition of the

VAR gives information about the relative importance of the random innovation.


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Figure 4.2: Impulse Response Function for Construction Sector

Figure 4.3: Impulse Response Function for Consumer Product Sector

Figure 4.4: Impulse Response Function for Finance Sector

8/20/2019 Syah

syahira - mba

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