EQUITY MARKET INTEGRATION IN SELECTED MARKETS: EVIDENCE FROM UNIT ROOT AND CO INTEGRATION TESTS

PROJECT REPORT

ON

“EQUITY MARKET INTEGRATION IN THE SELECTED

MARKETS: EVIDENCE FROM UNIT ROOT AND

COINTEGRATION TESTS”

SUBMITTED TO:

GURU GOBIND SINGH INDRAPRASTHA UNIVERSITY

In partial fulfilment of the requirement for the award of the degree ofBachelor of Business Administration

(2014-2017)

Supervised By Submitted By

MR. MAROOF AHMAD AMIT KUMAR(Faculty Guide) Enroll.No.00415501714

NEW DELHI INSITUTE OF MANAGEMENT

61, Tughlakabad Institutional Area, New Delhi-110062

i

ACKNOWLEDGEMENT

Any accomplishment requires the effort of many people and this work is not different.

Regardless of the source, I wish to express my gratitude to those who may have contributed

to this work, even though anonymously.

I am immensely grateful to the Almighty for bestowing me with so many blessings and

making me capable to stand where I am today.

At the successful and timely completion of this project, I would like to extend my heartfelt

gratitude and sincerest thanks to my Guide Mr.Maroof Ahmad, Asst. professor

NDIM,without whose insight and guidance, this project would not have become a distinct

reality. He not only helped me deciding a suitable topic for my work, but also guided me

throughout the project.

I would like to express my sincerest thanks to entire faculty of NDIM New Delhi for their

continuous guidance and co-operation.

I also owe profound gratitude to my parents, family members, teachers and friends who

encouraged me countless times to persevere through this entire process.

AMIT KUMAR

ii

Dated: 28/11/2015

CERTIFICATE

This is to certify that AMIT KUMAR, Enroll. No. 00415501714, a student of Bachelor of Business

Administration at New Delhi Institute of Management, affiliated to Guru Gobind Singh Indraprastha

University, New Delhi, has completed his project on the topic entitled “EQUITY MARKET

INTEGRATION IN THE SELECTED MARKETS: EVIDENCE FROM UNIT ROOT AND

COINTEGRATION TESTS”under my supervision and guidance.

To the best of my knowledge & belief the work is based on the investigations made, data collected

and analyzed by him and it has not been submitted in any other university or institution for award of

any degree or diploma.

MR. MAROOF AHMAD

Project Guide

iii

Mr Maroof AhmadAsst.

New Delhi Institute of ManagementAffiliated to Guru Gobind SinghIndraprastha University, New DelhiWeb: www.ndimdelhi.in

http://www.ndimdelhi.in/

TABLE OF CONTENTS

S.NO. PARTICULAR PAGE NO

Cover PageAcknowledgmentCertificateTable of Contents/IndexList of Tables and Graphs

iiiiiiiv

v-vii

1 Chapter 1: Introduction to Stock Markets1.1: Stock Market1.2: Stock Exchange1.3:Importance of Stock Exchange1.4:History

1-4

2 Chapter 2: Profile of the Stock Markets2.1: Stock Indices2.2: IndiaStock Exchange2.3: China Stock Exchange2.4:Taiwan Stock Exchange2.5:Hong Kong Stock Exchange

5-21

3 Chapter 3: Study of the selected Research Problem3.1: Introduction to Stock Market Integration3.2: Review of literature3.3: Research Methodology 3.3.1: Research Objectives3.3.2: Hypothesis 3.3.3 Sample Size3.4: Research design and methodology3.5: Application of the Research

22-35

4. Chapter 4: Data Analysis

4.1: Analysis of Data.36-57

5. Chapter 5: Summary & Conclusion 58-59

References and Websites Visited 60

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LIST OF TABLES

TITLEPAGE

NO.

TABLE 1: DESCRIPTIVE STATISTICS 37

TABLE 2: CORRELATION MATRIX 38

TABLE 3: CHINA INDICE UNIT ROOT TEST 42

TABLE 4: CHINA RETURNS VALUE UNIT ROOT TEST 44

TABLE 5: TAIWAN INDICE VALUE UNIT ROOT TEST 45

TABLE 6: TAIWAN RETURNS VALUE UNIT ROOT TEST 47

TABLE 7: INDIA INDICE VALUE UNIT ROOT TEST 48

TABLE 8: INDIA RETURNS VALUE UNIT ROOT TEST 50

TABLE 9: HONG KONG INDICE VALUE UNIT ROOT TEST 51

TABLE 10: HONG KONG RETURNS VALUE UNIT ROOT TEST 53

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LIST OF GRAPHS

TITLEPAGE

NO.

GRAPH 1: CHINA INDICE VALUE38

GRAPH 2: CHINA RETURNS VALUE 39

GRAPH 3: TAIWAN INDICE VALUE 39

GRAPH 4: TAIWAN RETURNS VALUE 40

GRAPH 5: INDIA INDICE VALUE 40

GRAPH 6: INDIA RETURNS VALUE 41

GRAPH 7: HONG KONG INDICE VALUE 41

GRAPH 8: HONG KONG RETURNS VALUE42

GRAPH 9: CHINA INDICE VALUE (HISTOGRAM) 43

GRAPH 10: CHINA RETURNS VALUE (HISTOGRAM) 44

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GRAPH 11: TAIWAN INDICE VALUE (HISTOGRAM) 46

GRAPH 12: TAIWAN RETURNS VALUE (HISTOGRAM) 47

GRAPH 13: INDIA INDICE VALUE (HISTOGRAM) 49

GRAPH 14: INDIA RETURNS VALUE (HISTOGRAM) 50

GRAPH 15: HONG KONG INDICE VALUE (HISTOGRAM) 52

GRAPH 16: HONG KONG RETURNS VALUE (HISTOGRAM) 53

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Chapter 1

Introduction to Stock Markets

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1.1 Stock MarketA stock market or equity market is the aggregation of buyers and sellers (a loose network of economic transactions, not a physical facility or discrete entity) of stocks (also called shares); these may include securities listed on a stock exchange as well as those only traded privately.

1.2 Stock ExchangeA stock exchange is a place or organization by which stock traders (people and companies) can trade stocks. Companies may want to get their stock listed on a stock exchange. Other stocks may be traded "over the counter", that is, through a dealer. A large company will usually have its stock listed on many exchanges across the world.

Exchanges may also cover other types of security such as fixed interest securities or interest derivatives.

1.3 Importance of Stock Exchange 1.3.1 Functions and Purpose

The stock market is one of the most important ways for companies to raise money, along with debt markets which are generally more imposing but do not trade publicly. This allows businesses to be publicly traded, and raise additional financial capital for expansion by selling shares of ownership of the company in a public market. The liquidity that an exchange affords the investors enables their holders to quickly and easily sell securities. This is an attractive feature of investing in stocks, compared to other less liquid investments such as property and other immoveable assets. Some companies actively increase liquidity by trading in their own shares.

The smooth functioning of all these activities facilitates economic growth in that lower costs and enterprise risks promote the production of goods and services as well as possibly employment.

1.3.2 Relation of the Stock Market to Modern Financial SystemA portion of the funds involved in saving and financing, flows directly to the financial markets instead of being routed via the traditional bank lending and deposit operations. The general public interest in investing in the stock market, either directly or through mutual funds, has been an important component of this process.

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The trend towards forms of saving with a higher risk has been accentuated by new rules for most funds and insurance, permitting a higher proportion of shares to bonds. Similar tendencies are to be found in other developed countries. In all developed economic systems, such as the European Union, the United States, Japan and other developed nations, the trend has been the same: saving has moved away from traditional (government insured) "bank deposits to more risky securities of one sort or another".

1.3.3 Behaviour of the Stock MarketFrom experience it is known that investors may 'temporarily' move financial prices away from their long term aggregate price 'trends'. Over reactions may occur so that excessive optimism (euphoria) may drive prices unduly high or excessive pessimism may drive prices unduly low.

Psychological research has demonstrated that people are predisposed to 'seeing' patterns, and often will perceive a pattern in what is, in fact, just noise. In the present context this means that a succession of good news items about a company may lead investors to overreact positively (unjustifiably driving the price up). A period of good returns also boosts the investors' self-confidence, reducing their (psychological) risk threshold.

On the other hand, Stock markets play an essential role in growing industries that ultimately affect the economy through transferring available funds from units that have excess funds (savings) to those who are suffering from funds deficit (borrowings). In other words, capital markets facilitate funds movement between the above-mentioned units. This process leads to the enhancement of available financial resources which in turn affects the economic growth positively.

1.3.4 CrashesA stock market crash is often defined as a sharp dip in share prices of stocks listed on the stock exchanges. In parallel with various economic factors, a reason for stock market crashes is also due to panic and investing public's loss of confidence. Often, stock market crashes end speculative economic bubbles.

One of the most famous stock market crashes started October 24, 1929 on Black Thursday. The Dow Jones Industrial Average lost 50% during this stock market crash. It was the beginning of the Great Depression. Another famous crash took place on October 19, 1987 – Black Monday. The crash began in Hong Kong and quickly spread around the world. By the end of October, stock markets in Hong Kong had fallen 45.5%, Australia 41.8%, Spain 31%, the United Kingdom 26.4%, the United States 22.68%, and Canada 22.5%. Black Monday itself was the largest one-day percentage decline in stock market history – the Dow Jones fell by 22.6% in a day. The names "Black Monday" and "Black Tuesday" are also used for

3

October 28–29, 1929, which followed Terrible Thursday - the starting day of the stock market crash in 1929.

1.4 HistoryIn 12th century France, the courtiers de change were concerned with managing and regulating the debts of agricultural communities on behalf of the banks. Because these men also traded with debts, they could be called the first brokers. In the middle of the 13th century, Venetian bankers began to trade in government securities. In 1351 the Venetian government outlawed spreading rumours intended to lower the price of government funds. Bankers in Pisa, Verona, Genoa and Florence also began trading in government securities during the 14th century. This was only possible because these were independent city states not ruled by a duke but a council of influential citizens. Italian companies were also the first to issue shares. Companies in England and the Low Countries followed in the 16th century.

The Dutch East India Company (founded in 1602) was the first joint-stock company to get a fixed capital stock and as a result, continuous trade in company stock occurred on the Amsterdam Exchange. Soon thereafter, a lively trade in various derivatives, among which options and repos, emerged on the Amsterdam market. There are now stock markets in virtually every developed and most developing economies, with the world's largest markets being in the United States, United Kingdom, Japan, India, Pakistan, China, Canada, Germany (Frankfurt Stock Exchange), France, South Korea and the Netherlands.

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Chapter 2

Profile of the Stock Markets

5

2.1 Stock Indices

The movements of the prices in a market or section of a market are captured in price indices called stock market indices, of which there are many, e.g. the S&P, the FTSE and the Euronext indices. Such indices are usually market capitalization weighted, with the weights reflecting the contribution of the stock to the index. The constituents of the index are reviewed frequently to include/exclude stocks in order to reflect the changing business environment.

It is a measurement of the value of a section of the stock market. It is computed from the prices of selected stocks (typically a weighted average). It is a tool used by investors and financial managers to describe the market, and to compare the return on specific investments.

2.1.1 Types of IndicesStock market indices may be classed in many ways. A 'world' or 'global' stock market index includes (typically large) companies without regard for where they are domiciled or traded. Two examples are MSCI World and S&P Global 100.

A 'national' index represents the performance of the stock market of a given nation—and by proxy, reflects investor sentiment on the state of its economy. The most regularly quoted market indices are national indices composed of the stocks of large companies listed on a nation's largest stock exchanges, such as the American S&P 500, the Japanese Nikkei 225, and the British FTSE 100.

Other indices may be regional, such as the FTSE Developed Europe Index or the FTSE Developed Asia Pacific Index. Indexes may be based on exchange, such as the NASDAQ-100 or NYSE US 100, or groups of exchanges, such as the Euro next 100 or OMX Nordic 40.

More specialized indices exist tracking the performance of specific sectors of the market. Some examples include the Wilshire US REIT which tracks more than 80 American real estate investment trusts and the Morgan Stanley Biotech Index which consists of 36 American firms in the biotechnology industry. Other indices may track companies of a certain size, a certain type of management, or even more specialized criteria.

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2.1.2 Index VersionsSome indices, such as the S&P 500, have multiple versions. These versions can differ based on how the index components are weighted and on how dividends are accounted for. For example, there are three versions of the S&P 500 index: price return, which only considers the price of the components, total return, which accounts for dividend reinvestment, and net total return, which accounts for dividend reinvestment after the deduction of a withholding tax. As another example, the Wilshire 4500 and Wilshire 5000 indices have five versions each.

2.1.3 WeightingAn index may also be classified according to the method used to determine its price. In a price-weighted index such as the Dow Jones Industrial Average, NYSE Arca Major Market Index, and the NYSE ARCA Tech 100 Index, the price of each component stock is the only consideration when determining the value of the index. Thus, price movement of even a single security will heavily influence the value of the index.

An equal-weighted index is one in which all components are assigned the same value. For example, the Barron's 400 Index assigns an equal value of 0.25% to each of the 400 stocks included in the index, which together add up to the 100% whole.

A modified capitalization-weighted index is a hybrid capitalization weighting and equal weighting. It is similar to a capitalization weighting with one main difference: the largest stocks are capped to a percent of the weight of the total stock index and the excess weight will be redistributed equally amongst the stocks under that cap. That is, a stock's weight in the index is decided by the score it gets relative to the value attributes that define the criteria of a specific index, the same measure used to select the stocks in the first place.

2.2 Indian Stock Exchange2.2.1 Bombay Stock Exchange

The Bombay Stock Exchange (BSE) is an Indian stock exchange located at Dalal Street, Kala Ghoda, Mumbai, and Maharashtra, India. Established in 1875 the BSE is Asia’s first and Worlds Fastest Stock Exchange with a speed of 6 microseconds and one of India’s leading exchange groups and the oldest stock exchange in the South Asia region. Bombay Stock Exchange is the world's 11th largest stock market by market capitalization at $1.7 trillion as of 23 January 2015. More than 5,000 companies are listed on BSE.

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The Bombay Stock Exchange is the oldest exchange in Asia. It traces its history to 1855, when four Gujarati and one Parsi stockbroker would gather under banyan trees in front of Mumbai's Town Hall. The location of these meetings changed many times as the number of brokers constantly increased. The group eventually moved to Dalal Street in 1874 and in 1875 became an official organization known as "The Native Share & Stock Brokers Association".

On 31 August 1957, the BSE became the first stock exchange to be recognized by the Indian Government under the Securities Contracts Regulation Act. In 1980, the exchange moved to the PhirozeJeejeebhoy Towers at Dalal Street, Fort Area. In 1986, it developed the BSE SENSEX index, giving the BSE a means to measure overall performance of the exchange. In 2000, the BSE used this index to open its derivatives market, trading SENSEX futures contracts.

Historically an open outcry floor trading exchange, the Bombay Stock Exchange switched to an electronic trading system developed by CMC Ltd in 1995. This automated, screen-based trading platform called BSE On-line trading (BOLT) had a capacity of 8 million orders per day. The BSE has also introduced a centralized exchange-based internet trading system, BSEWEBx.co.in to enable investors anywhere in the world to trade on the BSE platform.

2.2.2 BSE SENSEX

The S&P BSE SENSEX (S&P Bombay Stock Exchange Sensitive Index), also-called the BSE 30 or simply the SENSEX, is a free-float market-weighted stock market index of 30 well-established and financially sound companies listed on Bombay Stock Exchange. The 30 component companies which are some of the largest and most actively traded stocks, are representative of various industrial sectors of the Indian economy. Published since 1 January 1986, the S&P BSE SENSEX is regarded as the pulse of the domestic stock markets in India. The base value of the S&P BSE SENSEX is taken as 100 on 1 April 1979, and its base year as 1978–79.

As of 21 April 2011, the market capitalisation of S&P BSE SENSEX was about ₹29733 billion (US$449 billion) (47.68% of market capitalisation of BSE), while its free-float market capitalisation was ₹15690 billion (US$237 billion). During 2008-12, Sensex 30 Index share of BSE market capitalisation fell from 49% to 25% due to the rise of sectoral indices like BSE PSU, Bankex, BSE-Teck, etc.

8

https://en.wikipedia.org/wiki/Indian_rupee

https://en.wikipedia.org/wiki/Indian_rupee

2.2.3 CalculationThe index is calculated based on a free float capitalisation method, a variation of the market capitalisation method. Instead of using a company's outstanding shares it uses its float, or shares that are readily available for trading . As per free float capitalisation methodology, the level of index at any point of time reflects the free float market value of 30 component stocks relative to a base period. The market capitalisation of a company is determined by multiplying the price of its stock by the number of shares issued by of corporate actions, replacement of scrips, The index has increased by over twenty five times from June 1990 to the present. Using information from April 1979 onwards, the long-run rate of return on the S&P BSE SENSEX works out to be 18.6% per annum.

2.3 China Stock Exchange

2.3.1 Shanghai Stock ExchangeThe Shanghai Stock Exchange is a stock exchange that is based in the city

of Shanghai, China. It is one of the two stock exchanges operating independently in

the People's Republic of China, the other being the Shenzhen Stock Exchange.

Shanghai Stock Exchange is the world's 5th largest stock market by market

capitalization at US$5.5 trillion as of May 2015. The Shanghai Clearing House

provides security for financial market participants, and efficient clearing services

development purposes, but also conductive to international peers inter-agency

communication and cooperation. It provides central counterparty clearing of foreign

currency in the interbank market, including clearing, settlement, margin

management, collateral management, information services, consulting services, and

related management department under other business.

The market for securities trading in Shanghai begins in the late 1860s. The first shares list appeared in June 1866 and by then Shanghai's International Settlement had developed the conditions conducive to the emergence of a share market: several banks, a legal framework for joint-stock companies, and an interest in diversification among the established trading houses. In 1891 during the boom in mining shares, foreign businessmen founded the "Shanghai Sharebrokers Association" headquartered in Shanghai as China's first stock exchange. In 1904 the Association applied for registration in Hong Kong under the provision of the Companies ordinance and was renamed as the "Shanghai Stock Exchange". The supply of securities came primarily from local companies.

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Later in 1920 and 1921, "Shanghai Securities & Commodities Exchange" and "Shanghai Chinese Merchant Exchange" started operation respectively. An amalgamation eventually took place in 1929, and the combined markets operated thereafter as the "Shanghai Stock Exchange". By the 1930s, Shanghai had emerged as the financial centre of the Far East, where both Chinese and foreign investors could trade stocks, debentures, government bonds, and futures. The operation of Shanghai Stock Exchange came to an abrupt halt after Japanese troops occupied the Shanghai International Settlement on December 8, 1941. In 1946, Shanghai Stock Exchange resumed its operations before closing again 3 years later in 1949, after the Communist revolution took place.

During the 1980s, China's securities market evolved in tandem with the country's economic reform and opening up and the development of socialist market economy. On 26 November 1990, Shanghai Stock Exchange was re-established and operations began a few weeks later on 19 December.

2.3.2 StructureThe securities listed at the SSE include the three main categories of stocks, bonds, and funds. Bonds traded on SSE include treasury bonds (T-bond), corporate bonds, and convertible corporate bonds. SSE T-bond market is the most active of its kind in China. There are two types of stocks being issued in the Shanghai Stock Exchange: "A" shares and "B" shares. A shares are priced in the local renminbi yuan currency, while B shares are quoted in U.S. dollars. Initially, trading in "A" shares are restricted to domestic investors only while "B" shares are available to both domestic (since 2001) and foreign investors.

The SSE is open for trading every Monday to Friday. The morning session begins with centralized competitive pricing from 09:15 to 09:25, and continues with consecutive bidding from 09:30 to 11:30. This is followed by the afternoon consecutive bidding session, which starts from 13:00 to 15:00. The market is closed on Saturday and Sunday and other holidays announced by the SSE.

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Holiday ScheduleShanghai Stock Exchange

Holiday From ToNo. of days (excluding Saturday and

Sunday)

New Year 1 January 1 January 1 DAY

Chinese New Year 15 February 19 February 5 DAYS

Qingming Festival 5 April 5 April 1 DAY

Labour Day 3 May 3 May 1 DAY

Xuanwu Festival 14 June 16 June 3 DAYS

Mid-Autumn Festival

22 September

24 September

3 DAYS

National Day 1 October 7 October 5 DAYS

2.3.3 Listing Requirements

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According to the regulations of Securities Law of the People’s Republic of China and Company Law of the People’s Republic of China, limited companies applying for the listing of shares must meet the following criteria:

The shares must have been publicly issued following approval of the State Council Securities Management Department.

The company’s total share capital must not be less than RMB 30 million.

The company must have been in business for more than 3 years and have made profits over the last three consecutive years.

The company must not have committed any major illegal activities or false accounting records in the last three years.

2.3.4 SSE COMPOSITE INDEXThe SSE Composite Index is a stock market index of all stocks (A shares and B shares) that are traded at the Shanghai Stock Exchange.

2.3.5 Weighting And CalculationSSE Indices are all calculated using a Pasche weighted composite price index formula. This means that the index is based on a base period on a specific base day for its calculation. The base day for SSE Composite Index is December 19, 1990, and the base period is the total market capitalization of all stocks of that day. The Base Value is 100. The index was launched on July 15, 1991.

The formula is:

Current index = Current total market cap of constituents × Base Value / Base Period

Total market capitalization = ∑ (price × shares issued)

The B share stocks are generally denominated in US dollars for calculation purposes. For calculation of other indices, B share stock prices are converted to RMB at the applicable exchange rate (the middle price of US dollar on the last trading day of each week) at China Foreign Exchange Trading Centre and then published by the exchange.

2.3.6 Constituents

12

https://en.wikipedia.org/w/index.php?title=Company_Law_of_the_People%E2%80%99s_Republic_of_China&action=edit&redlink=1

https://en.wikipedia.org/wiki/Securities_Law_of_the_People%E2%80%99s_Republic_of_China

https://en.wikipedia.org/wiki/Securities_Law_of_the_People%E2%80%99s_Republic_of_China

The full list of all constituent stocks can be found on the SSE's website. The top 20 by market cap are listed below.

Constituent SSE number

Air China 601111

Aluminium Corporation of China 601600

Bank of China 601988

Bank of Communications 601328

Baoshan Iron & Steel 600019

Beijing Gehua CATV Network 600037

Beijing North Star 601588

China Citic Bank 601998

China Life Insurance 601628

China Merchants Bank 600036

China Merchants Energy Shipping 601872

China Minsheng Banking 600016

China Petroleum & Chemical 600028

China Shipping Development Company 600026

China United Telecommunications Corporation 600050

China Yangtze Power 600900

Citic Securities 600030

Daqin Railway 601006

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https://en.wikipedia.org/wiki/Daqin_Railway

https://en.wikipedia.org/wiki/Citic_Securities

https://en.wikipedia.org/wiki/China_Yangtze_Power

https://en.wikipedia.org/wiki/China_Unicom

https://en.wikipedia.org/wiki/China_Shipping_Development_Company

https://en.wikipedia.org/wiki/China_Petroleum_%26_Chemical

https://en.wikipedia.org/wiki/China_Minsheng_Banking

https://en.wikipedia.org/wiki/China_Merchants_Energy_Shipping

https://en.wikipedia.org/wiki/China_Merchants_Bank

https://en.wikipedia.org/wiki/China_Life_Insurance

https://en.wikipedia.org/wiki/China_Citic_Bank

https://en.wikipedia.org/wiki/Beijing_North_Star

https://en.wikipedia.org/wiki/Baosteel

https://en.wikipedia.org/wiki/Bank_of_Communications

https://en.wikipedia.org/wiki/Bank_of_China

https://en.wikipedia.org/wiki/Aluminum_Corporation_of_China

https://en.wikipedia.org/wiki/Air_China

http://english.sse.com.cn/information/indices/list/s/singleIndex/000001/const/index_const_list_en_1.shtml

Founder Technology Group 600601

GD Power Development 600795

The SSE Composite (also known as Shanghai Composite) Index is the most commonly used indicator to reflect SSE's market performance. Constituents for the SSE Composite Index are all listed stocks (A shares and B shares) at the Shanghai Stock Exchange. The Base Day for the SSE Composite Index is December 19, 1990. The Base Period is the total market capitalization of all stocks of that day. The Base Value is 100. The index was launched on July 15, 1991. At the end of 2006, the index reaches 2,675.47. Other important indexes used in the Shanghai Stock Exchanges include the SSE 50 Index and SSE 180 Index.

2.4 Taiwan Stock ExchangeThe Taiwan Stock Exchange Corporation (TWSE) is a financial institution, located in Taipei 101, in Taipei, Taiwan. The TWSE was established in 1961 and began operating as a stock exchange on 9 February 1962. It is regulated by the Financial Supervisory Commission.

As of 31 December 2013, the Taiwan Stock Exchange had 809 listed companies with a combined market capitalization of NT$24,519,622 million.

The exchange broadcasts before-hour information from 7:40 to 8:40. Then it has normal trading sessions from 09:00 to 13:45 and fixed price post-market sessions from 14:00 to 15:00 on all days of the week except Saturdays, Sundays and holidays declared by the exchange in advance.

14

https://en.wikipedia.org/wiki/New_Taiwan_dollar

https://en.wikipedia.org/wiki/Financial_Supervisory_Commission_(Republic_of_China)

https://en.wikipedia.org/wiki/Financial_Supervisory_Commission_(Republic_of_China)

https://en.wikipedia.org/wiki/Stock_exchange

https://en.wikipedia.org/wiki/Taiwan

https://en.wikipedia.org/wiki/Taipei

https://en.wikipedia.org/wiki/Taipei_101

https://en.wikipedia.org/wiki/Financial_institution

https://en.wikipedia.org/wiki/GD_Power_Development

https://en.wikipedia.org/wiki/Founder_Group

Month Date Description

January 12 New Year

Adjusted Holiday

February

1617

No Trading.Market opens only for Clearing & Settlement.

18 Lunar New Year's Eve

1920212223

Spring Festival

2728

Adjusted HolidayPeace Memorial Day

April

34

Adjusted HolidayChildren's Day

56

Tomb-sweeping DayAdjusted Holiday

May 1 Labour Day

June 1920

Adjusted HolidayDragon Boat Festival

September 2728

Mid-autumn FestivalAdjusted Holiday

October 910

Adjusted HolidayNational Day

The current chairman of the TWSE is Lee Sush-der. The President is Michael Lin.

Starting December 1986, eight Industrial Sub-Indices were introduced, i.e. Glass and Ceramic, Textile, Food, Plastic and Chemical, Electrical, Paper and Pulp, Building Material and Construction, Finance and Insurance. In August 1995, TWSE introduced additional 14 Industrial Sub-Indices, i.e. Cement, Plastic, Electric Machinery, Electric and Cable, (Chemical, Biotechnology, and Medical Care), Glass and Ceramic, Iron and Steel, Rubber, Automobile, Electronics, Shipping and Transportation, Tourism, Trading and Consumers' Goods, Other. In July 2007, TWSE also introduced additional 11 Industrial Sub-Indices, i.e. Chemical, Biotechnology and Medical Care, (Oil, Gas and Electricity), Semiconductor, Computer and Peripheral Equipment, Optoelectronic, Communications and Internet, Electronic Parts/Components, Electronic Products Distribution, Information Service, Other Electronic.

15

https://en.wikipedia.org/wiki/Lee_Sush-der

2.4.1 Taiwan Capitalization Weighted Stock Index (TAIEX)Taiwan Capitalization Weighted Stock Index (TAIEX) is a stock market index for companies traded on the Taiwan Stock Exchange (TWSE). TAIEX covers all of the listed stocks excluding preferred stocks, full-delivery stocks and newly listed stocks, which are listed for less than one calendar month. It was first published in 1967 by TWSE with 1966 being the base year with a value of 100.

The Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) is the most widely quoted of all TWSE indices. TAIEX is adjusted in the event of new listing, de-listing and new shares offering to offset the influence on TAIEX owing to non-trading activities.

2.4.2 Guidelinesfor Computation of TAIEXThe TAIEX weighted index compiled by TWSE is calculated by the following formula:Index = Aggregate market value / Base value of the current day * 100The aggregate market value is the summation of the market values obtained by multiplying the traded price of each constituent by the issued shares of the current day. If there is no traded price on the current day, the opening auction reference price of the current day may be used for calculation. However, stock of newly listed companies included in calculation of the index may be accounted for on the basis of the listed shares of the current date.The base value at the time of commencement of calculation of the index base period is the current aggregate market value at that time.

Upon occurrence of any of the below-listed events, the base value of the TAIEX computed by TWSE shall be adjusted to maintain the continuity of the TAIEX:

(1) Addition or deletion of a constituent - effective date.(2) Subscription of common shares for cash capital increase - ex-right date.(3) Distribution of common shares as bonus to employees or certificates of entitlement

to new shares - listing date.(4) Distribution of common shares as stock dividends on preferred stock - ex-right date.(5) Holding by a listed company of treasury stock for which capital cancellation has not

been carried out - ex-right date.(6) Share cancellation based on the law - ex-right date or the third trading day of the

next month following public announcement on capital decrease, whichever comes first.

(7) Failed offering for cash capital increase - at reversion to the original number of issued shares on the third trading day of the next month following receipt of notification.

(8) Listing of certificates of entitlement to shares or new shares following company merger or consolidation - listing date.

(9) Listing of common shares issued in replacement of certificates of entitlement to 16

https://en.wikipedia.org/wiki/Preferred_stock

https://en.wikipedia.org/wiki/Taiwan_Stock_Exchange

https://en.wikipedia.org/wiki/Stock_market_index

convertible bonds - listing date.(10) Common shares converted directly from convertible bonds or issued through

exercise of securities with subscription right - ex-right date or the third trading day of the next month following the public announcement of capitalization amendment registration.

(11) Cash capital increase shares or certificates of payment for which shareholders have waived subscription rights and public underwriting has been adopted - listing date.

(12) New shares issued for global depositary receipts - listing date.(13) Common shares converted from convertible preferred shares - listing date.(14) Other non-trading factors affecting aggregate market value.

2.5 Hong Kong Stock ExchangeThe Hong Kong Stock Exchange (HKE) is a stock exchange located in Hong Kong. It is Asia's third largest stock exchange in terms of market capitalization behind the Tokyo Stock Exchange and Shanghai Stock Exchange, and the sixth largest in the world behind Euronext. As of 30 November 2013, the Hong Kong Stock Exchange had 1,615 listed companies, 776 of which are from mainland China, 737 from Hong Kong and 102 from abroad (e.g. Cambodia, Italy, Kazakhstan, etc.).

2.5.1 HistoryRecords of securities trading in Hong Kong date back to 1866. In 1891 when the Association of Stockbrokers in Hong Kong was established, Hong Kong had its first formal stock market. It was renamed The Hong Kong Stock Exchange in 1914.

The Stock Exchange of Hong Kong Limited (the Exchange) was incorporated in 1980 and trading on the Exchange finally commenced on 2 April 1986. Since 1986, a number of major developments have taken place. The 1987 market crash revealed flaws in the market and led to calls for a complete reform of the Hong Kong securities industry. This led to significant regulatory changes and infrastructural developments. As a result, the Securities and Futures Commission (SFC) was set up in 1989 as the single statutory securities market regulator.

The market infrastructure was much improved with the introduction by the Exchange of the Central Clearing and Settlement System (CCASS) in June 1992 and the Automatic Order Matching and Execution System (AMS) in November 1993. Since then, the framework of market rules and regulations, both Exchange-administered or otherwise, have been undergoing continuing review and revision to meet changing market needs while ensuring effective market regulation.

In respect of market and product development, there are the listing of the first derivative warrant in February 1988, the listing of the first China-incorporated enterprise (H share) in July 1993; and the introduction of regulated short selling in

17

January 1994 and stock options in September 1995. Furthermore, the Exchange introduced the Growth Enterprise Market (GEM) in November 1999 to provide fund raising opportunities for growth companies of all sizes from all industries, and to promote the development of technology industries in the region. The exchange first introduced a computer-assisted trading system on 2 April 1986. In 1993 the exchange launched the "AutomaticOrder Matching and Execution System" (AMS), which was replaced by the third generation system (AMS/3) in October 2000.

2.5.2 Trading Hours

A pre-opening auction session from 9:00 am to 9:30 am. The opening price of a security is reported shortly after 9:20 am.

A morning continuous trading session from 09:30 am to 12:00 pm An extended morning session from 12:00 noon to 1:00 pm, also referred to as the lunch

break. Continuous trading proceeds in specifically-designated securities (currently two ETFs, 4362 and 4363). Trading in other securities is not possible. However, previously-placed orders in any securities can be cancelled from 1:00 pm onwards.

An afternoon continuous trading session from 1:00 pm to 4:00 pm.

Securities Market is closed on Saturdays, Sundays.

2.5.3 HangSeng IndexThe Hang Seng Index (HSI) is a free float-adjusted market capitalization-weighted stock market index in Hong Kong. It is used to record and monitor daily changes of the largest companies of the Hong Kong stock market and is the main indicator of the overall market performance in Hong Kong. These 50 constituent companies represent about 58% of the capitalisation of the Hong Kong Stock Exchange.

HSI was started on November 24, 1969, and is currently compiled and maintained by Hang Seng Index Company Limited, which is a wholly owned subsidiary of Hang Seng Bank, one of the largest banks registered and listed in Hong Kong in terms of market capitalisation. It is responsible for compiling, publishing and managing the Hang Seng Index and a range of other stock indexes, such as Hang Seng China EnterprisesIndex,HangSeng China AH Index Series, Hang Seng China H-Financials Index, Hang Seng Composite Index Series, Hang Seng China Industry Top Index, Hang Seng Corporate Sustainability Index Series and Hang Seng Total Return Index Series. Hang Seng in turn, despite being a public company, is held in majority by British financial firm HSBC.

18

https://en.wikipedia.org/wiki/HSBC

https://en.wikipedia.org/wiki/Hang_Seng_Bank

https://en.wikipedia.org/wiki/Hang_Seng_Bank

When the Hang Seng Index was first published, its base of 100 points was set equivalent to the stocks' total value as of the market close on July 31, 1964. Its all-time low is 58.61 points, reached retroactively on August 31, 1967, after the base value was established but before the publication of the index. The Hang Seng passed the 10,000 point milestone for the first time in its history on December 10, 1993 and, 13 years later, passed the 20,000 point milestone on December 28, 2006. In less than 10 months, it passed the 30,000 point milestone on October 18, 2007. Its all-time high, set on October 30, 2007, was 31,958.41 points during trading and 31,638.22 points at closing. From October 30, 2007 through March 9, 2008, the index lost 9,426 points or approximately 30%. On September 5, it fell past the 20,000 mark the first time in almost a year to a low of 19,708.39, later closing at 19,933.28. On October 8, 2008, the index closed at 15,431.73, over 50% less than the all-time high and the lowest closing value in over two years. On October 27, 2008, the index further fell to 10,676.29 points, having fallen nearly two-thirds from its all-time peak, but passed the 20,000 point milestone again to 20,063.93 on 24 July 2009. The index reached 25,000.00 on August 19, 2014, reaching as high as 25,201.21 that day, later closing at 24,909.26 points. It continued rising to hit 26,000 on April 8, 2015 with a close of 26,236.86 The following day, it rose to as much as 27,922.67 before closing at 26,944.39. Yet again, another milestone was reached on April 13, 2015 rising to over 28,000 points, or closing to 28,016.34, the highest since December 2007. On July 8, the index fell as much as 2138 points. On 21 August the index entered a bear market.

2.5.4 HangSeng Industry Classification System

Hang Seng Industry Classification System (formerly called Hang Seng Stock Classification System) is a comprehensive system designed for the Hong Kong stock market by Hang Seng Index Company Limited. It reflects the stock performance in different sectors. It caters for the unique characteristics of the Hong Kong stock market and maintains the international compatibility with a mapping to international industry classification

General classification guidelines:

i) The sales revenue arising from each business area of a company is the primary parameter of stock classification. Profit or assets will also be taken into consideration where these better reflects the company's business.

ii) A company will be classified into different sectors according to its majority source of sales revenue(or profit or assets if relevant).

iii) Re-classification of a stock's Industry Sector will occur once the company's business has undergone a major change, such as, substantial merger or acquisition.

19

https://en.wikipedia.org/wiki/Takeover

https://en.wikipedia.org/wiki/Merger

https://en.wikipedia.org/wiki/Bear_market

2.5.5 Selection Criteria forthe HSI Constituent Stocks

HSI constituent stocks are selected with the use of extensive analysis, together with external consultation. To be qualified for selection, a company:

must be among those that comprise top 90% of the total market value of all ordinary shares;

must be among those that comprise top 90% of the total turnover on the Stock Exchange of Hong Kong Limited "SEHK"

should have a listing history of 24 months or meet the requirements of the following guidelines:

Guidelines for Handling Large-cap Stocks Listed for Less than 24 Months

For a newly listed large-cap stock, the minimum listing time required for inclusion in the stock universe for the HSI review is as follows:

Average MV Rank at Time of Review Minimum Listing History

Top 5 3 Months

6–15 6 Months

16–20 12 Months

21–25 18 Months

Below 25 24 Months

Among the eligible candidates, final selections are based on their:

market capitalisation and turnover rankings;

representation of the respective sub-sectors within HSI; and

Financial performance.

20

2.5.6 Calculation Formula For HSI

The current Hang Seng Index is calculated from this formula:

Descriptions on parameters:

P(t)：Current Price at Day t P(t-1)：Closing Price at Day (t-1) IS：Issued Shares (Only H-share portion is taken into calculation in case of H-share

constituents.) FAF：Freefloat-adjusted Factor, which is between 0 and 1, adjusted quarterly CF：Cap Factor, which is between 0 and 1, adjusted quarterly

The representativeness of the HSI can be studied by the turnover of the whole stock market and by how much its market capitalisation covers. The aggregate market value of the HSI constituent stocks is maintained at approximately 60% of the total market value. This coverage ratio compares favourably with major overseas stock indices.

2.5.7 Other Related Hang Seng Stock Indexes

Hang Seng China Enterprises Index

Hang Seng China-Affiliated Corporations Index

HangSeng China H-Financials Index

Hang Seng Mainland 100

Hang Seng Mainland 25

Hang Seng HK 35

Hang Seng REIT Index

Hang Seng Corporate Sustainability Index Series

Hang Seng Composite Index

Hang Seng Composite Industry Indexes

Hang Seng Composite Size Indexes

Hang Seng Short & Leveraged Index Series

Hang Seng China A Industry Top Index

21

Chapter 3

Study of the Selected Research Problem

23

3.1 INTRODUCTION TO STOCK MARKET INTEGRATION

Financial integration may be looked into from two different ways. The most popular thinking

is the integration of financial markets of a country with those countries which fall in the

same geographical region or with which it maintains a good relationship in commerce and

trade. But there is another way of viewing integration, where different components in the

financial system of a country are integrated with one another and accordingly that may be

termed as “domestic financial integration” of that country. The degree of integration of

financial markets around the world has been increasing significantly since late 1980s and

early 1990s with the onset of globalization effort. A key factor underlying this process has

been the increased internationalization of investment seeking higher rates of return and the

opportunity to diversify risk across countries. At the same time, many developing countries

including India have encouraged inflows of capital by dismantling restrictions, deregulating

domestic financial markets, and improving their economic environment through the

introduction of market-oriented reforms. The extent of financial integration existing

externally among such countries has been taken care of by most of the relevant literature.

But, in this study, existence of the second postulates of financial integration namely,

domestic financial integration can be explored by examining into the impact of selected

macro-economic variables on stock markets in India. Thus, it is to be seen whether the

various segments of the financial markets in India move in tandem, keeping stock market as

the yardstick. There have been few studies in India and outside, which examine the impact of

selected economic, and even political, events on stock markets. But, these studies were

essentially devoted to test the short-term impact of events, like the impact of budgets, credit

policy, war-like situations, signing of international treaties and so on, which might not

consolidate in the long-run. These economic or political events may have distinct impact on

stock prices in the short-run; the lasting impact, if any, of those cannot be identified or

separated in long term when impact of many other events will come into effect. Here, since

the basic aim is to test the level of financial integration internally existing within the country,

unlike earlier studies, the long-term impact of macroeconomic variables like inflation rate,

foreign exchange rate, bullion prices, index of industrial production, return from money

market, etc. on stock market have been enquired into. Eun and Shin (1989) detected the

presence of substantial amount of interdependence among national stock markets of USA,

UK, Canada, Germany, Australia, France, Japan, Switzerland and Hong Kong. Using daily

closing price data during the period January 1980 through December 1985, the study found a

24

substantial amount of multi-lateral interactions among the national stock markets. The

analysis indicated that innovations in the U.S. were rapidly transmitted to other markets in a

clearly recognizable fashion, whereas no single foreign market can significantly explain the

U.S. market movements.

3.2 REVIEW OF LITERATURE:

Author(s), year

and country of

study

Title of study Journal

Sample

data(no. ofyears)

No. ofsample

countries

Methodology/toolsadopted for data

analysis

Findings and conclusions

Masih andMasih (1999),

Australia

“Are Asian stock market

fluctuations due mainly to

intra-regional contagioneffects?

Evidence based on

Asian emerging stock

markets”

Pacific BasinFinanceJournal

5 8

Unit root test (KPSS and

modified Dickey fuller

test), multivariatecointegration

analysis,VECM, VAR, and

VDCanalysis

The existence of a significant short and long termrelationship between the

developed andemerging markets

was found.

Maysami andKoh (2000),Singapore

“A vector error correction

model of the Singapore stock

market”

InternationalReview ofEconomic

andFinance

7 3

Unit root (ADF and PP)

test, multivariatecointegration test,

VECM

It was found that the three markets

are highlycointegrated and

changes in US and Japanese

stock market have a significant effect

on theSingapore stock

market..

Sheng and Tu(2000), Taiwan

“A study of cointegration

andvariance

decompositionamong national

equityindices before and during theperiod of the

Asian financialcrisis”

Journal ofMultinational

FinancialManagement

2 12

Unit root (ADF) test,

cointegration test, VECM,

Granger causality test,

VDC test

At least one cointegrational

relationship waspresent in stock

indices during the period of

financial crisis but it was not so in the

periodbefore the financial

crisis. The relationship forthe South-East Asian countries

was strongerthan that for the

North-East Asian countries

and USA played a dominant role in

influencingother markets.

25

Huang et al.(2000), Taiwan

“Causality and cointegration

of stock markets among the

United States, Japan and theSouth China

GrowthTriangle”

InternationalReview ofFinancialAnalysis

5 3

Unit root (ADF) test,

cointegration technique

and Granger causality test

No cointegration exists among the

marketsexcept between Shanghai and Shenzhen and

stock price changes in USA have more affect

on Chinese markets than those

of Japan.Similarly, price changes on the

Hong Kongstock market lead the Taiwan market

by oneday.

Chang and Nieh

(2001), Taiwan

“International transmissionof stock price movements

among Taiwan and its

trading partners: Hong Kong,Japan and the United States”

Review ofPacific Basin

FinancialMarkets and

Policies

1 4

Unit root test (ADF and PP

tests), Johansenmultivariate cointegration

test, VECM, VDC and IRF

The four stock markets were found to be

cointegrated and the US and

Japanese marketsplayed leading

roles in driving the fluctuations

in the other two markets. The October 1997

crises affected the US stock market

butshowed no

significant impact on other

markets. The Taiwan and Hong

Kong marketswere affected more

by regional markets such

as Japan than by the USA.

Masih andMasih (2001),

USA

“Long and short term

dynamic causal transmission

amongst international

stockmarkets”

Journal ofInternationalMoney and

Finance

13 9

VECM, VAR, generalized

impulse response analysis

Interdependencies were found between the

established OECD and the Asian markets, and

the leadership of the US and UK markets over

the short and long run was found.

Siklos and Ng(2001), Canada

“Integration among AsiaPacific and

internationalstock markets: a

commonstochastic trends

and regimeshifts”

PacificEconomicReview

20 7

Unit root (ADF and PP)

test, VAR and Johansen

cointegration test

All seven countries shared a single

commonstochastic trend and hence thus

found to beintegrated with

each other.

Seabra (2001),

“A co-integration

AppliedEconomics 10 6 Unit root (ADF)

test andThere was no common trend

26

Brazil

analysisbetween

Mercosur andinternational

stock markets”

Lettersbivariate and multivariate

cointegration tests

linking theArgentine and Brazilian stock price indexes.

While, cointegration was found in two Latin

American stock markets and the

US market.

Huang and Fok

(2001), Taiwan

“Stock market integration –

an application of the

stochastic permanent

breaksmodel”

AppliedEconomics

Letters8 10

Stochastic permanent

breaks (STOPBREAK)

model and Johansen’s

cointegration test

The US stock market was temporarily

cointegrated with the markets in

Japan,Germany, The

Netherlands and Switzerlandaccording to

STOPBREAK model. However,the US market is cointegrated only

with themarket in The Netherlands

according to theJohansen

cointegration test.

Tan and Tse(2001),

Singapore

“The integration of the East

and South-East Asian equity

markets”

www.mysmu.Edu 12 9

Correlation, Granger

causality test, theGeweke’s measure

offeedback, VAR and

IRF

The linkages and interactions between themarkets have

increased substantially in

postcrisis era.

Johnson andSoenen (2002),USA

“Economic integration andstock market comovements

in the America”

Journal ofMultinational

FinancialManagement

11 9Geweke measure

andpooled regression

Eight equity markets of the Americas were

highly associated with the USA.

George Filis (2006), Greece

Testing for Market

Efficiency in Emerging Markets :

Evidence from the Athens Stock

Exchange

Journal of Emerging

Market Finance

2 1

Unit roottests (ADF test), standard random

walk test, GARCH effects, Wilcoxon Signed Rank test

ASE is not semi-strong form

efficient in either of the two years

then itcannot be strong form efficient, as

well.

Ana Paula Serra (2003),

Portugal

The Cross-Sectional

Determinants of Returns:

Evidence from Emerging

Markets' Stocks

Journal of Emerging

Market Finance

7 21

Time-series standard deviations, explanatory powerstatistic, precision

weights, correlation

Most important factors arecommon to

emerging markets and these

important factors are similar

to those identified in previous studies

for mature markets.

27

W.I.C.S. Gunasinghe (2005), Sri

Lanka

Behaviour of Stock Markets in South Asia : An

Econometric Investigation

South Asia Economic

Journal11 3

Unit Root Tests, Correlation Matrix,

Generalized Impulse Response

Functions

a sudden change of the Indian stock

market may have some marginal

spillover effect on Sri Lanka and Pakistan stock

price movements. It further means

that the Sri Lankan and Pakistani stock

marketsare marginally vulnerable to

spillover effects that could

originate from the Indian

stock market.

Ingrid M. Werner and

Allan W. Kleidon

(1993), USA

U.K. and U.S. Trading of

British Cross-listed stocks : An

Intraday Analysis of

Market Integration

The Review of Financial

Studies11 2 Regression Model

The intraday patterns for British cross-listed stocks closely resemble

those of otherwise similar non-cross-

listed stocks.

GohSooKhoon (2007), Malaysia

Financial Liberalisation

and Openness in Malaysia

Journal Of Dissertation 11 1

ARCH-GARCH models, Regime

Switching models

A zero S-I correlation during

the financial liberalisation

period. Malaysia has exhibited a

substantial amount of, at least, de facto financial

openness despite the periodic

exchange controls.

3.3 RESEARCH METHODOLOGY

3.3.1 Research Objectives

The principal objective of the study is also to investigate into the degree of internal financial

integration among different segments of the Global financial sector in general, but that too

by looking into in particular the impact of selected macroeconomic variables on stock market

and vice versa during the period under study in terms of employing methods of time series

econometrics.

Statement of research objectives:

1. To check the distribution pattern in the select countries

2. To check the correlation among the select markets28

3. To check stationarity in the seven select markets

4. To check co-integration among the select markets

5. To find causality among the select markets

3.3.2 Hypothesis

Hypothesis 1: Stock Markets are normally distributed

Hypothesis 2: Moderate to very high correlation among all markets

Hypothesis 3: Existence of Unit Root (non-stionarity) in stock markets

Hypothesis 4: NO co-integration among stock markets

Hypothesis 5: No Causality is found between the US and other markets

3.3.3 Sample Size

The research will be based on the secondary data related to daily closing figures various

stock indices of various global stock markets over the period May 2000 to May 2014 The

daily returns of the sample stock markets are matched by the calendar date. It is assumed that

the timing of the trading sessions of the stock exchanges may not completely be related and

it will not add any value by taking into account the real trading timings of different stock

markets under study; therefore, the same has not been taken into account.

3.4 Research Design and Methodology

The following methods are used to test correlation, Stationary of time series, co-integration

and casualties between the stock market using the historical Data

(A) The Jarque-Bera Test is used to test whether returns of stock markets follow the normal

probability distribution.

(B) Pearson Correlation is used to find correlation between the stock market returns.

(C) Testing for stationary (unit root test) is done by using, both the Augmented Dickey-

Fuller and the Phillips-Perron Tests.

(D) Johansen Co-integration test is used for pinpointing the long run relationship among

the markets under study.

(E) For Causality, Granger Test is used which identify the direction of the influence from

one series to another.

29

Methodology

Mean: For a data set, the mean is the sum of the values divided by the number of values. The

mean of a set of numbers x1, x2,xn is typically denoted by, pronounced "x bar". This mean is

a type of arithmetic mean.

The arithmetic mean is the "standard" average, often simply called the "mean".

E.g. Mean of 5 numbers 12,20,22,32 & 34 is

12+20+22+32+34 = 24

5

Median: The median of a finite list of s can be found by arranging all the observations from

lowest value to highest value and picking the middle one. If there is an even of observations,

then there is no single middle value; the median is then defined to be the mean of the two

middle values.

Ex.1 - Calculation of median for the given set. 59,58,57,55,55,54,53?

Ans: Step 1 has to arrange the given set in ascending order.

The ordered list of given set is 53,54,55,55,57,58,59.

Step 1.Here, the given set is odd. So we have to pick out the middle.

Therefore, the median of the set is 55.

Ex.2 - Calculation of median for the given set. 58, 56, 59, 55, 57, 54, 53, 52

Step 1.We have to arrange the given set in ascending order.

The ordered list of given set is 52, 53, 54, 55, 56, 57, 58, 59.

Step 2.Here, the given set is even.

So we have to pick out the middle of two s and we have to find average or mean of those s.

Therefore, the median of the set is 55+56/2 = 55.5.

Standard Deviation-The standard deviation is a useful and popular statistic for calculating

the degree of spread around the mean. The standard deviation for a population is denoted by

σ and calculated using the following equation:-

Where μ is the mean of the population and is N the size of the population.30

The standard deviation is a measure of the average distance of the values from their mean. If

the value of the standard deviation tends to zero, it means that the values are close to the

mean; on the other hand, the higher the standard deviation is, the longer the distance to the

mean. If all the data values are equal, the standard deviation will be zero.

Skewness: Mean and variance may not describe an investment’s return adequately. In

calculations of variance, the deviations around the mean are squared, so it could not be

known whether large deviations are likely positive or negative; is important then to analyze

other important characteristics like the degree of symmetry in the distribution.

If the distribution is symmetrical about its mean each side of the distribution will be a mirror

image of the other. Talking about investments, the gain and losses intervals would exhibit

the same frequencies. This analysis is also used extensively in Risk Management.

A normal distribution will have an equal mean and median, it is completely described by its

mean and variance and roughly 68 percent of the observations lie between plus and minus

one standard deviation; 95 percent between plus and minus two standard deviations and 99

percent lie between plus and minus three standard deviations from the mean. A distribution

that is not symmetrical is called skewed. A distribution with positive skewness (skewed to

the right) has frequent small losses and a few extreme gains; a distribution with negative

skewness (skewed to the left) has few extreme losses and frequent small gains.

Exhibit 4.7 shows positively and negatively skewed distribution, the positively skewed

distribution has a long tail on its right side where the mode is less than the median; the

negatively one has a long tail on its left side and the median is less than the mode.

Skewness is computed using each observation’s deviation from its mean as the average

cubed deviation from the mean standardized by dividing by the standard deviation cube to

31

make the measure free of scale. A symmetric distribution has skewness equal to zero; a

positive or negative result will indicate if the skewness is positive or negative. Cubing the

numerator will preserve the sign comparing the calculation with the standard deviation.

The sample skewness or relative

skewness can be computed using

equation

If the size of the population or the number of observations is to large, equation can be

reduced to

Kurtosis: Kurtosis is the statistical measure that tells when a distribution is more or less

peaked than a normal distribution. A distribution that is more peaked than normal is called

leptokurtic (lepto from the Greek, slender); this distribution has fatter tails than the normal

distribution. A distribution that is less peaked than the normal is called platykurtic (platy

from the Greek word for broad), and a distribution identical to the normal is called

mesokurtic (messo being the Greek word for middle). Exhibit 4.8 shows the three different

types of kurtosis. The equation to calculate the kurtosis involves finding the average of

deviations from the mean raised to the power of four and then standardizing that average by

dividing by the standard distribution rose to the fourth power. A normal or other mesokurtic

distribution has a kurtosis equal to zero. A leptokurtic distribution has a kurtosis greater than

zero and a platykurtic distribution less than zero. A kurtosis of 1.0 or larger would be

considered unusually large.

To calculate the kurtosis from a sample equation is used.

32

Correlation: The correlation indicates the strength and direction of a linear relationship

between two random variables. Correlation refers to the departure of two variables from

independence. The correlation YX, ρ between two random variables X and Y with expected

values μ and ν, respectively, and standard deviations Xσ and Yσ is given by

The correlation is defined only if both standard deviations are finite and both of them are

nonzero. The correlation cannot exceed 1 in absolute value, meaning that the value for a

correlation goes from -1 to 1.

Augmented Dickey–Fuller test (ADF): Is a test for a unit root in a time series sample. It is an

augmented version of the Dickey–Fuller test for a larger and more complicated set of time

series models. The augmented Dickey–Fuller (ADF) statistic, used in the test, is a negative

number. The more negative it is, the stronger the rejection of the hypothesis that there is a

unit roots at some level of confidence. The testing procedure for the ADF test is the same as

for the Dickey–Fuller test but it is applied to the model

Where α is a constant, β the coefficient on a time trend and p the lag order of the

autoregressive process. Imposing the constraints α = 0 and β = 0 corresponds to modelling a

random walk and using the constraint β = 0 corresponds to modelling a random walk with a

drift.

Co-integration:- xt and yt are said to be co-integrated if there exists a parameter α such that

ut = yt - α xt is a stationary process.

33

Johansen Co integration Test: This test is named after Søren Johansen, is a procedure for

testing co-integration of several I(1) time series. This test permits more than one co-

integrating relationship so is more generally applicable than the Engle–Granger test which is

based on the Dickey–Fuller (or the augmented) test for unit roots in the residuals from a

single (estimated) co-integrating relationship.

There are two types Johansen test, either with trace or with Eigen value, and the inferences

might be a little bit different. The null hypothesis for the trace test is the number of co-

integration vectors r ≤? The null hypothesis for the Eigen value test is r =?.

Just like a unit root test, there can be a constant term, a trend term, both, or neither in the

model. For a general VAR(p) model:

JarqueBera Test: The Jarque-Bera test is used to check hypothesis about the fact that a given

sample xS is a sample of normal random variable with unknown mean and dispersion. As a

rule, this test is applied before using methods of parametric statistics which require

distribution normality. This test is based on the fact that skewness and kurtosis of normal

distribution equal zero. Therefore, the absolute value of these parameters could be a measure

of deviation of the distribution from normal. Using the sample Jarque-Bera statistic is

calculated:

(Here n is a size of sample), then p-value is computed using a table of distribution quantiles.

It should be noted that as n increases, JB-statistic converges to chi-square distribution with

two degrees of freedom, so sometimes in practice table of chi-square distribution quantiles is

used. However, this is a mistake - convergence is too slow and irregular.

For example, even if n = 70 (which is rather big value) and having JB = 5 chi-square

Distribution quartiles gives us p-value p = 0.08, whereas real p-value equals 0.045. So, we

can accept the wrong hypothesis. Therefore it's better to use the specially created table of

Jarque-Bera distribution quantiles.

34

Granger Causality Test: Is a statistical hypothesis test for determining whether one time

series is useful in forecasting another. Ordinarily, regressions reflect "mere" correlations, but

Clive Granger, who won a Nobel Prize in Economics, argued that there is an interpretation of

a set of tests as revealing something about causality.

A time series X is said to Granger-cause Y if it can be shown, usually through a series of t-

tests and F-tests on lagged values of X (and with lagged values of Y also included), that

those X values provide statistically significant information about future values of Y.

Method

The test for Granger causality works by first doing a regression of ΔY on lagged values of

ΔY. (Here ΔY is the first difference of the variable Y — that is, Y minus its one-period-prior

value. The regressions are performed in terms of ΔY rather than Y if Y is not stationary but

ΔY is.) Once the set of significant lagged values for ΔY is found (via t-statistics or p-values),

the regression is augmented with lagged levels of ΔX. Any particular lagged value of ΔX is

retained in the regression if (1) it is significant according to a t-test, and (2) it and the other

lagged values of ΔX jointly add explanatory power to the model according to an F-test. Then

the null hypothesis of no Granger causality is accepted if and only if no lagged values of ΔX

have been retained.

Mathematical statement

Let y and x be stationary time series. To test the null hypothesis that x does not Granger-

cause y, one first finds the proper lagged values of y to include in a univariateauto regression

of y:

yt = a0 + a1yt − 1 + a2yt − 2 + ... + amyt − m + residualt.

Here yt − j is retained in the regression if and only if it has a significant t-statistic; m is the

greatest lag length for which the lagged dependent variable is significant.

Next, the auto regression is augmented by including lagged values of x:

yt = a0 + a1yt − 1 + a2yt − 2 + ...amyt − m + bpxt − p + ... + bqxt − q + residualt.

One retains in this regression all lagged values of x that are individually significant

according to their t-statistics, provided that collectively they add explanatory power to the

regression according to an F-test (whose null hypothesis is no explanatory power jointly

added by the x's). In the notation of the above augmented regression, p is the shortest, and q

is the longest, lag length for which the lagged value of x is significant.

The null hypothesis that x does not Granger-cause y is accepted if and only if no lagged

values of x are retained in the regression.35

3.5 APPLICATION OF THE RESEARCH:

1) The research will help in understanding the integration among the major world markets

and subsequently the cause-effect of the co integration of the markets, which in turn will

help in finding the impact of the financial and other economic crises on the different

countries.

2) The research will help in construction of the efficient international portfolios both for

individuals and the firms.

3) The research will be a guiding tool for cross-border financial mergers and acquisitions

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Chapter 4

Data Analysis

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TSEC_TAIWAN SSE_CHINA HENGSENG_HK SENSEX_INDIA

Mean 6828.545 2221.763 17374.88 11536.61

Median 6956.880 2071.430 17501.48 12217.81

Maximum 9809.880 6092.060 31638.22 24363.05

Minimum 3446.260 1011.500 8409.010 2600.120

Std. Dev. 1398.331 890.0512 4886.312 6360.422

Skewness -0.188707 1.556285 0.030802 -0.021381

Kurtosis 2.038625 6.061779 1.978690 1.435410

Jarque-Bera 135.2466 2416.977 132.7344 310.6104

Probability 0.000000 0.000000 0.000000 0.000000

Sum 20779263 6760825. 52871758 35105914

Sum Sq. Dev. 5.95E+09 2.41E+09 7.26E+10 1.23E+11

Observations 3043 3043 3043 3043

TABLE 1: DESCRIPTIVE STATISTICS

TEST 1: NORMALITY TEST (JarqueBera test for Normality)The higher is the Standard Deviation, the higher will be the dispersion.A positive Skewness and Kurtosis shows longer tail towards right whereas a negative shows longer tail towards left.The benchmark of Kurtosis is 3 which should be either less than or more than 3.We need to reject null if p value is less than 0.05.And to transform data to normal form we need to take out returns.Normality test – the test used here is jarquebera test.The null hypothesis is H0 –The Data/residuals are normally distributed.

H1-the data/residuals are not normally distributedTest statistics: p<0.05 The data is not normally distributed as p value is less than 0.05 so the null can’t be rejected at level.p values not more than 0.05 even at the first difference (returns) but from the graph, the data seems behaving normal as the skewness has decreased.

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TABLE 2: CORRELATION MATRIX

TSEC_TAIWAN SSE_CHINA SENSEX_INDIA HENGSENG_HK

TSEC_TAIWAN 1 0.62 0.81 0.89

SSE_CHINA 1 0.59 0.74

SENSEX_INDIA 1 0.92

TEST 2: CORRELATION TEST

From the correlation matrix using Karl Pearson’s coefficient of correlation if p value is more than 0.05 the correlation between the two countries will be strong.

The highest correlation is shown between India and Hong Kong= 0.92

The lowest correlation is shown between China and India=0.59

GRAPH 1: CHINA INDICE VALUE

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GRAPH 2: CHINA RETURNS VALUE

GRAPH 3: TAIWAN INDICE VALUE

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GRAPH 4: TAIWAN RETURNS VALUE

GRAPH 5: INDIA INDICE VALUE

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GRAPH 6: INDIA RETURNS VALUE

GRAPH 7: HONG KONG INDICE VALUE

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GRAPH 8: HONG KONG RETURNS VALUE

TEST 3: TESTOF STATIONARITY (UNIT ROOT TEST)

The test used here is Augmented Dickey Fuller (ADF)

H0=there is unit root (non stationary)

H1=there is no unit root (stationary)

Null should be rejected and p value should be less than 0.05

TABLE 3: CHINA INDICE UNIT ROOT TEST

Lag Length: 0 (Automatic - based on SIC, maxlag=28)t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -1.451442 0.5583

Test critical values: 1% level -3.432310

5% level -2.862291

10% level -2.567214

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GRAPH 9: CHINA INDICE VALUE (HISTOGRAM)

The null hypothesis is: 3040

H0 –There is unit root

H1- There is not unit root

Test statistics: p value >0.05

The data at level 1 is not stationary as p value is more than 0.05 so the null should be

not be rejected (refer to TABLE 3 and GRAPH 9).

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TABLE 4: CHINA RETURNS VALUE UNIT ROOT TEST

Lag Length: 0 (Automatic - based on SIC, maxlag=28)

t-Statistic Prob.*



5% level -2.862292

10% level -2.567214

GRAPH 10: CHINA RETURNS VALUE (HISTOGRAM)

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Test statistics: p value <0.05

The data at level 1 is stationary as p value is less than 0.05 so the null should be

rejected (refer to TABLE 4 and GRAPH 10).

TABLE 5: TAIWAN INDICE VALUE UNIT ROOT TEST

Lag Length: 1 (Automatic - based on SIC, maxlag=28)

t-Statistic Prob.*



5% level -2.862292

10% level -2.567214

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GRAPH 11: TAIWANINDICE VALUE (HISTOGRAM)






not be rejected (refer to TABLE 5 andGRAPH 11).

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TABLE 6: TAIWAN RETURNS VALUE UNIT ROOT TEST

Null Hypothesis: RT_TAIWAN has a unit root

t-Statistic Prob.*



5% level -2.862292

10% level -2.567214

GRAPH 12: TAIWAN RETURNS VALUE (HISTOGRAM)

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TABLE 7: INDIA INDICE VALUE UNIT ROOT TEST

Null Hypothesis: SENSEX_INDIA has a unit root

t-Statistic Prob.*



5% level -2.862292

10% level -2.567214

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GRAPH 13: INDIA INDICE VALUE (HISTOGRAM)







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TABLE 8: INDIA RETURNS VALUE UNIT ROOT TEST

Null Hypothesis: RT_INDIA has a unit root

t-Statistic Prob.*



5% level -2.862292

10% level -2.567214

GRAPH 14: INDIA RETURNS VALUE (HISTOGRAM)

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The null hypothesis is:






TABLE 9: HONG KONG INDICE VALUE UNIT ROOT TEST

Null Hypothesis: HENGSENG_HK has a unit root t-Statistic Prob.*



5% level -2.862291

10% level -2.567214

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GRAPH 15: HONG KONG INDICE VALUE (HISTOGRAM)







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TABLE 10: HONG KONG RETURNS VALUE UNIT ROOT TEST

Null Hypothesis: RT_HK has a unit root

t-Statistic Prob.*



5% level -2.862292

10% level -2.567215

GRAPH 16: HONG KONG RETURNS VALUE (HISTOGRAM)

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The null hypothesis is:






TEST 4: JOHANSEN’S CO-INTEGRATION RANK TEST

Date: 11/24/15 Time: 15:46

Sample (adjusted): 5/15/2000 5/19/2014

Included observations: 3038 after adjustments

Trend assumption: Linear deterministic trend

Series: SSE_CHINA TSEC_TAIWAN HENGSENG_HK SENSEX_INDIA

Lags interval (in first differences): 1 to 4

Unrestricted Co integration Rank Test (Trace)

Hypothesized Trace 0.05

No. of CE(s) Eigen value Statistic Critical Value Prob.**

None * 0.010617 58.85463 47.85613 0.0033

At most 1 0.007420 26.42915 29.79707 0.1164

At most 2 0.001251 3.803529 15.49471 0.9187

At most 3 1.78E-07 0.000542 3.841466 0.9833

The data should be of integration 1

The Null Hypothesis:

H0- There is no co integration

H1- There is co integration

Test Statistics :

p value <0.05

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For 4 countries we get 4 nulls that is none, at most 1,at most 2,at most 3 and here at none the p value is more than 0.05 therefore H0 is not rejected and we will not move further to H1

Johansen Test of Co integration is for long term and the integration series =(n-1)

TEST 5: GRANGER’S CAUSALITY TEST

Pair wise Granger Causality TestsDate: 11/24/15 Time: 15:50Sample: 5/02/2000 5/19/2014Lags: 2 Null Hypothesis: Obs F-Statistic Prob. RT_TAIWAN does not Granger Cause RT_CHINA 3040 1.54030 0.2145 RT_CHINA does not Granger Cause RT_TAIWAN 38.5000 3.E-17

RT_HK does not Granger Cause RT_CHINA 3040 7.35943 0.0006 RT_CHINA does not Granger Cause RT_HK 136.076 3.E-57

RT_INDIA does not Granger Cause RT_CHINA 3040 3.81693 0.0221 RT_CHINA does not Granger Cause RT_INDIA 1.92877 0.1455

RT_HK does not Granger Cause RT_TAIWAN 3040 22.9294 1.E-10 RT_TAIWAN does not Granger Cause RT_HK 2.61503 0.0733

RT_INDIA does not Granger Cause RT_TAIWAN 3040 170.787 5.E-71 RT_TAIWAN does not Granger Cause RT_INDIA 0.09067 0.9133

RT_INDIA does not Granger Cause RT_HK 3040 328.610 7E-130 RT_HK does not Granger Cause RT_INDIA 3.05732 0.0472

The data should be for short run and the data should be stationary thus returns should be calculated first.

The Null Hypothesis:

H0 = Country X doesn’t Granger cause country Y

H1= Country X Granger cause country Y

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INTERPRETATION:

(1) As per the Granger Causality Test there is no causality between the TAIWAN STOCK EXCHANGE and CHINASTOCK EXCHANGE as the value of p is greater than 0.05 which means that even if there is change in the TAIWAN STOCK EXCHANGE it will not cause any change in the CHINA STOCK MARKET.

(2)As per the Granger Causality Test there is causality between the CHINA STOCK EXCHANGE and TAIWAN STOCK EXCHANGE as the value of p is less than 0.05 which means that if there is change in CHINA STOCK EXCHANGE it will results in change in the TAIWAN STOCK EXCHANGE.

(3)As per the Granger Causality Test there is causality between the HONG KONG STOCK EXCHANGE and CHINASTOCK EXCHANGE as the value of p is less than 0.05 which means that if there is change in HONG KONG STOCK EXCHANGE it will results in change in the CHINA STOCK EXCHANGE.

(4)As per the Granger Causality Test there is causality between the CHINA STOCK EXCHANGE and HONG KONGSTOCK EXCHANGE as the value of p is less than 0.05 which means that if there is change in CHINA STOCK EXCHANGE it will results in change in the HONG KONG STOCK EXCHANGE.

(5)As per the Granger Causality Test there is causality between the INDIAN STOCK EXCHANGE and CHINA STOCK EXCHANGE as the value of p is less than 0.05 which means that if there is any change in the INDIAN STOCK EXCHANGE it will result in change in the CHINA STOCK EXCHANGE.

(6)As per the Granger Causality Test there is no causality between the CHINA STOCK EXCHANGE and INDIAN STOCK EXCHANGE as the value of p is greater than 0.05 which means that even if there is any change in the CHINA STOCK EXCHANGE it will not cause any change in the INDIAN STOCK EXHCHAGE.

(7)As per the Granger Causality Test there is causality between the HONG KONG STOCK EXCHANGE and TAIWANSTOCK EXCHANGE as the value of p is less than 0.05 which means that if there is any change in the TAIWAN STOCK EXCHANGE it will result in change in the HONG KONG STOCK EXCHANGE.

(8)As per the Granger Causality Test there is no causality between the TAIWAN STOCK EXCHANGE and HONG KONG STOCK EXCHANGE as the value of p is greater than 0.05 which means that even if there is any change in the TAIWAN STOCK EXCHANGE it will not cause any change in the HONG KONG STOCK EXHCHAGE.

(9)As per the Granger Causality Test there is causality between the INDIAN STOCK EXCHANGE and TAIWANSTOCK EXCHANGE as the value of p is less than 0.05 which means that if there is any change in the INDIAN STOCK EXCHANGE it will result in change in the TAIWAN STOCK EXCHANGE.

(10)As per the Granger Causality Test there is no causality between the TAIWAN STOCK EXCHANGE and INDIAN STOCK EXCHANGE as the value of p is greater than 0.05

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which means even If there is any change in the TAIWAN STOCK EXCHANGE it will cause no effect on the INDAIN STOCK EXCHANGE.

(11)As per the Granger Causality Test there is causality between the INDIAN STOCK EXCHANGE and HONG KONGSTOCK EXCHANGE as the value of p is less than 0.05 which means that if there is any change in the INDIAN STCOK EXCHANGE it will result in a change in the HONG KONG STOCK EXCHANGE.

(12)As per the Granger Causality Test there is causality between the HONG KONG STOCK EXCHANGE and INDIANSTOCK EXCHANGE as the value of p is less than 0.05 which means that if there is any change in the HONG KONG STCOK EXCHANGE it will result in a change in the INDIAN STOCK EXCHANGE.

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Chapter 5

Summary & Conclusion

59

CONCLUSION

The historical data is in the form of index values of the selected share markets under which

the study has been taken for the analysis. The data at level is not normally distributed. So the

data has been transformed into normal data by taking difference of log called as the first

difference, thus the return series is generated to carry out all the test; otherwise the tests

won’t be possible. For normality test we use JarqueBera test. The data is not normally

distributed as p value is less than 0.05 so the null should be rejected at level. p value is not

more than 0.05 even at the first difference (returns) but from the graph, the data seems

behaving normal as the skewness has decreased. Here, null should be rejected. If standard

deviation is higher, the dispersion will also be higher. Karl Pearson’s Co-efficient test is used

to find out the strength of the correlation between two countries. We found that the standard

deviation is highest in India and lowest in China. The correlation is maximum between India

and Hong Kong and the lowest is between China and India. ADF test is used to measure the

stationarity of the data. The normal data at level is not stationary so null cannot be rejected.

But in case of returns data at difference is stationary, and thus alternate can be accepted as

there is no unit root and the data is stationary. Johansen’s co-integration test basically is used

to make the data in parallel situation. The Granger’s causality test is done to see which

country causes the change in other. The countries which are causing Granger causality test

are,as per the Granger Causality Test there is causality between the INDIAN STOCK

EXCHANGE and CHINA STOCK EXCHANGE as the value of p is less than 0.05 which

means that if there is any change in the INDIAN STOCK EXCHANGE it will result in

change in the CHINA STOCK EXCHANGE.As per the Granger Causality Test there is no

causality between the TAIWAN STOCK EXCHANGE and CHINA STOCK EXCHANGE

as the value of p is greater than 0.05 which means that even if there is change in the

TAIWAN STOCK EXCHANGE it will not cause any change in the CHINA STOCK

MARKET.As per the Granger Causality Test there is causality between the HONG KONG

STOCK EXCHANGE and INDIAN STOCK EXCHANGE as the value of p is less than 0.05

which means that if there is any change in the HONG KONG STCOK EXCHANGE it will

result in a change in the INDIAN STOCK EXCHANGE.

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REFERENCES

1. Masih, A.M.M. and Masih, R. (1999), “Are Asian stock market fluctuations due mainly to intra-regional contagion effects? Evidence based on Asian emerging stock markets”, Pacific Basin Finance Journal, Vol. 7 Nos 3-4, pp. 251-82.

2. Maysami, R.C. and Koh, T.S. (2000), “A vector error correction model of the Singapore stock market”, International Review of Economic and Finance, Vol. 9 No. 1, pp. 79-96.

3. Sheng, H.C. and Tu, A.H. (2000), “A study of cointegration and variance decomposition among national equity indices before and during the period of the Asian financial crisis”, Journal of Multinational Financial Management, Vol. 10 Nos 3/4, pp. 345-65.

4. Chang, T. and Nieh, C.C. (2001), “International transmission of stock price movements among Taiwan and its trading partners: Hong Kong, Japan and the United States”, Review of Pacific Basin Financial Markets and Policies, Vol. 4 No. 4, pp. 379-401.

5. Masih, R. and Masih, A.M.M. (2001), “Long and short term dynamic causal transmission amongst international stock markets”, Journal of International Money and Finance, Vol. 20 No. 4, pp. 563-87.

6. Siklos, P.L. and Ng, P. (2001), “Integration among Asia Pacific and International stock markets: a common stochastic trends and regime shifts”, Pacific Economic Review, Vol. 6 No. 1, pp. 89-110.

7. Seabra, F. (2001), “A co-integration analysis between Mercosur and international stock markets”, Applied Economics Letters, Vol. 8 No. 7, pp. 475-8.

8. Huang, B.N. and Fok, R.C.W. (2001), “Stock market integration – an application of the stochastic permanent breaks model”, Applied Economics Letter, Vol. 8 No. 11, pp. 725-9.

9. Tan, K.B. and Tse, Y.K. (2001), “The integration of the East and South-East Asian equity markets”, available at: http://staff.mysmu.edu/yktse/ICSEAD_paper.pdf (accessed 25 November 2010).

10. Johnson, R. and Soenen, L. (2002), “Economic integration and stock market comovements in the Americas”, Journal of Multinational Financial Management, Vol. 13 No. 1, pp. 85-100.

Websites Visited

https://en.wikipedia.org/wiki/Bombay_Stock_Exchange

http://www.bseindia.com

https://en.wikipedia.org/wiki/Shanghai_Stock_Exchange

http://english.sse.com.cn

https://en.wikipedia.org/wiki/Taiwan_Stock_Exchange

http://www.twse.com.tw/en

https://en.wikipedia.org/wiki/Hong_Kong_Stock_Exchange

http://www.hkex.com.hk/eng/index.htm

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EQUITY MARKET INTEGRATION IN SELECTED MARKETS: EVIDENCE FROM UNIT ROOT AND CO INTEGRATION TESTS

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Transcript of EQUITY MARKET INTEGRATION IN SELECTED MARKETS: EVIDENCE FROM UNIT ROOT AND CO INTEGRATION TESTS