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Due to inclination towards liberalization and deregulation in the capital and money markets, global markets have tended to become highly integrated in recent times in case of developed as well as developing countries. There are many reasons as to why the linkages among the different stock markets should be studied some of the reasons are emerging markets have attracted a great number of foreign investors, removal of statutory controls over their capital market and foreign exchange, stock prices interconnection due to the global capital movements, regional policy and the presence of economic ties.
Specialists of finance have given substantial attention to the linkages and the relationships between different stock markets, to explore and examine the potential benefits from international portfolio diversification. Most of the studies are done taking into account developing and emerging Asian markets. Interest of foreign investors have resulted in several fund management centres concentrating on Asian developing markets not only for the growth and development but also to diversify their risk.
The aim of this paper is to study the relationship of developed and emerging stock markets. Literatures on the different prospects of stock market have been studied. Many researchers have focused on the integration among the stock market. While studying the literatures it has been seen that different areas are being covered and focused which includes dynamic linkages among stock market during pre and post Asian financial crisis and Russian financial crisis, effect of linkages on the portfolio diversification, effects of linkages on the daily stock prices and domination of developed markets over the developing markets. Further, examining of the empirical question in the literature on capital market integration between different economies is done.
For the empirical analysis, data of twenty year for everyday closing stock prices of six indices have been taken from 3 January 1989 to 8 June 2009. Six indices are New York Stock Exchange (USA), London Stock Exchange (UK), Tokyo Stock Exchange (Japan), Bombay Stock Exchange (India), The Stock Exchange of Thailand (Thailand), Bursa Malaysia (Malaysia). In the econometrics literature, there exist a number of alternative methods to estimate cointegration. Econometrics techniques which are being used in this study are Augmented Dickey-Fuller test, Johansen’s cointegration test and Error Correction test. E-views software is used for the calculating the results. Empirical results obtained from the three test, it was found that time series are non stationary and null hypothesis is not rejected which suggest that they are highly cointegrated and to test whether any variations in one stock exchange can lead to fluctuations in other stock indices. Johansen cointegration test is conducted which shows that there is no evidence of cointegration between Indian stock index and other stock indices.
Further, Error Correction test is conducted which shows that there is poor cointegration between Indian stock exchange index with other stock indices. Indian stock market appear to be least integrated with Malaysia, where as Malaysia stock market is integrated with all the other stock markets. Thailand stock market is seems to be more dependent on Japanese and Indian stock market than other stock markets. Little integration is seen between Japanese stock exchange and USA stock exchange. It is found that UK and USA are highly integrated. To conclude, stock exchanges of the developed economies are better cointegrated as compared to those of developing economies.
What is stock market?
In simple words stock refers to a supply. But in financial market terms, stock refers to the money which a company has raised. And the supply of the money comes from the people who invest in the company in hope that the company will make their money grow. Stocks exist because it enables the company to “sell” pieces of the business called as stocks (equity securities) in need of long term financing. When stocks are issued by corporations are owned by the public at large which includes both private investors and institution are said to be publicly held.
A public place where things are bought and sold is called as Market. And the term stock market refers to a business where stock is bought and sold. Stock market can be splitted into two main sectors; the primary market and the secondary market. The primary market is the one where new issues are offered for the first time and primary market is the one where subsequent trading goes on. There are basically two types of stock namely common stock and preferred stock.
A security which represents ownership in a corporation is known as common stock. Holder of the common stock has the power to vote and elect board members. If the company goes bankrupt, the common stockholder will not be paid until unless creditors, bondholders and preferred stockholders are paid their share of the leftover assets of the company. Where as, preferred stock is a stock which is issued when all the common stock has been issued. Preferred stock olders are given dividends. They have a preference that is why they are paid dividend before any dividends are paid to common stock holders.
The stock market is not a specific place but still some people use the term “Wall Street” which is the main street in New York City’s financial district and it is referred to the US stock market.
Why companies issue stock market and why people buy it?
As every company wants to grow, so some owners build more factories and some develop new product which needs money. A company can actually get loan from the financial institution like banks but companies without going into debt by taking loans issues stock which raise money for the growth of a company. Only Business Corporation can issue the stock which has special legal rights and responsibility. A proprietorship or ownership cannot issue stock.
A shareholder invests in a hope that company will grow and so will their money grow because if a company earns money, the shareholders will share the profits. There are different types of gains from the stock such as dividends, capital gains, short selling, risk and rewards for investing. Over the long term bases, investments in stocks have proven to be an excellent way to more than keep pace with erosive effects of inflation.
Stock market is an organised market for trading of stocks and bonds. These markets were originally open to all but now a days only members of the association can buy and sell directly and these members or stock broker can buy and sell for themselves or others by charging the commission for their provided service. A stock can only be bought and sold if it is listed on an exchange. There are stock exchange in all the financial centres of the world. Some of them are stated below; the New York stock exchange since 1792 which had the largest trading in the world of $7.3 trillion in 1998, Tokyo stock exchange, London stock exchange, Bombay stock exchange and NASDAQ. NASDAQ was the first exchange which recognised the role of electronics in stock market.
History of the Stock Exchanges
In the decade of 1870s, introduction of a securities system initiated the public bond negotiation in Japan which resulted in the need of a public institution for trading and hence in May 1878, the “Stock Exchange Ordinance” was in enacted followed by establishment of Tokyo Stock Exchange Co. Ltd. On May 15, 1879 and trading began on June 1st.
On June 30, 1943, establishment of a quasi-public corporation named the “Japan Securities Exchange” took place by uniting all 11 stock exchanges throughout Japan. During the Second World War, the trading sessions were suspended on August 10, 1945 but the trading restarted under the management of unofficial group transactions in December 1945. Japan Securities Exchange was dissolved on April 16, 1947. Three stock exchanges in Tokyo, Osaka and Nagoya were founded on April 1, 1949 and trading began on May 16 followed by formation of five additional stock exchanges in July in Kobe (dissolved, October 1967), Hiroshima, Kyoto (merged into Tokyo Stock Exchange, March 2001), Fukuoka and Niigata.
In the beginning of the next decade of 1950s, margin transactions were introduced and bond trading started on April 2, 1956. October 1, 1966 observed the first listings of government bonds after the Second World War and in the following year, a new process of auction was put into action and “Baikai” trades (off-exchange trades) were eliminated. In April 1968, registration system was replaced by licensing system for securities companies and on July 1, 1969, Tokyo Stock Price Index (TOPIX) was launched. Joining the International Federation of Stock Exchanges (FIBV) along with starting of convertible bonds trading and Book Entry Clearing system were the major developments by TSE before listing of Yen-based foreign bonds and opening of Foreign Stock Section in 1973.
The next 10 years observed major developments in technical fields such as introduction of Market Information System (MIS) and Computer-assisted Order Routing and Execution System (CORES). From February 1, 1986 to May 23, 1988, a total of 32 securities companies joined the TSE membership out of which 22 were foreign companies. Trading in TOPIX futures, TOPIX options, U.S. T-Bond futures and Japanese government bond futures began by May 1990. Other 10 securities companies including 3 foreign ones joined the TSE membership followed by introduction of Floor Order Routing and Execution System (FORES) by the end of that year.
Major happenings in the next decade were:
Starting of Central Depository and Clearing System on Oct 9, 1991;
Listing of Nikkei 300 Stock Index Listed Fund on May 29, 1995;
Initiation of 5-year Japanese government bond futures trading on Feb 16, 1996;
Trading in equity options on July 18, 1997;
Calculation of new stock price index series on Apr 2, 1998; introduction of ToSTNet and TDnet (Timely Disclosure Network) in 1998; restriction on off-exchange trading for listed securities abolished on Dec 1, 1998;
50th Anniversary celebrations on Apr 2, 1999; introduction of Target (TSE wide area network) on June 1; brokerage commission liberalized in October; establishment of MOTHERS market for emerging companies and growth on Nov 11, 1999;
and merging of Hiroshima and Niigata stock exchanges into TSE along with introduction of TSE ARROWS in 2000.
Demutualization of TSE resulted in the formation of Tokyo Stock Exchange Inc. in 2001 and later on August 1, 2007, Tokyo Stock Exchange Group, Inc. was established. Tokyo Stock Exchange Regulation was established on October 17th with its commencement on November 1, 2007.
The present Thai market’s origin starts from the early years of 1960s when a private group established a stock exchange in July 1962 as a limited partnership which later turned into a limited company under the name of Bangkok Stock Exchange Co. Ltd. (BSE) in 1963. But BSE was relatively inactive irrespective of its good foundation as its annual turnover values reduced from being 160 million baht in 1968 to an all time low of 26 million baht in 1972, even when turnover in debentures were 87 million baht.
So finally, BSE stopped operating in early 1970s and the major reasons behind its failure were limited understanding of equity market among the investors and no government support officially. But, BSE’s concept was able to attract enough attention to form an organized securities market with official support. Hence, a plan to establish a market having apt facilities and regulations for securities trading was proposed by the Second National Economic and Social Development Plan (1967-1971). On recommendation of the World Bank in 1969, the government gained the works of Professor Sidney M. Robbins from Columbia University who studied different methods for the development of Thai capital market. And in the same year, the Bank of Thailand also created a working group for the development of capital market which was given the job of establishing the stock market.
After a year of intensive study, Professor Robbins generated an all-inclusive report named “A Capital Market in Thailand” and this report turned out to be the master plan required for the Thai capital market development in future. In 1972, the government brought some changes to the “Announcement of the Executive Council No. 58 on the Control of Commercial Undertakings Affecting Public Safety and Welfare” according to which the government now controlled and regulated the operations related to finance and securities companies. “The Securities Exchange of Thailand” also known as SET was passed in May 1974 after the amendments were made followed by the amending of the Revenue Code by the year-end. By 1975, the legislative framework was put into action and official trading at SET started on April 30, 1975. January 1, 1991 saw the changing of name from “The Securities Exchange of Thailand” to “The Stock Exchange of Thailand”.
In 1930, Singapore Stockbrokers' Association was Malaysia’s first formal securities business organisation establishment and in 1937 was re-registered by the name of Malayan Stockbrokers' Association. The public shares trading began after the establishment of The Malayan Stock Exchange in 1960 and the board system was having its trading rooms in Kuala Lumpur as well as Singapore, connected by usage of direct telephone line. The year 1964 saw the foundation of the Stock Exchange of Malaysia but in 1965, the withdrawal of Singapore from Malaysia forced the Stock Exchange of Malaysia to become the Stock Exchange of Malaysia and Singapore.
In 1973, the Stock Exchange of Malaysia and Singapore was divided into two separate markets namely the Kuala Lumpur Stock Exchange Berhad and the Stock Exchange of Singapore due to ceasing of interchangeability of currency between Malaysia and Singapore. The Kuala Lumpur Stock Exchange integrated on December 14, 1976 as a company limited by guarantee took over the operations and management of the Kuala Lumpur Stock Exchange Berhad. On April 14, 2004, the demutualization exercise made the name to be changed to Bursa Malaysia Berhad. The main aim of this exercise was to boost competitive position and to act in response to trends in the exchange sector globally by becoming more market-oriented and customer-driven.
The listing of Bursa Malaysia on the Main Board of Bursa Malaysia Securities Berhad took place on 18 March 2005. The certifications for conformance to the ISO 9001:2000 Quality Management System and ISO 14001:2004 Environmental Management System standards were received by the exchange on 5 October 2007. Faster processing and execution of orders and providing wider trading functions and features were done by introduction of Bursa Trade Securities as a new trading platform in Dec 2008.
The New York stock exchange trace back to 172, when twenty four New York City stock brokers and merchants signed the Buttonwood Agreement. At that time five securities were traded in New York City out of which three were government bonds and two were bank stocks. It was agreed that securities will be traded on commission basis on signing the Buttonwood agreement by the brokers. After the war in 1815 securities market in New York began to grow. The New York stock and exchange board was formed on March 8, 1817.
The name was shortened The New York Stock Exchange (NYSE) in 1863. More than 2800 companies are listed in NYSE which are having value exceeding $15 trillion. During the period 1824 to 1830 annual trading reached a peak of 380,000 shares. Average volume reached to 8500 shares which show that it increased a 50-fold in seven years. During 1836-1853 NYS&EB prohibited trading in the street and in 1837 average daily volume fell down from 7393 in January to 1534 by June. Due to invention of telegraph, brokers and investors broaden the market participation outside New York City.
It was a panic period during 1857 when Ohio Life Insurance & Trust company collapsed, prices dropped eight to ten percent in the single trading session and there was 45% decline in market value in the beginning of the year. During 1860s first stock ticker came into existence, membership in NYSE became a “property right”, prohibition of issue of shares in secret known as watering stock and at the end on 24th September 1869, gold speculation resulted in “Black Friday”. In 1890s NYSE established clearing house, it also recommended that all listed companies will send their shareholder the annual report and in 1896.
The Dow Jones Industrial Average was published by the Wall Street journal for the first time, with an initial value of 40.74. During that period DJIA topped 100 for the first time. Federal Reserve System Wall Street became world financial leader. Centralized stock clearing system was established and fraud bureau was established during the period. In the mid of 1929 Black Thursday came when market crashed on volume of over 16 million shares which was the beginning of the Great depression and the Dow finally reached bottom in July 1932. During 1960-1979, International Federation of stock Exchange and daily volume on the NYSE exceeded 4 million shares nearly triple the level immediately following the war. On February 03, 1977 foreign broker were permitted membership on the floor. The Inter market Trading system (ITS) was inaugurated.
Taking about 20th century, first Global index was launched in 2000, DJIA experienced its largest one day point gain and new trading room at 30 Broad street was opened. In 2001, NYSE volume topped 2 billion shares. The NYSE is now a for-profit business. It is formed out of the merger of the NYSE and Archipelago Holding, Inc. And the merger is the largest ever among securities up to this time.
The London Stock Exchange is one of the world’s oldest stock exchanges and traces its history back more than 300 years. It started in the 17th century in London coffee houses. Exchange grew quickly and became the city’s most important financial institution. John casting began in back 1698 to organise the market in Jonathan’s coffee house through a simple list of stock and commodity prices. The wave of speculative fever known as the south sea bubble burst in 1720. In 1761 a group of stock broker form a club at Jonathan’s to buy and sell shares and then in 1773 they put up their own building in Sweeting’s Alley with dealing room and members named it “The Stock Exchange”.
On 3 March 1801, first regulated exchange comes into existence in London and the business reopens under a formal membership basis and the modern stock exchange was born. First codified rule book was created in 1812 and first regional exchange were opened in Manchester and Liverpool in 1836 and it was rebuilt in 1854. A new deed settlement came to existence in 1876. In 1914 after Great War, the exchange market was closed from the end of July till the New Year. During 1986, there was deregulation of market which is known as ‘Big Bang’. Ownership of member firms by an outside corporation was allowed.
Brokers were able to operate in a dual capacity and minimum scales of commission were abolished. Trading was moved to computers and telephones from separate dealing rooms. The exchange became private limited company under the Companies Act 1985. The trading name became “The London Stock Exchange” in 1991. In 1997, SETS (Stock exchange Electronic Trading System) was launched. In 2003, EDX London was created, a new international equity in partnership with OM Group and later in 2004, LSE moved to new headquarters Paternoster Square. Latest in 2007, LSE merged with Borsa Italiana, creating London Stock Exchange Group.
The Bombay Stock Exchange (BSE) is located in Dalal Street, Mumbai. It was established in 1875 and is one of the oldest stock exchanges in Asia. Around 3600 companies in the country are listed on this stock exchange and have a substantial trading volume. The market capitalization of the BSE is about Rs.20 trillion (US$ 466 billion). The ‘Sensex’ is commonly used market index for the BSE and it is among the five big exchanges in the world in terms of number of transactions.
Its history traces back to the time in mid 1850s, when an informal group of 22 shareholders used to trade under banyan tree in the Town Hall of Bombay. The association the native sharebrokers was formally organized as The Bombay Stock Exchange in 1875. The BSE is the oldest stock exchange in Asia and Premchand Roychand used to be the leading sharebroker in that time.
He was the one who assisted in setting out procedures and conventions for the trading of stock at BSE. James M. Maclean inaugurated the Brokers Hall in 1899. in 1928, it was shifted to an old building in Town Hall, Bombay and later on the building was constructed on Dalal Street in 1930 where the BSE building now stands. The BSE follows the system of eTrading, which came into use in 1995. In 2000, BSE Sensex was used to open its derivatives market for trading Sensex future contracts, followed by development of equity derivatives in 2001 and 2002 which expanded its trading platform.
Stock exchanges by providing a centralized and ready market, facilitates the business for financing through flotation of bonds and stocks. Sometimes speculation in stock can put stress on the instability of an economy. The reality of the Great depression was emphasised by the stock market crash in 1929.
Stock market crash of 1929
After the First World War, there was a growth in industrialisation and new technologies. During 1920s was the time of peace and prosperity because the economy was benefited greatly from the new life changing technologies.
Many investors quickly purchased the shares on seeing Dow Jones industrial average surged. Due to the powerful economic boom the stocks were seen very safe to most of the economists. Stocks were purchased by the investors on margin. From 1921 to 1929, the Dow Jones rocketed from 60 to 400 and for every dollar invested; a margin user would borrow 9 dollars worth of stock.
But on Thursday October 24, 1929 the Dow Jones Industrial Average fell 38 points to 260, which was a drop of 12.8 percent and across the two days its average fell 23 percent and finally at the end of the period on November 11, there was a cumulative drop of 40 percent. Overvalued stocks, low margin requirements, interest rate hikes and poor banking structure were the few causes of the crash. In total, 14 billion dollars of wealth were lost during this market crash.
Stock market crash of 1987
Dow hit a record 2722.44 points on 25 August, 1987 but then the Dow started to head down. And valuation in the United States dropped around 36 percent from the days between October 14 to October 19, 1987. On black Monday October 19, 1987 the Dow Jones Industrial Average plummeted 508 points losing approx 22.6 percent of its total value and S&P 500 dropped to 20.4 percent. Reasons for the crash were no liquidity, overvalued stock, program trading and the use of derivative securities software. During the crash half trillion dollars wealth were lost.
Stock market crash of 2008
The failures of financial organizations in the USA due to exposure of credit default swaps and subprime loans resulted in a global crisis as banks all over the world failed and the values of shares and commodities fell drastically. The Indonesian Stock Market stopped operating on seeing a 10% drop in a day on October 8. Comparisons were made of this crisis with the one in 1987 but that lasted for just one day whereas the present one lingered on for the whole week. Dow Jones saw its worst ever decline of 18% during the week commenced on October 6.
The failure of banks in Iceland devalued the Icelandic Krona and forced the country to the verge of bankruptcy which was saved by an emergency loan from International Monetary Fund (IMF). The main index of Iceland had a 77% decrement. October 24 saw the worst downfalls for many countries whereas Dow Jones industrial average was somewhat better at 3.6%. The value of United States Dollar and Japanese Yen increased whereas that of British Pound and Canadian Dollar was among the major losers.
The competition among different industrial countries' markets was witnessed by their respective national stock exchange markets during the late 1980s and the economists observed that linkage or interrelation between the global markets existed. Due to the less restrictive climate towards capital movements, economists actually started thinking that the major financial markets of the world are systematically interrelated. Growth can be seen in reaction towards external developments in macro-economic policies and the world financial environment due to this interrelation.
Technological developments in communications, trading system and the innovations of financial products have created global international investment opportunities. Linkages among stock market have important implication and significance for security pricing, trading strategies, hedging and financial market regulations. And also the presence of short term and long term relationship may be used to attain financial gains from international portfolio diversification and to also reduce systematic risk. International Market linkages have been widely investigated.
Several studies have been conducted explaining the empirical and theoretical issues on linkages amongst stock market and mainly focused on the co-movement between developed and emerging markets. There is a wealth of literature on stock market interdependence and integration. However, depending on the data, methodology, and theoretical models used there is no clear resolution of the issue yet. Some previous work has have found that international stock markets are integrated and some found that stock markets are not interlinked.
Most of the studies on stock market interdependence in emerging markets have been done on geographical groups of markets, such as markets in Central and Eastern Europe and America and in Asian countries. Further, I summarize some of the most recent findings.
1.2 Interdependence of Stock Markets
A number of studies have examined stock market linkages among emerging stock market and the developed stock markets like Arshanapalli, Doukas and Lang 1995 and Chen, Firth and Rui, 2002. Arshanapalli, Doukas and Lang (1995) report that after the 1987 crash international market linkages have strengthened in terms of increased number of co-integrating vectors in the post crash period. They investigated in their paper that presence of a common random variable trend between the US and Asian stock market movements during the post October 1987 period. They showed that the cointergating structure which actually ties the stock market together has significantly increased since October 1987.
US stock market influence on the other markets was considerably found greater in the post crisis period. Their results indicate that the Asian equity market is more integrated with US equity market than Japan equity market. Where as, Masih and Masih (1997) and Masih and Masih (1999) found cointegration relationship among the equity markets of Malaysia, Thailand, US, UK, Japan, Singapore and Hong-Kong during pre-financial crises period 1987. Number of papers investigates the short term and long term linkages among Central and Eastern Europe (CEE) stock exchanges. Talking about long term relationship, Gilmore and McManus (2002) and Gilmore and McManus (2003) analysed that no long term relationship can be established among the CEE stock markets with the US and Germany stock markets, where as Voronkova (2004) shows the existence of long term linkages among the Central European markets and CEE.
Hamao and Masulis (1990), King and Wadhwani (1990), Kasa (1992) and Arshanapalli and Doukas (1993) have found that the equity markets of developed markets are integrated and US equity market leads the other developed market like Japanese equity market, UK equity market and few other European equity markets. Yang, Hsiao, Li and Wang (2005) also examined the long run price relationship and the dynamic price transmission among USA, Germany and four Eastern European emerging stock markets. They paid particular attention to Russian crisis in their study. VAR analysis was conducted.
It was concluded that both long run relationship and the dynamic transmission were strengthen among these markets after the crisis and Germany became dominant and noticeable only after the Russian crisis amongst all the Eastern European markets. Syllignakis and Kouretas also examined the short and long term relationship between ten central Eastern European stock markets and two developed stock market i.e US and Germany, they used Gowzalo and Granger method and indicated weak partial integration among these markets. They also indicated that the four big stock exchange market like Republic, Hungary, Poland and Slovenia together with Germany and the US stock market have substantial permanent factor which drives the system of stock market exchange in the long run.
Egert and Kocenda (2006) analyse the co-movement and interdependence among three stock markets in Western, Central and Eastern Europe and found no robust cointegration relationship for any of the stock index pairs. Data from 2003 to 2005 for stock indices have been taken and applied wide range of econometric techniques like unit root and stationary tests, cointergration tests, Granger causality test, VAR estimation have been used. Results show that there are signs of short term spillover effects both in terms of stock price and stock return volatility. Granger causality test show the existence of bidirectional causality for both returns and volatility series and limited number of short term relationships using VAR framework.
Limited interaction has been found among the market in case of Poland and Hungary by Li and Majerowska (2007) and also showed that emerging markets are weekly linked to the developed markets by using GARCH approach .In this paper linkages between the emerging markets of Warsaw and Budapest with the established market in Frankfurt and US were studied by using four-variable asymmetric GARCH-BEKK model. At the end it was implied that by adding the stock in the emerging markets to their investment portfolio they may benefit from reducing the risk.
Further, looking at some more European counties Lucey andVoronkova (2008) examined relationship Russia and other equity markets over the period of 1995-2004 by using number of co-integration approach like Gregory-Hansen test, a stochastic cointegration framework, the non-parametric test for unit root and cointegration and found Russian market does not show strong evidence of increased long run convergence either with regional or developed markets, so therefore correlation is low. They also stated that Russian equity market in the long run was isolated from the influence of international markets and structural break in August 1998 did not alter the long term relationship nature.
Ozdemir, Olgun and Saracoglu (2008) examined dynamic linkages between the equity market of US representing the center and emerging market using the Granger causality test as a result showed significant causal relation to all emerging markets and conclude that there is no evidence in the literature suggesting an effect of an emerging stock exchange market to that of large markets like US, Japan and UK.
Where as Chinzara, examined to what extent South Africa equity market is integrated into world equity market using cointegration, VECM and VAR model and taking data for period 1995-2007. He finds that there are significant volatility linkages exist among the market and US being the most exogenous but overall findings show integration of South Africa market into global market is still very low. FIND OUT THE DATE FOR THIS JOURNAL.
Interdependence of stock markets’ effect on portfolio diversification
Many literatures show that there is contradicting evidence for international stock market linkage between developed markets. Integration in developed markets has been proved high and is unable to satisfy investors demand for portfolio diversification regardless of various controversial results. Emerging markets could face the requirements for diversification that is why investors try to improve their risk return by investing in the international portfolio of developed market region to diversify their portfolios. The common answer to these studies ends up by mentioning that market crises cause increase market correlation and integration and thus reduction in portfolio diversification benefits.
Arshanapalli, Doukas and Lang (1993), signify increasing co-integration among major developed markets after October 1987. Chan (1997) examined stock market indices of eighteen national stock markets and found that large number of cointegrating vectors increased before the October 1987 stock market crash. He concluded that due to no co-movement in stock market in the long run, the international diversification among stock market can be effective. Particularly, that emerging equity markets have attracted the interest of international fund managers for portfolio diversification which might help them to reduce their risk from the returns from a particular region portfolio.
Patev, Kanaryan and Lyroudi (2006) investigates the co-movement of Central and Eastern European equity markets before and after major emerging market crisis, they used concept of co-integration and found no long term relationship between US and other four Central European markets in addition they confirmed a decrease in portfolio benefit in the crisis period and an increase in portfolio benefits in the post crisis period.
In 2005 Phylaktis and Ravazzolo studied the market linkages of a group of Pacific-Basin countries with US and Japan over the period 1980-1998 using Multivariate cointegration model and indicated opportunities of portfolio diversification for international investors by investing in most of the Pacific Basin countries. Their main aim was to investigate that whether the linkages among these stock markets were affected by the foreign exchange restriction and they found no stock markets they studied were linked during 80’s and 90’s. They also found close financial linkages among Thailand and Taiwan with both Japan and US during the period when foreign ownership was restricted and some more restriction were also in place.
Interdependence of Asian markets
Recently foreign investors have articulated an increasing extent of interest in the emerging markets of ASEAN and Asian NICs due to their vast potential and this result has interested a variety of international fund managers and fund management centres who concentrated on this region not only to exploit their growth but to give alternative to agents to diversify their risks from these markets. Further, on this topic of emerging and developed market relationship Masih and Masih (2001) investigated the dynamic causal linkages among nine major international stock price indexes by using Granger causality, unit roots, integrated processes, cointegration, vector error-correction modelling, and vector autoregression.
The actual aim of their study was to examine the long term and short term dynamic linkages among developed market and Asian emerging markets and they also try to compute the Asian stock market fluctuations. The results show that there is interdependence between the established OECD and Asian markets and also show the leadership of US and UK market in both the long run and short run.
In year 2004, Wong, Penm, Terrell, Lim also studied the topic of co-movement between the major developed markets to that of Asian emerging markets by using the concept of co-integration and found that there is co-movement between some of developed and emerging market and also stated that in long run some of the emerging markets differ from the developed markets. And once again like Arshanapalli and Doukas 1993 and Yang, they found out that there has been increase in the co movement and interdependence between emerging and developed market after the 1987 stock market crash and 1997 Asian financial crisis. Increase in the interdependence among developed and emerging markets due market crashes give the reason for international diversification becoming limited.
In 2007 Gooijer and Sivarajasingham using non parametric test for Granger non causality and the conventional parametric Granger non causality test examined non linear and linear causal linkages among eleven equity markets out of which six were industrialized and five were emerging markets of South-East Asia. They analysed the data from 1987 to 2006 and also took Asian financial crisis 1997 into the account. They showed that Asian stock market have become more internationally integrated after the Asian financial crisis. Its showed that Sri Lankan stock market has no considerable long term linear and non linear casual linkages with other markets.
Also, Anoruo, Ramchander and Thiewes (2003) studied the extent of linkages and return dynamics of the stock market linkages of six newly industrialized countries of Asia and role of US and Japan in this region. They used the data for the period 1988 - 1999 and showed that there are significant stock market linkages among the emerging markets of Hong Kong, India, Korea, Malaysia, Singapore and Thailand. It also show dominant correlation do exist and no market is insulated from market moments that derive from other countries in the region. This document also states that there is presence of some chronological instability in the transmission mechanism that coincides with Asian Crisis and showed that during the latter part of crisis period, Singapore’s pressure is greatly diminished while shocks from other countries, most probably India play a more leading role.
Further, long term and short term dynamic linkages among international and Asian emerging stock markets are studied by Masih and Masih (2001) by using integration, Granger Causality, unit root, integrated processes, co-integration, vector error correction modelling and vector auto-regression and they analysed that there is interdependence between established and Asian emerging markets, also stated the US and UK markets have leadership in the long run as they have constantly contributed large amount of global stock market capitalization and levels VAR also demonstrate that Japanese market’s influence as an additional long run leader.
They concluded that significant long term and short term relationship between most of the Asian market and UK and US markets. In (1999) Masihand Masih also focused on the extent of the Asian stock market fluctuations which are explained by intra-regional contagion effect. Linkages amongst four established and four emerging Asian markets are being shown and then quantified the extent of dynamic interdependence between them using some time series econometric techniques like Vector error correction model, level VAR model. As a result they show that there is leadership of US over both long term and short term at the global level and leading role of Hong-Kong over the regional level in Southeast Asia.
Naeem (2002) studied the linkages of South Asia stock market ( Pakistan, India, Bangladesh, Sri Lanka) with the stock market of US and UK by using bi-variate and multi variate co-integration analysis to model linkages and found no linkages between the South Asian stock market but found co-integration for the pre-nuclear test period i.e from Jan 1994 - Apr 1998 and also shows that stock markets of South Asia are not co-integrated with stock markets of US and UK.
Most recently, Awokuse, Chopra and Bessler (2008) using rolling co-integration methods and recently developed logarithms of inductive causation examined the interdependence among Asian emerging markets and three major stock market (UK, US and Japan). They detected time varying co-integration relationship exists among Japan, UK and US. It also indicates that Japan and US have the greatest influence on the emerging markets while the influence of Singapore and Thailand has increased since the Asian financial crisis.
Ibrahim (2005) using co-integration and vector auto-regression studied the international linkages of Indonesian market during the pre and post crisis period and detected lack of co-integration among the Indonesian market, other ASEAN markets (Malaysia, Philippines, Singapore and Thailand) and two advance markets (US and Japan) during both pre crisis and post crisis period. He also documented evidence for significant interaction among the ASEAN markets and showed that ASEAN markets respond quickly to shocks in US. Indonesian market became more reactive to the developed markets of US and Japan during post crisis period.
Using some new methods like Engle Granger two-step, Gregory and Hansen Test Valadkhani and Chancharat (2007) investigated existence of co-integration and causality between stock market price indices of Thailand and its major trading partners like Australia, Hong Kong, Indonesia, Japan, Korea, Malaysia, Philippines, Singapore, Taiwan UK and USA) , they found that potential long-run benefits exist from diversifying the investment portfolios internationally to reduce the associated systematic risk across countries and also stated that stock returns of Thailand, Malaysia, Singapore and Taiwan are interrelated. But the test provided no indication between stock prices of Thailand with other countries.
Different stock markets like property stock markets of Asian and European countries are also studied by Liow, Ooi and Gong (2004). They studied the relationship of among four Asian property stock markets of Japan, Hong-Kong, Singapore and Malaysia and four European property stock markets of UK, France, Germany and Italy using E-GARCH model for short term analysis and Johansen Multivariate co-integration approach for long term analysis and found that there is minimal co-integration, lack of significant evidence of cross volatility spillover among these markets which implies that investors can benefit from diversifying the portfolio at international level in Asia and Europe in both short and long run.
Taking the case of Taiwan and its four major trading partners, Lina and Cheng (2008) studied the economic determinates that effect the co-movement in the stock market of China, Hong-Kong, US and Japan with the stock markets of Taiwan by using the data for ten years from 1994 to 2004. They investigated the co movements in Taiwan and its major trading partners for pre crisis period, during crisis period and post crisis period and empirical results show that the period after the financial crisis have more probabilities of the positive co-movement for Taiwan. In addition, it was said that interest rate differentials play an ever more important role in the period after the crisis.
Whereas no evidence was found to indicate long run relationship among the South East Asia stock market over the period 1988-1998 by Thiam Hee Ng (2002). He used correlation analysis to examine the linkages among stock market of Indonesia, Malaysia, Philippines, Singapore and Thailand and indicate that South East Asian stock market are becoming more integrated after under going the substantial liberalization which opened financial markets to foreign investors.
Lim L.K. (2006) investigates the dynamic interdependence of the five members of ASEAN, namely Indonesia, Malaysia, Philippines, Singapore and Thailand. Similarly as stated in other studies that markets became more integrated after the Asian financial crisis, in this journal also it has been found that these five ASEAN countries have expected to become more interdependent after the 1997 Asian financial crisis.
The main focus is to examine the long run relationship in these countries and whether there are sign of increased cross market integration after the financial crisis. This study covers data from 1990 to 2007 by using vector auto regression approach to examine the direction of Granger causality between these markets. The study indicated an increase in the integration between ASEAN-5 markets after the crisis and states that there has been an increase in cross market interdependence over the post crisis period, particularly the Indonesia market returns.
Mukherjee and Mishra (2008) using GARCH model explored the possibility of stock market integration and volatility spillover among India and its major Asian counterparties during the period from 1997 to 2008. Hong Kong, Korea, Singapore and Thailand are few Asian countries which have significant flow of information to India and countries like Pakistan and Sri lanka are also found strongly influenced by the movements in Indian stock market. Further, studying the after financial crisis effect of stock market integration in ASEAN countries, Click and Plummer (2003) came with the results suggesting that ASEAN-5 (Indonesia, Malaysia, Philippines, Singapore and Thailand) markets are co-integrated and are not completely segmented by national borders.
They examined the degree to which the five stock markets in the original ASEAN countries are correlated which will be a approach to assess the possibility of ASEAN stock market integration and implications for portfolio diversifications and concluded that they are integrated in economic sense but that integration is not complete.
Johanssonand Ljungwall (2008) studied linkages among different stock markets in Greater China Region (China, Hong Kong and Taiwan) and showed no indication of long run relationship among the markets but overall there is interdependence among these three stock markets. Unit root and cointegration test was used. It has been shown that the China and Hong-Kong are affected by the spillover effect from Taiwan. Further, Huang and Bacon (2009) studied the relationship between emerging stock market of China and US for the period 2000-2007.
Cumulative coefficients like running beta and correlation coefficient are used to determine the co-movement and showed that relationship between US and China stock market has significantly increased since 2005 where as influence of China’s emerging stock market on the global offers significant value to portfolio managers worldwide. Further, the relationship between the US and China stock market has extensively increased since 2005, which may be because of policy change in 2005 by China to move towards a more free market economy.
Yang, Kolari and Sutanto (2003) studied the stability of long run relationship between US stock market and emerging stock markets, they used cointegration analysis and showed no long run relationship over most of the sample period throughout 1997 but they found clear data of cointegration in response to recent global emerging market crisis 1997-1998 which shows that significant crisis event can change the degree of cointegration between markets. Chen, Lobo and Wong (2005) examined bilateral relation between three pairs of stock market ( India-US, India-China and China-US) by using fractionally Integrated Vector Error Correction Model to examine the cointegration mechanism between markets and results show that all three pairs of stock markets are fractionally co-integrated. It also shows US stock market plays a dominant role in the relation with other two markets, where as interactive relation between Chinese and Indian stock market is being shown. In case of Chinese market, Indian stock market dominates the first moment feedback than US stock market.
Raj and Dhal used Unit Root Test, Dickey-Fuller (ADF) unit root test of stock price indices in US dollar, Lag length of VECM, Co-integration rank test and Error Correlation Equation and discussed the crisis since Jan 2008 Empirical evidence supports the international integration of India’s stock market in terms of stock prices measured in US dollars but not in local currency, a finding attributable to investment decisions of foreign investors. The differential nature of stock market cointegration arising from US dollar- and local currency-denominated stock prices also has implications for the efficiency of national stock markets. At the same time, it was found that India’s stock market provides opportunities for higher returns than other regional and global markets.
Patel (2008) investigates the calendar effect in the Indian stock market. In his earlier studies Patel (2003) he examined the relationship among US stock market and ten emerging markets of Asia, he found that the Indian stock market and few other South Asian markets were different from other emerging stock markets of Asia. In his later study Patel(2006) he focused entirely on the Indian and the US stock markets. He examined three Indian stock indexes, namely BSE30, BSE 100, and BSE 200 stock indexes and in US he utilized DJIA, S&P 500 and NASDAQ.
He found that Indian market has benefited US investors by offering important return and diversification, especially in that period when US market generated low returns. In Patel (2008) he examined calendar effect by taking Indian stock market exchange indexes for the period 1999 to 2007 and showed that Nov-Dec effect is greater than the returns during remaining ten months. Wong, Agarwal and Du (2005) examined the short term dynamic linkage and long run equilibrium relation between the Indian stock market and the major developed stock markets (United States, United Kingdom and Japan) after 1990.
Empirically he examined the Granger causality relationship and the pairwise, multiple and fractional cointegration between three developed stock markets and Indian stock markets using unit root test, cointegration, Error Correction Model, Vector Autoregression Model, Johansen Multivariate Cointegration, Fractional Cointegration. He determined that the Indian stock market is integrated with mature markets and is responsive to the dynamics in these markets in long run where as, in short run both US and Japan Granger causes the Indian stock market.
Baekin Cha and Sekyung Oh (1999) paper examines the relationship between the two largest equity market in the world (US and Japan) and the four Asian emerging stock exchange markets (Hong Kong, Singapore and Taiwan) using a trivariate vector autoregression model with proper control for heteroscedasticity. It showed that links between developed markets like US, Japan and the Asian emerging markets began to increase after the stock market crash in October 1987.
Andrew C. Worthington a, Helen Higgs (2002) short term and long term linkages among Asia pacific economies are analysed using Multivariate co-integration procedures, level VAR, Granger causality and generalised decomposition analysis based on error-correction and vector autoregressive model. They have taken data for the period 1995 to 2000 for the analysis by taking into account seven developed (Australia, Canada, Hong Kong, Japan, New Zealand, Singapore and the United) and eleven emerging markets (China, Chile, Indonesia, Korea, Malaysia, Mexico, Peru, the Philippines, Russia, Taiwan and Thailand) which shows that there is a significant short run casual linkages and a fixed long run relationship among the APEC equity markets and also state that Australian, South American and Northern Asian markets are comparatively more influenced by domestic market conditions where as North American and Southern Asian markets are more influenced by regional factors and geographically close domestic markets respectively. Variations in the degree of co-movement have been shown in the results.
Climentand Meneu (2002) This paper studies the effect of the Asian 1997 crisis on the relationship of the South East Asian stock markets with the stock market of Europe, North America and Latin America. Co-integration test, vector auto-regression analysis has been done and showed significant dynamic relationships between international stock markets. Results show that there is no multivariate cointegration relationship across the markets and showed that after crisis US leadership role became stronger where as Asian markets respond quickly to external markets especially after the crisis. Kim (2004) In this paper linkages among the developed Asia Pacific stock market like Australia, Hong-Kong, Japan, Singapore with the US and information leadership of Japan and US in early 1990s is investigated. The results show that volatility and return were significant from the linkages and became stronger after the Asian Crisis period. But US and Japan did not produce time varying influence from effects of returns, volatility and trading volume. Overall results show that linkages became more acute after the Asian Crisis 1997.
Elyasiani, Pereraand Puri (1998) paper examines the dynamic linkages and interdependence between the capital market of Sri Lanka and its major trading partners like Japan, South Korea, Hong-Kong, India and the US. No interdependence is being discovered between the Sri Lanka stock exchange market with stock exchange markets of US and Asia. Vector auto regression (VAR) technique has been used to find the linkages.
Leong and Felmingham (2002) in this journal interdependence of five East Asian stock prices indices are examined from 1990 to 2000. Using correlation analysis the co-movement among Singapore, Japan, Taiwan has been done which once again revealed that correlation has become stronger since Asian Crisis. It also showed that degree of integration has increased among these markets and opportunities for diversification has lessened during the 1990s.
Nedal A. Al-Fayoumi, Khamees and Ali A. Al-Thuneibat (2009) examines dynamic relations among daily stock returns of Amman stock exchange (ASE) indices over the period from 2000 – 2007. They used unit root test and vector error correction model and found that indices are related through one cointegrating vector in the long-run. Results also indicate that there is strong short run causality operating from general, financial and industry to some other indices. The results show that the financial sector is the most influential sector in ASE where as service sector is least incorporated with other sectors which may give the good diversification opportunities inside the ASE.
Janakiramanan and Lamba (1998) examined the linkages between Pacific-Basin region stock markets during 1988-98. He analysed the influence of US market on the other stock market. To show linkages he used a Vector auto regression model. Results mainly showed that during the period the US market influences all Australasian markets, except Indonesia and no one out of these markets put a considerable influence on the US market.
Alkulaib, Najand and Mashayekh (2008) their study investigates the lead/ lag relation between Middle East and North Africa countries. Results show the linkages between stock market among Levant region in this region. More interaction and linkages have been shown in Gulf cooperation council region than in North Africa regions. It is also being shown that all the markets in the region are lead by UAE’s stock exchange.
Diamandis (2008) have used weekly observations for the period 1988-2006 to show the linkages and long run relationship between four Latin America stock markets (Argentina, Brazil, Chile and Mexico) with US stock market. He estimated the autoregressive and moving average representation of VAR model and also used cointegration vectors for statistical analysis. Findings suggest that there is co-integration among these markets and there is one long run relationship exists among these five equity. He concluded that although cointegration exists still there are long run benefits from international portfolio diversification.
Phylaktis and Ravazzolo (2002) investigates the real financial links at regional level as well as at global level for the group of Pacific Basin countries over the period 1980-1998 by analysing the covariance of excess returns on national stock exchange. It showed the implications of establishing foreign exchange restrictions to control the amount of financial integration between markets.
Study by Patricia L. Chelley-Steeley (2005) shows the extent to which the equity markets of Eastern Europe (Hungary, Poland the Czech Republic and Russia) have become less segmented. He has used various econometric test to show if there has been constant increase in the co-movement of Eastern European markets with developed markets. Global factors have an increasing influence on equity returns for Hungary and Poland which is being analysed by using variance decompositions from a vector autoregression represention of retrns. To conclude, results find that Hungary is the country which is becoming integrated the most quickly.
Research paper on linkages of Russian market after the 1998 crisis by Lucey andVoronkova (2008) examine the relationship of Russian and other equity markets during 1995-2004. Non parametric tests for unit roots and co-integration have been done. Tests showed that Russian market remained isolated in the long run from the influence of international markets.
Brocato (2006) measures the adjustments that have occurred during 1980 to 1987 in stock market correlation of sic major international stock exchange markets which are taken in account for almost ninety percent of the world wide stock market capitalization. The VAR variance decomposition method is used to find out inter market linkages. As a result he found that there were significant changes in inter market linkages patterns during 1980s and as found in several studies he also analysed that US market is the dominant financial market among the other markets (specifically in the Pacific Basin region) are now the reason for an increasing impact on observed index correlation. It also states that world financial integration is also responsible for some linkages patterns during 1980s.
Cheng and Glascock (2006) studies the stock market linkages between the United states and three greater China Economic area equity markets (China, Hong Kong and Taiwan) before and after the 1997 Asian financial crisis. They have taken daily stock market indices from 1995 to 2000 to analyse their study. Use of Granger causality is being done which provide results indicating that relationship between the markets increased in the post crisis period. Principal component analysis suggests that more pleasant market co-movement after the financial crisis, which was due to some common factors affecting returns. Further, results suggests that stock market got more reactive to foreign shocks and also became more interrelated after the 1997 Asian crisis.
Banfiglioli and Favero (2005) investigated co-movement between stock market by taking into account the distinction between interdependence and contagion. They try to find long term and short term interdependence between US and Germany stock market. Method used by them is vector error correction as a baseline and found that that hypothesis of the long run interdependence between these markets cannot be rejected. They found no long term interdependence between US and Germany stock market.
Connolly and Wang ( ) in this paper they measured the cross market equity returns and volatility linkages for US, UK and Japan. They also investigated that how much these linkages can be explained by the news announcement in these countries which included money supply, Industrial production, price inflation, unemployment rate and trade deficit during 1985 till 1996. Results indicate that news announcements account very little of the direct inter market return spillover and can also affect the size of inter market return spillover.
While most of the studies have found no cointegration or low integration among emerging and US stock market but there are few recent studies which find long run relationship between these markets.
The data used for the analysis is secondary data. Our dataset used in the study comprise daily closing price of the six stock market for the period 3 January, 1989 to 8 June, 2009. Stock prices are represented by Indices: UK(London Stock Exchange), USA(NYSE), Japan(Tokyo Stock Exchange), India(Bombay Stock Exchange), Malaysia(Bursa Malaysia), Thailand(The Stock exchange of Thailand) . These six different time series are from the DataStream. This study employs a empirical methodology to investigate the stock market linkages. The empirical work in this paper is based on standard time series of Unit root, Cointegration and Error Correction framework.
Today it is commonly accepted that most financial time series are integrated and thus have a unit root. A test of stationary (or non stationary) that has become popular over the past few years is the unit root test.
Definition: A unit root test is a statistical test for the proposition that in a autoregressive statistical model of a time series, the autoregressive parameter is one y(t), where t a whole number, modelled by
(t+1) = a y (t) + other items
Where a is an unknown constant, a unit root test would be a test of the hypothesis that a = 1, usually against the alternative that │a│ is less than 1.
In the start point of unit root process we start with
-1 ≤ ρ ≤ 1 (i)
Where is a white noise error term.
If then, in case of the unit root becomes a random walk model without drift, which is a non stationary stochastic process. Therefore, to find out if the estimated is stationary equal to 1, it is simply regress on its (one period) lagged value . And if it is equal to 1 then is non stationary. This is the common idea behind unit root test.
After subtracting from both sides of above equation we find
Where and, as usual is the first difference operator.
In practice, therefore equation (ii) is estimated and test the (null) hypothesis that= 0.
If = 0, then, that is we have a unit root, meas that the time series under consideration is non stationary.
So, if = 0 equation (ii) will become
Since is a white noise error term, it is stationary, which means that the first difference of a random walk time series is stationary.
Next we will take equation (ii) and estimate. To estimate it, we should first take difference of and regress then on and see if the estimated slope coefficient in this regression is zero or not. And if it is zero, we will conclude that is non stationary. But if it is negative, we will say that is stationary. And to find out if the estimated coefficient of is zero or not, we cannot use test to find it because under the null hypothesis that = 0 (i.e. , and the value of the estimated coefficient of doesn’t have an asymptotic normal distribution.
Dickey and Fuller have shown that under the null hypothesis that = 0, the estimated value of the coefficient of in follows the (tau) statistic. In the literature the tau test is known as the Dickey-Fuller (DF) test. And if the hypothesis that = 0 is rejected that is the time series is stationary, one can use the usual test. The DF test is estimated in three different forms that are:
is random walk: (ii)
is a random walk with drift: (iii)
is a random walk with drift around
around a stochastic trend: (iv)
where is the time. In case, the null hypothesis is that = 0; that means there is unit root and time series is non-stationary. If is less than zero, the time series is non-stationary. And if the null hypothesis is rejected, it means that is a stationary time series with zero mean in the case of random walk, is stationary with a zero mean in case of random walk with drift and is stationary around a deterministic trend in random walk with drift around a stochastic trend.
The Augmented Dickey-Fuller (ADF) test
While conducting the DF test it was assumed that the error term is uncorrelated but in the case are correlated, Dickey and Fuller has developed a test known as the augmented Dickey-Fuller (ADF) test. This test is conducted by “augmenting” the preceding three equations by adding the lagged values of the dependent variables . The ADF test consists of estimating the following regression:
Where is a pure white noise error term and where , etc.
The number of lagged difference term to include is determined empirically, this is done to include enough terms so that the error term in above equation is serially uncorrelated. In ADF it is still test whether = 0 and the ADF test follows the same asymptotic distribution as the DF statistic, so the same critical values can be used.
Cointegration analysis is concerned with estimating long run economic relationships among non-stationary and integrated variables. Suppose that and are variables. Generally, any linear combination of two I(1) variables will also be I(1). But there may be some particular linear combination of two I(1) variables which is stationary I(0). In that case, the two variables are said to cointegrate. Cointegration may also take place between more than two I(1) variables, in the sense that some linear combination of those variables is stationary.
An (n´1) vector of I(1) time series variables is cointegrated if some linear combination of the series y is a stationary I(0) variable for some non-zero (n´1) vector b.
It has been that the regression of a non stationary time series on another non-stationary time series may produce a spurious regression. However, there is an exception to this rule. If and are non stationary I(1) variables, then we expect their difference or any linear combination of them, such as: , to be I(1) as well.
There is an important case when is a stationary I(0) process. So, in this case and are said to be cointegrated. Cointegration implies that and share similar stochastic trends and that’s the reason why the difference is stationary, they never diverge too far from each other.
A simple way to test whether and are cointegrated is to test whether the error is stationary. Since, we cannot observe , we test the stationarity of the least square residuals, using a Dickey-Fuller test. Cointegration test is effectively a test of the stationarity of residual that means if residuals are stationary, then and are said to be cointegrated and if residuals are non-stationary, then and are not cointegrated which means regression relationship between them is spurious.
It is stated that regression has no constant term because the mean of the regression residual is zero. For example we take consumption and income as two I(1) variable, savings defined as (income – consumption) could be I(0). As a result, a regression of consumption on income would be meaningful (i.e. not spurious). In this way we can say that two variables are cointegrated.
There are tree different methods to test cointegration namely Augmented Engle-Granger (AEG) Test, Cointegration Regression Durbin-Watson (CRDW) Test and Johansen method. The Johansen (1988) and Stock and Waston (1988) method are similar. We have used Johansen method to find the cointegration and to carry out the Johansen test, we first formulate the VAR
A Trace Test (Johansen 1991) is conducted to determine. The null hypothesis for the trace test is that there are most cointegrating vectors.
TRACE TEST =
Regression when there is no cointegration
Regression with I(1) variables is acceptable providing those variables are cointegrated which allow us to avoid the problem of spurious results. Regression with stationary I(0) variable, is also acceptable. When there is no cointegration between I(1) variable, then non stationary series is converted to stationary series and techniques are used to estimate the dynamic relationships between the stationary variables. This step is taken only when we fail to find the cointegration between I(1) variables. Regression with the cointegration I(1) variable makes the least squares estimator “super consistent”. The model we use to convert non stationary series to stationary series depend on whether variable are difference stationary or trend stationary. So first we convert the non stationary by taking first difference and later on convert the non stationary series to its stationary counterpart by de-trending.
Error correction model
Definition: An error-correction model is dynamic model in which “the movement of the variable in any period is related to the previous period’s gap from long-run equilibrium”.
While studying time-series properties of data and cointegration relationship between pairs of non-stationary series, we assumed that one o the variable among two was the dependent variable (and the other one was independent (variable. We treated that relationship as a regression model. If y and x are I(1) and cointegrated, then we need to modify the equations to allow the cointegrating between the I(1) variables. There are two reasons behind it. First, is to retain and use valuable information about the cointegration relationship and second to ensure that best technique is used to take in account the properties of the time-series data. Introducing the cointegration relationship leads to a model known as VEC model.
Consider two non-stationary variables and that are integrated of order 1;
~ I(1) and ~ I(1) and which we have shown to be cointegrated so that
The VEC model is a special form of the VAR for I(1) variable that are cointegrated. The VEC model is
All the variables in the above equation ( and ) are stationary. Hence, the standard regression analysis may be used to test the significance of the parameters. We have to look at how we combine stationary and non stationary variables in a regression model. Cointegration is actually about the relationship between I(1) variables. The cointegrating equation does not contain I(0) variables. The corresponding VEC model, however, relates the change in an I(1) variable to other I(0) variable namely the cointegration residuals and if required, other stationary variables may be added.
Results of Unit Root Tests
The first step involved in finding whether the stock markets of the developed countries such as the UK, the USA and Japan are cointegrated with those of the developing countries ( India, Malaysia and Thailand) is testing for the existence of unit roots in the concerned time series.
The result of the Augmented Dickey-Fuller (ADF) unit root tests in the following table suggests that the representative stock exchanges in their natural logarithm level are non-stationary series. The settled trend includes both the intercept and the time trend.
(you can insert standard explanation on a unit root process from your econometrics text book.)
Then insert a table showing the test results (sample table is given below, you can change the order in which different countries appear in the table):
Null hypothesis in the present case is that the logarithmic time series of stock exchange indices are unit root processes.
The Dickey Fuller Test for existence of Unit Roots carried out for the six stock exchange indices show that all the time series are unit root processes. This is evident from the test results presented in the table above. None of the ADF t-statistics has a probability value less than 0.05, indicating that the null hypothesis is not rejected. That the time series involved in the present research are non-stationary is also evident from the low R-square values obtained when the regression is run on the lag values of the relevant time series on one hand and significant F-statistic on the other hand.
Considering that these time series are non-stationary, it is likely that they are highly cointegrated. If this is the case then any kind of fluctuation in one stock exchange index can lead to a determinate variation in other stock exchange indices. To test this, the cointegration test is run for various lag lengths. To avoid the problem of nonstationarity, first differences of the logarithmic values of stock indices are used. These necessarily give the percentage rate of change in the stock indices. This test is specifically run for the Indian stock exchange index and the other stock exchange indices.
Analysis of Cointegration Test
Empirical results of the cointegration rank test is derived from the Johansen’s Multivariate VECM which involves the six stock exchanges.
These cointegration tests involve unrestricted rank test and the results based on trace and maximum eigenvalues both suggest that there is no evidence of cointegration between the Indian stock exchange index and the rest of the stock exchange indices. Also, as the lag length increases, the normalized cointegrating coefficients become weaker. None of these coefficients show a consistently positive or negative value. This indicates that the impact of variations in the stock exchange indices on Indian stock exchange index is not clear.
Error Correction Model
Further, to confirm this conclusion, the error correction model is also estimated. (once again, you can give the theory of ECM from your textbook, here)
The six error correction models show the interrelation among all the stock exchange indices.
The error correction model for India shows that the R-square value is very low, indicating poor cointegration between Indian stock index and the rest used in the study. When it comes to individual impact, Indian stock market appears to be least dependent on the Malaysian stock market, whereas it is relatively more dependent on the stock markets of the developed countries.
Thailand stock market shows better explanatory power as its R-square is higher. But its dependence on the other stock exchanges is not very clear. It seems to be more dependent on the Japanese and Indian stock exchanges, and less dependent on the Malaysian, UK and USA stock exchanges.
Malaysian stock exchange seems to be better integrated with all stock exchanges. This is evident from higher R-square values and significant individual coefficients. However, as the lag length increases, the dependence decreases, and the coefficients for higher lag lengths are progressively insignificant.
Japanese stock exchange also shows spurious cointegration results. On one hand, it has relatively high R-square value, while on the other hand hardly any individual explanatory variable is significant. Whatever little integration is evident is between the Japanese stock exchange and the USA stock exchange. None of the other stock exchanges seem to influence the Japanese stock market.
The highest degree of cointegration is evident in case of the USA and the UK stock markets. The error correction models for both these stock exchange indices show high R-square values. Moreover, almost all the individual explanatory variables are significant. And, although the level of significance deteriorates with an increase in the lag length, the variables do not turn highly insignificant in any case.
This indicates that the stock exchanges of the developed economies are better cointegrated as compared to those of the developing economies. One of the reasons behind this may be that the investments in the developing countries are still driven by a very high potential for growth in the presence of large quantities of unemployed resources. Second, the trading is more dependent on long term investment policies of both the government and private firms. Third, the stock exchanges in the developing countries like India are highly regulated by regulatory authorities like SEBI, while those of the developed economies are more open. Fourth, capital inflow, especially in terms of foreign institutional investors is greater in developing countries, which ensure higher real rate of return as compared to the rate of return in the developed countries.