Relationships between vietnams stock prices and united statess stock prices, exchange rates, gold prices, crude oil prices

The thesis investigates the relationships between Vietnam’s stock prices andthe US’ stock prices, foreign exchange rates, gold prices, crude oil prices.. In addition, Vietnam’s stock pri

Đ NG C ANH

RELATIONSHIPS BETWEEN VIETNAM’S STOCK PRICES AND UNITED STATES’ STOCK PRICES, EXCHANGE RATES, GOLD PRICES, CRUDE OIL PRICES

MAJOR: BANKING AND FINANCE MAJOR CODE: 60.31.12

MASTER THESIS INSTRUCTOR: Dr VÕ XUÂN VINH

Ho Chi Minh City – 2011

First of all, I would like to express my deep gratitude to Dr Vo Xuan Vinh forhis kind supervising my thesis work, providing sources, especially software,documents related to this thesis, and giving me invaluable instructions and criticism.Throughout my thesis preparation, Dr Vinh provided great encouragement andteaching

I would like to thank all the authors of many reference sources for providingmany precious theories, ideas as important input of my research

I am also grateful to the professors and visiting lecturers of University ofEconomics Hochiminh City for the enthusiasm and expertise to transfer me theinvaluable knowledge and insights during the MBA courses

I want to extend my appreciation to my classmates, my friends who makegreat contribution in collecting data and give me encouragement Without their helpand sharing, I could not successfully complete this MBA program and the thesis

Finally, special thanks are for my colleagues and my family, who haveunderstood, encouraged and created as much better conditions as possible for me tocontinue studying until the end of the course and completing this thesis

The thesis investigates the relationships between Vietnam’s stock prices andthe US’ stock prices, foreign exchange rates, gold prices, crude oil prices Using thedaily data from 2005 to 2010, the results indicate that Vietnam’s stock prices areinfluenced by crude oil prices In addition, Vietnam’s stock prices are also affectedsignificantly by US’ stock prices, foreign exchange rates over the period before the

2008 Global Financial Crisis There are evidences that Vietnam’s stock prices arehighly correlated with US’ stock prices, foreign exchange rates and gold prices for thesame period Furthermore, Vietnam’s stock prices are cointegrated with US’s stockprices before and after the crisis, foreign exchange rates, gold prices and crude oilprices just during and after the crisis

Keywords: Vietnam’s stock prices, US’ stock prices, US Doll ar - VN Dong

exchange rates, gold prices, crude oil prices, correlation, cointegration, Grangercausality

TABLE OF CONTENTS

ACKNOWLEDGEMENTS i

ABTRACT ii

TABLE OF CONTENTS iii

LIST OF TABLES iv

ABBREVIATIONS v

CHAPTER 1: INTRODUCTION 1

1.1 Background and problem statement 1

1.2 Research objectives 3

1.3 Research methodology 3

1.4 Research structure 3

CHAPTER 2: LITERATURE REVIEW 5

CHAPTER 3: METHODOLOGY 11

3.1 Methodology 11

3.1.1 Correlation 11

3.1.2 Cointegration 11

3.1.3 Unit root 13

3.1.4 Granger causality 16

3.2 Data 17

3.2.1 Data descriptive statistics 17

3.2.2 Time differences 18

CHAPTER 4: EMPIRICAL RESULTS 19

4.1 Descriptive statistics 19

4.2 Correlation test 24

4.3 Unit root test 28

4.4 Cointegration test 30

4.4.1 Bivariate cointegration test 30

4.4.2 Multivariate cointegration test 32

4.5 Granger causality test 34

CHAPTER 5: CONCLUSION 36

REFERENCES 38

LIST OF TABLES

Table 1a: Summary statistics of daily prices 20

Table 1b: Summary statistics of daily returns 23

Table 2a: Correlations among the daily prices 25

Table 2b: Correlations among the daily returns 27

Table 3a: Unit root tests for the daily prices 28

Table 3b: Unit root tests for the daily returns at level ADF test statistic 29

Table 4: Bivariate cointegration test result 31

Table 5: Multivariate cointegration test result 33

Table 6: Granger causality test result 35

CHAPTER 1: INTRODUCTION

1.1 Background and problem statement

Stock prices are one of economic indicators which move in the same direction

as the economy and are used to forecast the health as well as the growth of theeconomy Thus, an examination of the main economic factors’ impact on stockindices has an important implication for both the government and investors Thisthesis investigates the long and short-run relationship between Vietnam’s stock prices(VN-Index) and United States’ stock prices (S&P 500 Index), United States Dollar -Vietnam Dong exchange rates, gold prices, crude oil prices covering a five-year timeframe before and after the 2008 Global Economic Crisis because of the followingreasons:

Standard and Poor's 500 Index (S&P 500 Index) is a capitalization-weightedindex of 500 stocks The index is designed to measure performance of the broadUnited States’ economy through changes in the aggregate market value of 500 stocksrepresenting all major industries The index was developed with a base level of 10 forthe 1941- 43 base periods Thus many mutual funds, exchange-traded funds, andother funds such as pension funds, are designed to track the performance of the S&P500-Index and hundreds of billions of US dollars have been invested in this.Furthermore, S&P 500 Index is also considered as a leading indicator for stock priceindices in many countries around the world In Vietnam, there are a lot of investorsand trade magazines using S&P 500 Index to presage the fluctuation of VN-Index.However, to the best of the author’s knowledge, there is none of the publishedresearch investigates the relationship between them, just very simple analyses bydrawing some graphs

According many researchers, exchange rates are considered one of factorsinfluencing stock prices significantly and vise versa Exploring the relationships

between two variables is very meaningful to governments, multinational corporationsand investors Firstly, they may be the basis for the governments’ decisions about themonetary and fiscal policy When the policy-makers decide to expand or contracttheir countries’ monetary and fiscal policies targeted at neutralizing the interest rateand real exchange rate or to depress their domestic currency value in order to boostthe export, they should be aware how such policies effect to the stock market.Secondly, the knowledge about the relationship between two variables may help themultinational corporations to predict the fluctuations in the exchange rate relying onthe changes in stock prices and hence to manage their exposure to foreign contracts,exchange rate risk and stabilize their earnings Thirdly, currency has beenincreasingly included as an asset in the investors’ portfolios Thus, the more accuratethe investors estimate of the variability of a given portfolio, the more benefit they willachieve Last, the analysts believe that understanding the linkage between currencyand stock markets may help to foresee a crisis like the Asian Financial Crisis in 1997.The collapse of the stock markets in this crisis is thought to be caused by the sharpdepreciation of Thai baht triggering the dropping of other currencies in the region.

In addition, gold has been increasingly added to investors’ strategic assetallocations, both directly and indirectly because of its diversification benefits andpotential role as a hedge against the inflation, deflation, political unrest and currencyrisk (Faff and Chan, 1998, Jaffe, 1989, Mahdavi and Zhou, 1997, Worthingtona andPahlavanib, 2007) as well as stocks, bonds on average and as a safe haven for bothstocks and bonds in a case of market crash (Baur and Lucey, 2010)

Furthermore, the crude oil price is determined the cost of a country andusually used to analyse the economic growth There are many researches havingfocused on the relationship between crude oil price and economic indicators andcommodities (Gisser and Goodwin, 1986, Hamilton, 1983), the impact of oil pricechanges on these economic indicators (Hakan et al., 2010, Mussa, 2000, Schneider,

2004) The results show that there are the long-run and two-way feedback relationsbetween the crude oil prices and stock prices (Wang et al., 2010).

1.2 Research objectives

The purpose of the study is to suggest the answers to the following criticalissues Firstly, are there relations between the pairs of VN-Index and S&P 500 Index,VN-Index and gold prices, VN-Index and US Dollar - VN Dong exchange rates, VN-Index and crude oil prices? Secondly, are there lead-lag relationships between VN-Index and the other variables in pairwise analysis?

1.3 Research methodology

This research examines the co-movement between VN-Index and S&P 500Index, VN-Index and gold prices, VN-Index and US Dollar - VN Dong exchangerates, VN-Index and crude oil prices by employing a number of econometric andfinancial modelling techniques To explore the short-run relationships among thevariables, the techniques of correlations are utilized The technique of Grangercausality is applied to test whether movements in one variable appear to lead those ofanother The technique of cointegration is employed to investigate the long-runrelationships

1.4 Research structure

The remainder of this study is structured as follows:

Chapter 2 reviews the literature.

Chapter 3 describes the methodology employed in the study and represents

the data descriptive statistics

Chapter 4 reports the empirical results.

Chapter 5 concludes the study.

CHAPTER 2: LITERATURE REVIEW

There are many researches which investigate the factors in the economyplaying an important role in effecting and determining the stock prices These studiesemploy econometric models including correlation, cointegration, Granger causalitytechniques to examine the relationships among the international stock markets,relationships between stock prices and bond prices, stock prices and other variables inthe economy However, these studies show the mixed and conflicting results due toconducting with different groups of stocks in different areas in the world Besides,most of studies just concentrate to analyse the relation between the stock prices withone another variable in the economy There are few researches investigating therelationships among the combination of stock prices, oil prices, gold prices andexchange rates

Firstly, in terms of the integration of international equity markets, manystudies found stability of the correlation structure over time (Panton et al., 1976,Watson, 1980) but the preponderance of the literature indicates that there is instability

in the relationship (Makridakis and Wheelwright, 1974, Maldonado and Sounders,

1981, Meric and Meric, 1989, Fischer and Palasvirta, 1990, Madura and Soenen,

1992, Wahab and Lashgari, 1993, Longin and Solnik, 1995, Kearney and Lucey,2004) and that this is determined primarily by real economic linkages betweencountries (Bodurtha et al., 1989, Campbell and Hamao, 1992, Roll, 1992,Arshanapalli and Doukas, 1993, Bachman et al., 1996, Bracker and Koch, 1999).Employing the Engle–Granger cointegration methodolo gy, Kasa (1992) examinesthe major equity markets over the 1974–1990 period and finds a single cointegratingvector indicating a very low level of integration Other studies also find the similarresults of low integration (Chan et al., 1992, Arshanapalli and Doukas, 1993,Gallagher, 1995, Allen and MacDonald, 1995, Chan et al., 1997) Kanas (1998a)employs multivariate trace statistics, the Johansen method, and the Bierensnonparametric approach to test for pairwise cointegration between the US

and each of the six largest European equity markets, namely those of the UK,Germany, France, Switzerland, Italy and the Netherlands for the data covering theperiod from 1983 to 1996 The results show that the US market is not pairwisecointegrated with any of the European markets and this suggests low levels ofintegration so that there are potential long-run diversification benefits for USinvestors seeking to invest in European markets Vo and Daly (2005b) analyse andtest 10-year period daily return data from 1994 to 2003 of Asian equity marketindices and selected advanced nation’s equity market indices They employcorrelation, cointegration and the Granger causality test The results from their testssuggest a weak causal relationship between Asian equity markets and developedcountries’ equity markets This implies that there are potential diversification benefitsfor foreign investors from Australia and the US investing in Asian equity markets Inaddition, employing the same methodology, Vo and Daly (2005a) also suggest thatthere are very low linkages among European equity markets and suggest that thereare potential diversification advantages On the other hand, other authors state thatthe long-run covariances between markets are higher than in the short-run, and hencethe benefits of international diversification are lower (Grubel and Fadner, 1971,Panton et al., 1976, Taylor and Tonks, 1989) Employing the more sophisticatedJohansen multivariate approach, other studies yield the contrary results of strongintegration (Chou et al., 1994, Hung and Cheung, 1995, Kearney, 1998, Gilmore andMcManus, 2002, Manning, 2002, Ratanapakorn and Sharma, 2002).

Secondly, in terms of the relationship between stock prices and exchangerates, most of studies show that falling in domestic currency value has a negativeshort-run and long-run effect on the aggregate domestic stock price Domesticcurrency appreciations, on the contrary, often lead to higher stock prices On the otherhand, when the aggregate domestic stock price increases, domestic currency valuedrops in short-term but goes up in long-term (Ajayi and Mougoue, 1996, Dimitrova,

2005, Wang et al., 2010) Ajayi and Mougoue (1996) study the relation

between stock prices and exchange rates in eight advanced economies According tothem, the stock index’s increasing in one country indicates an expanding in theeconomy with higher inflation expectations Due to perceiving higher inflationnegatively, the foreign investors’ demand for the currency of this country depresses

so the currency depreciates As regards to the effect of the currency on the stockmarket, the currency’s depreciation implies higher inflation in the future that makesthe investors have suspicions about the companies’ future performance As a result,the stock prices decrease This outcome is supported by data getting from U.Kmarkets Dimitrova (2005) affirms that the above relation is positive when the equitymarkets have prior changes and negative when the currency prices fluctuate first.However the empirical results are rather weak Furthermore, Dimitrova (2005) assertsthat the joint linkage help two markets to be self-recovered during the financial crisis.When the currency depreciates sharply, it makes the stock prices fall softly Due tojoint causality, the stock market’s collapse will trigger the currency’s appreciation andthen cause the stock market to rise again Wang et al (2010) have the samehypothesis with Ajayi and Mougoue (1996) but have the different explanation Theybelieve that changes in exchange rates lead to changes in international trades, therebyaffect the stock markets When exchange rate drops (domestic currency appreciates),the costs of imports decrease, thus with the same selling price for the merchandises,the profits increase and hence stock prices go up Conversely, when domesticcurrency depreciates, the revenues of the exporters decrease, therefore with the sameselling price for the merchandises, the profits decrease and the stock prices drop.Wang et al.’s above explanation is right only the exporters sell a fixed volume ofproducts However, when domestic currency is depreciated, the competitiveness ofdomestic products increases because their prices become cheaper Thus, exporters cansell their products with a greater volume, leading to the increase in their revenues.Conflicting with Wang et al (2010), some researchers state that stock prices’reactions to changes in currency value are ambiguous (Granger et al., 2000) Theyexplain this by analysing the effect of

exchange rate’s changes on the balance sheet of multinational corporations in someAsian countries during the Asian Crisis of 1997 Currency depreciation could make acompany’s value be changed however its increase or decrease is conditional uponwhether the company chiefly imports or exports Therefore, the net effect on thestock market index cannot be forecast.

Thirdly, there have been many researches on gold in the last number of yearsand most of the academic studies concentrate on the areas which are gold asdiversifier, as a hedge against inflation or other assets, and the operation’s efficiency

of the gold market This attractiveness comes from gold’s characteristic low/negativecorrelation and high positive skewness Baur and Lucey (2010) carry out aninvestigation the relation between U.S., U.K., German stock, bond returns and goldreturns They find that gold just acts as a hedge, “defined as an asset that isuncorrelated or negatively correlated with another asset or portfolio on average”against stocks and a safe haven, “defined as an ass et that is uncorrelated ornegatively correlated with another asset or portfolio in times of market stress orturmoil” for stocks not for bonds in any market Th ey further suggest investorsshould not keep gold too long because the safe haven just exist for a limited period oftime Besides analyzing whether gold and stocks having negative correlation, in otherwords, the absence of co-movement between gold and stocks, many studies suggestthe role and the proportion of gold in a portfolio in order to reduce risks and increasereturns (Sherman, 1982, Chua et al., 1990, Scherer, 2009, Klement and Longchamp,2010)

Finally, referring to the impact of oil price changes on stock prices, there areconflicting among the researchers Mussa (2000) indicates that when the oil pricesincrease, although the consumer and business confidence fall fairly strongly, the stockprices drop, the decrease is much more caused by non-oil related factors Someauthors, on the contrary, indicate that there is a relationship between in crude oilprices and equity values (El-Sharifa et al., 2005, Arouri, 2011, Filis et al., 2011) buttheir conclusions are different El-Sharifa et al (2005), test with data from the

United Kingdom, the largest oil producer in the European Union and find that therelationship is always positive, often highly significant and volatility in the price ofcrude oil has the direct impact on share values Conversely, Filis et al (2011) showthat the relation of two markets is negative regardless the origin of the oil priceshock, but exception in Global Financial Crises periods (2008), oil prices has apositive effect on stock prices based on data from six countries; Oil-exporting:Canada, Mexico, Brazil and Oil-importing: USA, Germany, Netherlands.Furthermore, Filis et al (2011) also believe that the relationship is not influenced bysupply-side oil price shocks, just by demand-side oil price shocks caused due toglobal business cycle's fluctuations or world turmoil (i.e wars) and oil markets is not

a “safe haven” for stock markets Arouri (2011) adds that the strength of the relationvaries greatly across European sector stock markets and there is a asymmetry in thereaction of stock returns to changes in the price of oil Especially, the most noticeablestudy is the one carried out by Narayan and Narayan (2009) By adding the US Dollar

- VN Dong exchange rate as an additional determinant of stock prices and exploringdaily data for the period 2000–2008, Narayan and Narayan (2009) find that there arerelations between Vietnam’s stock prices and oil prices, nominal exchange rates Arise in oil price and exchange rate (Vietnamese currency’s depreciation) make theVietnam’s stock p rice increase significantly However, this result is inconsistent withtheir theoretical expectations and they believe that Vietnam’s stock market wasaffected by the internal and domestic factors than oil price rise In addition toinvestigating the relationship between oil price and stock prices, there are somepapers exploring the impact of oil price risk on stock returns (Sadorsky, 1999, Basherand Sadorsky, 2006, Nandha and Faffa, 2008, Fayyad and Daly, 2011) By applying avector autoregression (Sadorsky, 1999), an international multi-factor model onemerging equity markets (Basher and Sadorsky, 2006), they find that oil price riskhas significant effect on stock returns Nandha and Faffa (2008) performs andempirical investigation with 35 DataStream global industry indices for the periodfrom April 1983 to September 2005 and find

that a rise in oil price affects negatively on equity returns for all sectors exceptmining, and oil and gas industries Thus, they suggest the international portfolioinvestors should hedge oil price risk By employing Vector Auto Regression (VAR)analysis with daily data from September 2005 to February 2010 relating to sevencountries (Kuwait, Oman, UAE, Bahrain, Qatar, UK and USA), Fayyad and Daly(2011) indicate that a rise in oil prices brought about the increase in the predictivepower of oil price on stock returns and the impulsive response of a shock to oil raisedduring Global Financial Crises periods (2008).

In summary, all the researches give the conflicting results about whether thereare the relationships between stock markets, gold prices and stock prices havingnegative correlation, the relationship between stock prices and crude oil prices ispositive or negative as well as stock prices’ reactions to changes in currency value

On the other hand, to the best of the author’s knowledge, there are very few publishedstudies examining the relationships between Vietnam’s stock prices and UnitedStates’ stock prices, exchange rates, gold prices Moreover, there is one study aboutthe impact of oil prices on Vietnam’s stock prices for the period 2000 – 2008 but theresult is inconsistent with the researchers’ theoretical expectations All the abovereasons motivate the author to carry out the study about the relationships betweenVN-Index and S&P 500 Index, US Dollar – VN Dong exchange rates, gold prices,crude oil prices with daily data from 2005 to 2010

CHAPTER 3: METHODOLOGY AND DATA

3.1 Methodology

3.1.1 Correlation

The correlation coefficient is traditionally employed to measure the degree ofintegration between any two variables using historical data Therefore, thecorrelations between the pairs of VN-Index and S&P 500 Index, VN-Index and goldprices, VN-Index and US Dollar - VN Dong exchange rates, VN-Index and crude oilprices are firstly analysed to test if investors have potential gain by diversifying thoseproducts However, the correlation coefficient just shows the short-run relationship.Therefore, using this parameter may supply the wrong results because economicvariables often separate in short terms (i.e periods up to 1 year) but converge overlonger terms To avoid the major disadvantage, cointegration tests represent any long-run combinations between couples of these economic variables

To explore the international stock market cointegration where the perfectmarket integration means a pair of stock prices is cointegrated and this also impliesthat there is little gain from international diversification Many authors investigate theco-movement in the long-run of the stock market prices using the technique of

cointegration to pinpoint whether there exists such long-run benefits frominternational equity diversification (Taylor and Tonks, 1989, Chan et al., 1992,Arshanapalli and Doukas, 1993, Chowdhury, 1994, Rogers, 1994, Arshanapalli et al.,

1995, Kwan et al., 1995, Chan et al., 1997, Masih and Masih, 1997, Kanas, 1998a,Kanas, 1998b, Kanas, 1999) Many of the previous studies have focused on thediversification benefits of international investment in relation to the cointegrationconcept The interpretation that no cointegration among two or more national stockmarkets and bond markets implies long-run gains from international portfoliodiversification has been suggested by several authors (Kasa, 1992)

This thesis will employ the Johansen cointegration technique to investigate thelinkages between VN-Index and S&P 500 Index, US Dollar - VN Dong exchangerates, gold prices and crude oil prices before and after 2008 Global financial crisis Inaddition, the analysis of these links has strong implication for diversification,especially, investment with the long-term horizons Furthermore, the knowledgeabout these links will help the investors to forecast the movement of VN-Indexbasing oneself on the information about the given changes of S&P 500 Index, USDollar - VN Dong exchange rates, gold prices and crude oil prices

In general, international investors will normally hold many products frommore than one national market in the expectation of achieving a reduction in risk viathe resulting international diversification This study will consider the diversificationbenefits from the perspective of Vietnamese investors contemplating to invest in theabove products If VN-Index is very strongly correlated with the others in the long-run, diversification will be less effective On the other hands, if Vietnamese stockmarket is operated independently with the others, diversification benefits will beachieved by Vietnamese investors Hence, an important indication of the degree towhich long-run diversification is available to investors is given by determiningwhether the stock, currency, gold and oil markets are integrated

In order to test for cointegration, the first step is to check if each series (inlevels) is integrated of the same order It is common in financial market data thatmost of the macroeconomic and financial time series are integrated of order one, inother words, they are following an I (1) process.

3.1.3 Unit root

A time series must be examined whether it is stationary because the use ofnon-stationary data can lead to spurious regressions If two time series are trendingover time, a regression of one on the other could have a high R2 even if the two aretotally unrelated Such a model would be termed a “ spurious regressions” It is non-stationary when it contains a unit root (integrated of order one) and its first difference

is stationary (integrated of order zero) For this reason, this study uses the Dickey–Fuller (DF) and Augmented Dickey-Fuller (ADF) methodologies to test for a unitroot The basis of the DF and ADF unit root tests is briefly outlined as follows

The standard DF test is carried out by estimating Eq (1) after subtracting yt−1from both sides of the equation:

where  =  −1

The null and alternative hypotheses may be written as H 0 :  =0 (  = 1) and

H 1 :  <0 (  < 1) and can be evaluated using the conventional t-ratio for  , t  =

^ ^ ^  /(SE(  )), where

 is the estimate of  , and SE( ) is the coefficient standard^error

The ADF test constructs a parametric correction for higher-order correlation

by assuming that the y series follows an AR (  ) process and adding  lagged

difference terms of the dependent variable y to the right-hand side of the test

regression:

 y t =  y t1 + x’t  +  1  y t1 + 2  y t2 + ···+   y t + ε t (3)Similar to the DF unit root test, this augmented specification is then used totest the null hypothesis H 0 :  =0 against the alternative hypothesis H 1 :  <0 usingthe conventional t-ratio, which is the ratio of the estimated  to the coefficientstandard error of the estimated  An important result obtained by Fuller is that theasymptotic distribution of the t-ratio for  is independent of the number of laggedfirst differences included in the ADF regression Moreover, while the assumption that

y follows an autoregressive (AR) process may seem restrictive, Said and Dickey(1984) demonstrate that the ADF test is asymptotically valid in the presence of amoving average (MA) component, provided that sufficient lagged difference termsare included in the test regression

The finding that many macro time series may contain a unit root has spurredthe development of the theory of non-stationary time series analysis Engle &Granger (1987, p 380) pointed out that a linear combination of two or more non-

stationary series may be stationary If such a stationary linear combination exists, thenon-stationary time series are said to be cointegrated The stationary linearcombination is called the cointegrating equation and may be interpreted as a long-runequilibrium relationship among the variables.

The purpose of the cointegration test is to determine whether a group of stationary series is cointegrated or not As explained below, the presence of acointegrating relation forms the basis of the vector error correction (VEC)specification In the current research, we employ the Johansen (1988) technique totest for the cointegration between those economic variables As this methodology iswell-known and widely used in the literature, we will only discuss it in brief

non-Suppose that a set of g variables (g≥2) are under consideration and they are I(1) and it is thought that they may be cointegrated A vector autoregressive model (VAR) with  lags containing these variables could be set up:

The Johansen test centers around an examination of the П matrix and П can beinterpreted as a long-run coefficient matrix, since in equilibrium, all the  y t i value

will be zero, and setting the error terms, u t, to their expected value of zero will leave

П y t i =0

The test for cointegration between the ys is calculated by looking at the rank

of П matrix via its eigenvalues There are two test statistics for cointegration underthe Johansen approach, the so-called trace statistic (  trace) and maximumeigenvalue statistic (  max) If the test statistic is greater than the critical values then

we reject the null hypothesis that there are r cointegration vectors in favour of the

alternative that there are r + 1 (for  trace) or more than r (for  max).

An important factor which influences the results of these tests is choosing theappropriate the lag length It is normally a problem in determining the optimalnumber of lags of the dependent variable As suggested by Brooks (2002), there aretwo ways to do this Firstly, it could be decided based on the frequency of the data.However, as high frequency data (daily) are used, it is not an obvious choice in thiscase Secondly, another option which is more appropriate in this case is to base thedecision on the information criterion There are three popular information criteria,Akaike’s (1974) information criterion (AIC), Schwarz’s (1978) Bayesian informationcriterion (SBIC) and the Hannan-Quinn criterion (HQIC) In this study, SBIC is used

to identify the optimal lag length as SBIC embodies a much stiffer penalty than AIC,while HQIC is somewhere in between

3.1.4 Granger causality

The Granger causality method seeks to determine how much of a currentvariable, y, can be explained by past values of y and whether adding lagged values ofanother variable, x, can improve the explanation Then, y is said to be “Granger-

caused” by x if x helps to predict y In other word s, one looks at the coefficients on the lagged x’s to see if they are statistically significant based on an F-test.

This method runs the bivariate regression of the form:

3.2.1 Data descriptive statistics

The daily Vietnam Stock Index (VN-Index) which is a capitalization-weightedindex of all the companies listed on the Ho Chi Minh City Stock Exchange isconsidered Vietnam stock prices The index was created with a base index value of

100 as of July 28, 2000 The VN-Index data are supplied by Advanced wavetechnology development investment joint stock company website

The data series, namely S&P 500 Index, are extracted from Yahoo Financewebsite The US Dollar - VN Dong exchange rates are the inter-bank average rate ofVietnam Dong versus United States Dollar quoted by the State Bank of Vietnam Thegold prices are London Afternoon (PM) Gold Prices, extracted from USA Goldwebsite The oil price data are the WTI crude oil spot prices and extracted fromUnited States Department of Energy via Wikiposit website All data are daily closingprices over the period from 4 January 2005 to 31 December 2010 and

divided into two sub-periods The first sub-period is running from the beginning ofthe data set to 28 December 2007 The second sub-period is from 2 January 2008 tothe end of the data set The rationale for this division is to avoid the excessivefluctuations during the financial crisis and to uncover the differences in linkagesbefore, during and after the 2008 Global Financial Crisis.

The trading time in Vietnam market is eleven hours ahead of New York time(U.S market), six hours ahead of London time (U.K market) Given that the U.Sclosing stock price, oil price and London afternoon gold price of a day (t−1) beforeVietnam stock market opening price, what follows is that if Vietnam stock prices aresensitive to the U.S stock price, oil price and U.K gold price changes and the market

is efficient, the U.S stock price, oil price and U.K gold price information in day (t−1)should be reflected in the opening price on day t of the Vietnam stocks If theVietnam stock market is partly efficient, only part of the information will be reflected

in the Vietnam opening price of day t, with the remaining changes spilling overduring the course of the day

CHAPTER 4: EMPIRICAL RESULTS

4.1 Descriptive statistics

The summary statistics of daily price and returns are provided in Table 1a, 1b

It can be seen in Table 1a that the skewness coefficient of VN-Index in the wholeperiod (from 2005 to 2010) is higher than zero (1.004322) It indicates the data set isnot symmetric and skewed to the right of the center point However, the skewnesscoefficients of VN-Index in all sub-periods are near zero and positive The kurtosiscoefficient of VN-Index in the whole period is near the expected value of 3 In thefirst sub-periods (4/1/2005 – 28/12/20 07), the kurtosis coefficient is much lower than

3 (1.576052) showing that the data set is less peaked in the mean than a normaldistribution In the second sub-period (2/1/2008 – 31/12/2010), the kurtosiscoefficient is much higher than 3 (5.392263) indicating that the data set has a higherpeak around the mean compared to a normal distribution

The skewness coefficients of S&P 500 Index in the whole period and in thesecond-sub period are negative indicating left-skewed data set The kurtosiscoefficient of S&P 500 Index is near expected 3 in the whole period but just littlelower or higher than 2 in every sub-period showing that the data sets are less peaked

in the mean than a normal distribution

In the whole period, the skewness coefficient of US Dollar - VN Dongexchange rate is positive and the kurtosis coefficient of US Dollar - VN Dongexchange rate is little higher than 3 indicating that the data set is right-skewed andnearly as peaked as a normal distribution However, in every sub-period, theskewness coefficients are near zero and the kurtosis coefficients are nearly 1.5showing that the data sets are nearly symmetric about its mean but have flat topsrelative to a normal distribution