Relationships between Vietnam''''s stock prices and United States''''s stock prices, exchange rates, gold prices, crude oil prices

UNIVERSITY OF ECONOMICS HOCHIMINH CITY --- oOo --- ĐỖ NGỌC ANH RELATIONSHIPS BETWEEN VIETNAM’S STOCK PRICES AND UNITED STATES’ STOCK PRICES, EXCHANGE RATES, GOLD PRICES, CRUDE OIL PRIC

UNIVERSITY OF ECONOMICS HOCHIMINH CITY

- oOo -

ĐỖ NGỌC ANH

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

MASTER THESIS

Ho Chi Minh City – 2011

MAJOR: BANKING AND FINANCE MAJOR CODE: 60.31.12

MASTER THESIS INSTRUCTOR: Dr VÕ XUÂN VINH

Ho Chi Minh City – 2011

ACKNOWLEDGEMENTS

First of all, I would like to express my deep gratitude to Dr Vo Xuan Vinh for his 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 and teaching

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

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

I want to extend my appreciation to my classmates, my friends who make great contribution in collecting data and give me encouragement Without their help and sharing, I could not successfully complete this MBA program and the thesis Finally, special thanks are for my colleagues and my family, who have understood, encouraged and created as much better conditions as possible for me to continue studying until the end of the course and completing this thesis

ABTRACT

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

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

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

exchange rates, gold prices, crude oil prices, correlation, cointegration, Granger causality

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

ABBREVIATIONS

S&P 500 Index Standard and Poor's 500 Index

USD/VND United States Dollar - Vietnam Dong exchange rate

VN-Index Vietnam Stock Index listed on the Ho Chi Minh City

Stock Exchange

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 the economy Thus, an examination of the main economic factors’ impact on stock indices has an important implication for both the government and investors This thesis 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 time frame before and after the 2008 Global Economic Crisis because of the following reasons:

Standard and Poor's 500 Index (S&P 500 Index) is a capitalization-weighted index of 500 stocks The index is designed to measure performance of the broad United States’ economy through changes in the aggregate market value of 500 stocks representing all major industries The index was developed with a base level

of 10 for the 1941- 43 base periods Thus many mutual funds, exchange-traded funds, and other funds such as pension funds, are designed to track the performance

of the S&P 500-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 price indices in many countries around the world In Vietnam, there are a lot of investors and 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 published research investigates the relationship between them, just very simple analyses by drawing some graphs

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

between two variables is very meaningful to governments, multinational corporations and investors Firstly, they may be the basis for the governments’ decisions about the monetary and fiscal policy When the policy-makers decide to expand or contract their countries’ monetary and fiscal policies targeted at neutralizing the interest rate and real exchange rate or to depress their domestic currency value in order to boost the export, they should be aware how such policies effect to the stock market Secondly, the knowledge about the relationship between two variables may help the multinational corporations to predict the fluctuations in the exchange rate relying on the changes in stock prices and hence to manage their exposure to foreign contracts, exchange rate risk and stabilize their earnings Thirdly, currency has been increasingly included as an asset in the investors’ portfolios Thus, the more accurate the investors estimate of the variability of a given portfolio, the more benefit they will achieve Last, the analysts believe that understanding the linkage between currency and 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 sharp depreciation of Thai baht triggering the dropping of other currencies in the region

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

Furthermore, the crude oil price is determined the cost of a country and usually used to analyse the economic growth There are many researches having focused on the relationship between crude oil price and economic indicators and commodities (Gisser and Goodwin, 1986, Hamilton, 1983), the impact of oil price changes 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 relations between 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 critical issues 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 500 Index, VN-Index and gold prices, VN-Index and US Dollar - VN Dong exchange rates, VN-Index and crude oil prices by employing a number of econometric and financial modelling techniques To explore the short-run relationships among the variables, the techniques of correlations are utilized The technique of Granger causality is applied to test whether movements in one variable appear to lead those

of another The technique of cointegration is employed to investigate the long-run relationships

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 economy playing an important role in effecting and determining the stock prices These studies employ econometric models including correlation, cointegration, Granger causality techniques to examine the relationships among the international stock markets, relationships between stock prices and bond prices, stock prices and other variables in the economy However, these studies show the mixed and conflicting results due to conducting 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 with one another variable in the economy There are few researches investigating the relationships among the combination of stock prices, oil prices, gold prices and exchange rates

Firstly, in terms of the integration of international equity markets, many studies 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 between countries (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 methodology, Kasa (1992) examines the major equity markets over the 1974–1990 period and finds a single cointegrating vector indicating a very low level of integration Other studies also find the similar results 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 Bierens nonparametric 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 the period from 1983 to 1996 The results show that the US market is not pairwise cointegrated with any of the European markets and this suggests low levels of integration so that there are potential long-run diversification benefits for US investors seeking to invest in European markets Vo and Daly (2005b) analyse and test 10-year period daily return data from 1994 to 2003 of Asian equity market indices and selected advanced nation’s equity market indices They employ correlation, cointegration and the Granger causality test The results from their tests suggest a weak causal relationship between Asian equity markets and developed countries’ equity markets This implies that there are potential diversification benefits for foreign investors from Australia and the US investing in Asian equity markets In addition, employing the same methodology, Vo and Daly (2005a) also suggest that there are very low linkages among European equity markets and suggest that there are potential diversification advantages On the other hand, other authors state that the long-run covariances between markets are higher than in the short-run, and hence the benefits of international diversification are lower (Grubel and Fadner, 1971, Panton et al., 1976, Taylor and Tonks, 1989) Employing the more sophisticated Johansen multivariate approach, other studies yield the contrary results of strong integration (Chou et al., 1994, Hung and Cheung, 1995, Kearney,

1998, Gilmore and McManus, 2002, Manning, 2002, Ratanapakorn and Sharma, 2002)

Secondly, in terms of the relationship between stock prices and exchange rates, most of studies show that falling in domestic currency value has a negative short-run and long-run effect on the aggregate domestic stock price Domestic currency appreciations, on the contrary, often lead to higher stock prices On the other hand, when the aggregate domestic stock price increases, domestic currency value drops 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 to them, the stock index’s increasing in one country indicates an expanding in the economy with higher inflation expectations Due to perceiving higher inflation negatively, 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 stock market, the currency’s depreciation implies higher inflation in the future that makes the investors have suspicions about the companies’ future performance As a result, the stock prices decrease This outcome is supported by data getting from U.K markets Dimitrova (2005) affirms that the above relation is positive when the equity markets have prior changes and negative when the currency prices fluctuate first However the empirical results are rather weak Furthermore, Dimitrova (2005) asserts that 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 to joint causality, the stock market’s collapse will trigger the currency’s appreciation and then cause the stock market to rise again Wang et al (2010) have the same hypothesis with Ajayi and Mougoue (1996) but have the different explanation They believe that changes in exchange rates lead to changes

in international trades, thereby affect 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 domestic currency depreciates, the revenues of the exporters decrease, therefore with the same selling 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 of products However, when domestic currency is depreciated, the competitiveness of domestic products increases because their prices become cheaper Thus, exporters can sell 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) They explain this by analysing the effect of

exchange rate’s changes on the balance sheet of multinational corporations in some Asian countries during the Asian Crisis of 1997 Currency depreciation could make

a company’s value be changed however its increase or decrease is conditional upon whether the company chiefly imports or exports Therefore, the net effect on the stock market index cannot be forecast

Thirdly, there have been many researches on gold in the last number of years and most of the academic studies concentrate on the areas which are gold as diversifier, 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/negative correlation and high positive skewness Baur and Lucey (2010) carry out an investigation the relation between U.S., U.K., German stock, bond returns and gold returns They find that gold just acts as a hedge, “defined as an asset that is uncorrelated or negatively correlated with another asset or portfolio on average” against stocks and a safe haven, “defined as an asset that is uncorrelated or negatively correlated with another asset or portfolio in times of market stress or turmoil” for stocks not for bonds in any market They further suggest investors should not keep gold too long because the safe haven just exist for a limited period

of time Besides analyzing whether gold and stocks having negative correlation, in other words, the absence of co-movement between gold and stocks, many studies suggest the role and the proportion of gold in a portfolio in order to reduce risks and increase returns (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 are conflicting among the researchers Mussa (2000) indicates that when the oil prices increase, although the consumer and business confidence fall fairly strongly, the stock prices drop, the decrease is much more caused by non-oil related factors Some authors, on the contrary, indicate that there is a relationship between in crude oil prices and equity values (El-Sharifa et al., 2005, Arouri, 2011, Filis et al., 2011) but their 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 the relationship is always positive, often highly significant and volatility in the price of crude oil has the direct impact on share values Conversely, Filis et al (2011) show that the relation of two markets is negative regardless the origin of the oil price shock, but exception in Global Financial Crises periods (2008), oil prices has a positive 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 by supply-side oil price shocks, just by demand-side oil price shocks caused due to global 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 relation varies greatly across European sector stock markets and there is a asymmetry in the reaction of stock returns to changes in the price of oil Especially, the most noticeable study 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 exploring daily data for the period 2000–2008, Narayan and Narayan (2009) find that there are relations between Vietnam’s stock prices and oil prices, nominal exchange rates A rise in oil price and exchange rate (Vietnamese currency’s depreciation) make the Vietnam’s stock price increase significantly However, this result is inconsistent with their theoretical expectations and they believe that Vietnam’s stock market was affected by the internal and domestic factors than oil price rise In addition to investigating the relationship between oil price and stock prices, there are some papers exploring the impact of oil price risk

on stock returns (Sadorsky, 1999, Basher and Sadorsky, 2006, Nandha and Faffa,

2008, Fayyad and Daly, 2011) By applying a vector autoregression (Sadorsky, 1999), an international multi-factor model on emerging equity markets (Basher and Sadorsky, 2006), they find that oil price risk has significant effect on stock returns Nandha and Faffa (2008) performs and empirical investigation with 35 DataStream global industry indices for the period from April 1983 to September 2005 and find

that a rise in oil price affects negatively on equity returns for all sectors except mining, and oil and gas industries Thus, they suggest the international portfolio investors should hedge oil price risk By employing Vector Auto Regression (VAR) analysis with daily data from September 2005 to February 2010 relating to seven countries (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 predictive power of oil price on stock returns and the impulsive response of a shock to oil raised during Global Financial Crises periods (2008)

In summary, all the researches give the conflicting results about whether there are the relationships between stock markets, gold prices and stock prices having negative correlation, the relationship between stock prices and crude oil prices is positive 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 published studies examining the relationships between Vietnam’s stock prices and United States’ stock prices, exchange rates, gold prices Moreover, there

is one study about the impact of oil prices on Vietnam’s stock prices for the period

2000 – 2008 but the result is inconsistent with the researchers’ theoretical expectations All the above reasons motivate the author to carry out the study about the relationships between VN-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 of integration between any two variables using historical data Therefore, the correlations 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 are firstly analysed to test if investors have potential gain by diversifying those products However, the correlation coefficient just shows the short-run relationship Therefore, using this parameter may supply the wrong results because economic variables often separate in short terms (i.e periods up to 1 year) but converge over longer terms To avoid the major disadvantage, cointegration tests represent any long-run combinations between couples of these economic variables

3.1.2 Cointegration

Cointegration has been showing as an important technique to examine whether the economic and financial time series are cointegrated Besides, there are many areas of finance where cointegration might be expected to hold Therefore, the methodology of cointegration has been more and more widely used in the empirical studies

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

cointegration to pinpoint whether there exists such long-run benefits from international 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 the diversification benefits of international investment in relation to the cointegration concept The interpretation that no cointegration among two or more national stock markets and bond markets implies long-run gains from international portfolio diversification has been suggested by several authors (Kasa, 1992)

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

In general, international investors will normally hold many products from more than one national market in the expectation of achieving a reduction in risk via the resulting international diversification This study will consider the diversification benefits from the perspective of Vietnamese investors contemplating

to invest in the above 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 stock market is operated independently with the others, diversification benefits will be achieved by Vietnamese investors Hence, an important indication

of the degree to which long-run diversification is available to investors is given by determining whether the stock, currency, gold and oil markets are integrated

In order to test for cointegration, the first step is to check if each series (in levels) is integrated of the same order It is common in financial market data that most of the macroeconomic and financial time series are integrated of order one, in other 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 of non-stationary data can lead to spurious regressions If two time series are trending over time, a regression of one on the other could have a high R2 even if the two are totally 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 unit root 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−1 from both sides of the equation:

where α= ρ−1

The null and alternative hypotheses may be written as H0: α=0 (ρ= 1) and

H1: α<0 (ρ< 1) and can be evaluated using the conventional t-ratio for α, tα=

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

by assuming that the y series follows an AR (ρ) process and adding ρlagged difference terms of the dependent variable y to the right-hand side of the test

regression:

∆yt= αyt−1 + x’tδ + β1∆ yt−1 + β2 ∆ yt−2 + ···+ βρ ∆ yt−ρ + εt (3)

Similar to the DF unit root test, this augmented specification is then used to test the null hypothesis H0: α=0 against the alternative hypothesis H1: α<0 using the conventional t-ratio, which is the ratio of the estimated α to the coefficient standard error of the estimatedα An important result obtained by Fuller is that the asymptotic distribution of the t-ratio for α is independent of the number of lagged first 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 a moving average (MA) component, provided that sufficient lagged difference terms are included in the test regression

The finding that many macro time series may contain a unit root has spurred the 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, the non-stationary time series are said to be cointegrated The stationary linear combination is called the cointegrating equation and may be interpreted as a long-run equilibrium 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 a cointegrating relation forms the basis of the vector error correction (VEC) specification In the current research, we employ the Johansen (1988) technique to test for the cointegration between those economic variables As this methodology is well-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 ρlags containing these variables could be set up:

The Johansen test centers around an examination of the П matrix and П can

be interpreted as a long-run coefficient matrix, since in equilibrium, all the ∆yt−i

value will be zero, and setting the error terms, u t, to their expected value of zero will leave П yt−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 under the Johansen approach, the so-called trace statistic (λtrace) and maximum eigenvalue statistic (λmax) 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 λtrace) or more than r (for λmax)

An important factor which influences the results of these tests is choosing the appropriate the lag length It is normally a problem in determining the optimal number of lags of the dependent variable As suggested by Brooks (2002), there are two 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 this case Secondly, another option which is more appropriate in this case is to base the decision on the information criterion There are three popular information criteria, Akaike’s (1974) information criterion (AIC), Schwarz’s (1978) Bayesian information criterion (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

“Granger-caused” by x if x helps to predict y In other words, 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 weighted index of all the companies listed on the Ho Chi Minh City Stock Exchange is considered 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 wave technology development investment joint stock company website The data series, namely S&P 500 Index, are extracted from Yahoo Finance website The US Dollar - VN Dong exchange rates are the inter-bank average rate

capitalization-of Vietnam Dong versus United States Dollar quoted by the State Bank capitalization-of Vietnam The gold prices are London Afternoon (PM) Gold Prices, extracted from USA Gold website The oil price data are the WTI crude oil spot prices and extracted from United States Department of Energy via Wikiposit website All data are daily closing prices 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 of the data set to 28 December 2007 The second sub-period is from 2 January 2008 to the end of the data set The rationale for this division is to avoid the excessive fluctuations during the financial crisis and to uncover the differences in linkages before, during and after the 2008 Global Financial Crisis

3.2.2 Time differences

Karolyi and Stulz (1996) and Alaganar and Bhar (2001) pointed out that there are different time zones in international markets and markets are not open and closed at the same time Therefore, in estimation, it is important that the time differences between markets should be considered Another important factor is that national holidays also differ between countries To deal with this, the closing prices from the previous days for non-trading days are used

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.S closing stock price, oil price and London afternoon gold price of a day (t−1) before Vietnam stock market opening price, what follows is that if Vietnam stock prices are sensitive 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 the Vietnam 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 over during the course of the day