The dependence between international crude oil price and vietnam stock market nonlinear cointegration test approach

MINISTRY OF EDUCATION AND TRAINING UNIVERSITY OF ECONOMICS OF HO CHI MINH CITY HÀ THỊ NHƯ PHƯƠNG THE DEPENDENCE BETWEEN INTERNATIONAL CRUDE OIL PRICE AND VIETNAM STOCK MARKET NONLINEAR

MINISTRY OF EDUCATION AND TRAINING UNIVERSITY OF ECONOMICS OF HO CHI MINH CITY

HÀ THỊ NHƯ PHƯƠNG

THE DEPENDENCE BETWEEN INTERNATIONAL CRUDE OIL PRICE AND VIETNAM STOCK MARKET NONLINEAR COINTEGRATION TEST APPROACH

ECONOMIC MASTER THESIS

Ho Chi Minh City -2015

UNIVERSITY OF ECONOMICS OF HO CHI MINH CITY

HÀ THỊ NHƯ PHƯƠNG

THE DEPENDENCE BETWEEN INTERNATIONAL CRUDE OIL PRICE AND VIETNAM STOCK MARKET NONLINEAR COINTEGRATION TEST APPROACH

Major: FINANCE - BANKING Code: 60340201

ECONOMIC MASTER THESIS

INTRUCTOR:

Assoc Prof NGUYỄN THỊ NGỌC TRANG

Ho Chi Minh City -2015

I commit that the economic master thesis titling “the dependence between international crude oil price and Vietnam stock market: Nonlinear cointegration test approach” was made by myself with the direction of Associate Professor Nguyen Thi Ngoc Trang The study’s results are truthful and data was collected from the credible sources such as: Ho Chi Minh City stock exchange, Energy Information Administration, the State Bank of Vietnam and General Statistics Office of Vietnam

Ho Chi Minh City, October 28th, 2015

Author

HA THI NHU PHUONG

TABLE OF CONTENT

SUB TITLE PAGE

COMMITMENT

TABLE OF CONTENT

LIST OF ABBREVIATIONS

LIST OF TABLES

LIST OF FIGURES

Abstract 1

1 Introduction 2

2 Literature Review 7

2.1 Literature Review 7

2.1.1 The relationship between crude oil price and stock market 7

2.1.1.1 Negative effect from crude oil price to stock market 7

2.1.1.2 Positive effect from crude oil price to stock market 9

2.1.1.3 Insignificant nexus between oil price and stock market 11

2.1.1.4 The imperial evidences about the relationship between oil prices and Vietnam stock market 12

2.1.2 The relationship between stock market and exchange rate 13

2.2 Overview about Vietnam stock market, oil sector and exchange rate regime 17

2.2.1 Vietnam stock market 17

2.2.2 Oil section 19

2.2.3 Exchange regime 22

3 Data and research methodology 25

3.1 Data 25

3.2 Methodology 30

3.2.1 Gregory and Hansen Test - GH test 30

3.2.2 Toda-Yamamoto (TY) version of Granger non-causality test 32

3.2.3 Error Correction Model 34

4 Researching result 36

4.1 Descriptive statistics 36

4.2 Unit root test 41

4.3 Gregory and Hansen Test-GH test 45

4.4 TY procedure of Granger non–causality test 46

4.5 Error correction model 49

5 Conclusion 51 References

Appendices

LIST OF ABBREVIATIONS

Abbreviation Discription

GDP Gross domestic product

MSCI Middle Small Market Capitalization but Investable

OPEC Organization of the Petroleum Exporting Countries

SVAR Structural Vector Autoregressive Model

TVTP Time-varying transition-probability

LIST OF TABLES

Table 3.1 Variable descriptions and sources: 30

Table 4.1 Descriptive statistic of three variables, exchange rate, crude oil price and VN index for the entire sample 38

Table 4.2 Descriptive statistic of three variables, exchange rate, crude oil price and VN index for four phases 40

Table 4.3 Unit root test result for entire sample 43

Table 4.4 Unit root test result for four phases: 44

Table 4.5 Threshold cointegration results 45

Table 4.6 Critical values of GH test with significant level at 5% and 3 regressors 45

Table 4.7 TY version of Granger non–causality tests 48

Table 4.8 Error correction model 49

LIST OF FIGURES Figure 1.1 Global crude oil and petroleum liquids consumption, supply and inventory in 2014 and 2015 (Source: Energy Information Administration) 2

Figure 1.2 Crude oil export revenues and productions from 2009 to 08 months of 2015 (source: General Statistics Office of Vietnam) 4

Figure 2.1 Vietnam Stock market capitalization to GDP (%) from 2004-2014 (Source Federal Reserve Economic Data) 18

Figure 2.2 Proportion of sectoral market capitalization in 2015 (source: HOSE website) 19

Figure 2.3 Average interbank exchange rates from 2006 to 2015 23

Figure 3.1 Graphical presentation of the series for first phase 26

Figure 3.2 Graphical presentation of the series for the second phase 27

Figure 3.3 Graphical representation of the third phase 28

Figure 3.4 Graphical representation of the fourth phase 29

Figure 3.5 Graphical representation of the entire sample 29

on Vietnam stock market, and there is a balance between benefits and damages in this period ECM model indicates that oil prices and stock prices have a positive relationship in short term, and the speed of adjustment of stock price to return the equilibrium state after a shock is slow around 0.25% These findings also have an important policy implication that helps the government intercept the market to reduce the negative effect from the energy shocks in general and oil price shocks in particular Those are to pay more attention to domestic production and trade revenues to get more stable budget, research the alternative energy and enhance international cooperation in the energy sector

Key words: Oil price, stock market, threshold cointergration

1 Introduction

The oil crude prices has fallen less than $50 per barrel, about 50% of August 2015 The main reason is the oil supply more than demand (see the figure 1) Growing oil inventories and supply typically put downward pressure on near-term prices The United States discovered and applied the new oil drill technology, called “shale oil revolution” This pushes the oil production is near 10 million barrels per day So that it can be offset the substantial oil supply disruption in the Organization of the Petroleum Exporting Countries (OPEC) However, the resumption of significant Libyan oil production, combined with the weakening outlook for global oil demand, the large economies in the word such as China, Russia, Europe area show not good performances about industrial production and expectation for economic growth

On July 14, the P5+1 (the five permanent members of the United Nations Security Council and Germany) and Iran announced an agreement that could result in relief from United States and European Union nuclear-related sanctions (which include some oil-related sanctions) If the agreement is implemented and sanctions relief occurs, it will put additional Iranian oil supplies on a global market that has already

Figure 1.1 Global crude oil and petroleum liquids consumption, supply and

inventory in 2014 and 2015 (Source: Energy Information Administration).

seen oil inventories raise significantly over the past year All things have put downward pressure on oil prices

Vietnam is net exporter of crude oil, but is a net importer of oil products, the volatilities in crude oil prices or costs of raw materials should affect to revenue resource of state budget and economic growth Volatilities of world crude oil price can affect negatively or positively to profit outlooks of the listed companies on Vietnam stock market The companies have inputs from the oil waste products (such as PLV-Petrolimex Petrochemical Corporation, BMP - Binh Minh Plastic Joint Stock Company, ) and other companies have input coming directly from the petroleum sector (PVT- PetroVietnam Transportation Corporation, PVS - PetroVietnam Technical Services Corporation, PVC - Drilling Mud Joint Stock Corporation, GAS - PetroVietnam Gas Corporation) are likely to be impacted negative by the decrease of oil price

The figure 1.2 shows that revenues and productions from 2009 -2015 The crude oil prices have decreased since third quarter in 2014, strongly affecting to crude oil revenues in 2015 although the export productions is bigger than the same period

We are the one of emerging country and its economy depends very large in export activities The decline of crude oil price will impact to budget revenues and deficit Energy, in particular crude oil has played an important role in our economy The purpose of this study is to investigate the relationship between crude oil price and Vietnam stock market In this paper, I will answer this question from several perspectives:

(i) Does it exist a long-run relationship between crude oil prices, stock prices and exchanges rates in entire sample?

(ii) Do the big world events such as the financial crisis in 2007 and the technology shock called “shale oil revolution” affect to the co-movement of the three?

(iii) Can the prior values of crude oil prices predict the future values of stock prices

or crude oil prices do Granger cause to stock prices?

(iv) Can the exchange rates do Granger cause to stock prices?

(v) Is the crude oil price an exogenous variable?

(vi) How does the impact of crude oil prices, exchanges rates on stock prices? And How adjusted speed of stock prices to return the equilibrium if having a shock in present?

F2a Crude oil export value

(unit: 1billion dollars)

F2b Crude oil export production

(unit: $1billion ton) Figure 1.2 Crude oil export revenues and productions from 2009 to 08 months of 2015 (source: General Statistics Office of Vietnam)

The study will provide investors the market outlook in the future before the oil price fluctuations in the present Therefrom the investor can make the appropriate hedging strategies and diversification Specially, for a country which the foreign currency primarily comes from exporting natural resources such as coals, oil …like

us, then the strong oil price volatilities will snappily impact its international balances, budget deficits and economic growth rates Through this study, I hope to provide further evidences about the energy importance to the economy and suggest solutions that government can intercept to reduce marker volatilities

0.00 5.00 10.00 15.00 20.00 25.00 30.00

The study uses the daily data with the period spanning from 01/03/2006 to 08/31/2015 Year 2006 chosen is the beginning study year, because Vietnam stock market has just had steps on obvious development in numbers of listed companies and trading volume, and can reflect partly economic situation The entire samples is divided into 04 phases, the first phase from 01/03/2006 to 12/28/2007 , the second phase from 01/02/2008 to 12/31/2009, the third phase from 01/04/2010 to 06/30/2014 and the last phase from 07/01/2014 to 08/31/2015, attaching with the significant decline in oil prices since the beginning of July 2014 due to excessive oil supply while the world economy is still gloomy and has not yet recovered

Threshold cointegration test of Gregory and Hansen (1996) is employed test the long-term relationship of oil prices, exchange rates and stock prices From the result’s GH test indicate there exists the long run nexus between these variables Interesting however, the null hypothesis of no co integration cannot reject at significant level 5% in all four phases This does prove that events in the research stage impact the long-term structure of oil prices and the stock market The interruptions make them impossible to reach the equilibrium in the limited time of subsample

Using the T-Y version of Granger non-causality test proposed by Toda-Yamamoto (1995) shows that there exists a unidirectional relationship, running from oil prices

to stock prices in the entire sample, and the second and third phase These are in and after the 2007 financial crisis In contrast to my expectation, the crude oil prices insignificantly cause Granger to stock price in the last phase The effects of oil price

on stock market maybe need several lags and this phase is not long enough to see them Besides that, some industries take benefits and some industries bear damages from the decline of oil prices, maybe there has the balance between benefits and damages in this period

Error Correction Model indicates that oil prices and stock prices have a positive relationship in short term The reason may come from the oil industry’s stocks have

large market capitalization and significant impact to the market index The decline

of oil prices will lead these stocks of these companies plummet, market sentiment in the short term push the market index decrease However, the exchange rates don’t affect the stock market in the short term The speed of adjustment of stock price to return the equilibrium state after a shock is slow around 0.25%

The study also suggest some policies that help the government intercept the market

to reduce the negative effect from the energy shocks in general and oil price shocks

in particular Those are to paying more attention to domestic production and trade revenues to get more stable budget, research the alternative energy and enhance international cooperation in the energy sector

The outline of this study is structured as follow: Section 1 introduction; section 2 literature reviews and overall about Vietnam stock market, oil sector, and Exchange regime; section 3 data and research method; section 4 researching result and section

5 conclusion

2 Literature Review

There are many papers investigating on the relationship between oil prices and stock prices However, the results of studies are not consistent; the differences depend on the features of each economy I classify the literature review of the nexus between stock prices and oil prices into three sections: (1) negative effect, (2) positive effect and (3) insignificant effect

2.1 Literature Review

2.1.1 The relationship between crude oil price and stock market

2.1.1.1 Negative effect from crude oil price to stock market

Chen 2010 investigates whether the high oil price can lead the stock market into the

bear territory or a recession of the stock market He uses monthly data from 1957M1 to 2009M5, including returns on Standard & Poor’s S&P 500 price is a proxy of stock market returns and oil price shocks are measured in different methods: changes in oil prices, oil price increases, net oil price increases, and scaled oil price increases In this paper, he employs a time-varying transition-probability (TVTP) Markov-switching model, which allows the probability of switching between states (bull markets vs bear markets) to characterize the fluctuations in the stock market and to identify the impact of oil prices on the switching between stages The empirical evidences show that an increase in oil prices leads to a higher probability of a bear market emerging

Juncal and Fernando (2013) examine the impact of oil price shocks on stock

returns in 12 oil importing European economies using Vector Autoregressive (VAR) and Vector Error Correction Models (VECM) for the period 1973M2 – 2011M12 The oil price shocks was divided into 02 kinds, they are oil supply and oil demand shocks, which are measured by world oil production and world oil prices respectively Moreover, there are other explanation variables added to models such as industrial production indexes, short-term interest rates in order to express

different channels through which oil prices could affect stock returns The main contribution is identification that stock returns may respond in different ways to supply and demand shocks The oil supply shocks tend to have a greater negative impact on stock market returns than oil demand shocks Generally, the oil price change lowers economic activity in oil importing economies because of more expensive energy inputs However, if the increase comes from demand shock, then economic activity in oil importing economies can be impacted negative (due to higher production cost ) or positive (due to increase world income and consumption)

Rumi et al (2011) aims to how important the oil price changes and oil price

volatility impact on Korean stock market – a net oil - importing country The model used is VECM with monthly observations from May 1988 to January 2005 of interest rates, industrial production, real stock returns, real oil prices and oil price volatility In which, the oil prices and interest rate are exogenous variables The result of paper shows that there exists the long – run and stable relationship between four variables, especially, oil price movement has significant effect on stock returns and the real stock returns are the main channel of short-run adjustment to long-run equilibrium The oil price changes have two ways to impact to firm profitability; those are through production cost and investor sentiment to stock market index Besides that, authors also suggest three solutions coping with high and volatile oil prices such as increasing government’s strategic oil reserves, considering oil-saving measures and enhancing dialogue with oil-exporting countries

Wensheng et al (2014) examines the impact of structural oil price shocks on the

covariance of the US stock market return and stock market volatility by using the SVAR model in period from January 1973 to December 2013 The SVAR with recursive structural restrictions follows up the order of variables such as: oil supply, aggregate demand, oil market-specific demand, and covariance of return and volatility Positive shocks to aggregate demand and to oil-market specific demand

are associated with negative effects on the covariance of return and volatility An unanticipated reduction in crude oil production is associated with a statistically significant increase implied-covariance of return and volatility The spillover index between the structural oil price shocks and covariance of stock return and volatility

is large and highly statistically significant

2.1.1.2 Positive effect from crude oil price to stock market

Zhu et al (2013) researches on structural dependence between crude oil prices and

Asia-Pacific stock market returns There are many different approaches, however, traditional approaches such as VAR or VECM require variables follow normal or Student t-distributions Furthermore, it is well known that traditional mean variance optimization analysis portfolios are symmetric measures that cannot capture non-linear dependence or changes in the tails of asset return curves and that investors pay closer attention to downside than to upside risk The model proposed is the copula - GARCH models The data set includes daily crude oil prices and ten Asia –Pacific countries' stock returns from January 4, 2000 to March 30, 2012 The data is divided into two subsamples referred to as pre-crisis (January 4, 2000 to 23 September 2008) and post-crisis (September 24, 2008 to March 30, 2012), respectively to explore differences of the dependence between phases The results show that the dependence between crude oil prices and Asia-Pacific stock market returns is generally weak, that it was positive before the global financial crisis, except in Hong Kong, and that it increased significantly in the aftermath of the crisis They found that the tail dependence was very weak before the crisis and that the lower tail dependence was much higher than the upper tail dependence after the crisis, except in the cases of Japan and Singapore

Moya-Martínez et al (2013) examines oil price sensitivity of Spanish industries

for the period January 1993 to December 2010 Data set includes weekly observations of stock market returns (proxied by indices General de la Bolsa de Madrid), weekly returns of 14 industries, the Dated Brent crude oil prices and

interest rates A multifactor market model is used to investigate the impact of oil price changes on industry stock returns and is estimated for sub-samples based on the breakpoints identified by Bai and Perron multiple structural break tests The result indicates that the exposure of Spanish industries to oil price is rather limited, although the oil price exposure is different considerably between industries The exposure was very small in the 1990s, period of fairly stable and cheap oil prices It increases in the 2000s with higher and more volatile oil prices Because aggregate demand-side oil price shocks are driven by fluctuations in the global business cycle,

so crude oil price and Spanish equity market have moved together

Riza et al (2015), this study contributes to the literature on the relationship

between oil and stock markets by formally testing whether oil price risk is systematically priced in the cross-section of stock returns in net oil exporting nations, the Gulf Cooperation Council (GCC) nations Using firm-level data on Gulf Arab stock markets for the period March 31, 2004 and March 31, 2013 such as stock price, number of shares and book equity data, combine with exchange rate, three-month U.S Treasury Bill rate as the risk-free rate, and Brent crude oil prices The results indicate that stocks that are more sensitive to oil price fluctuations indeed yield significantly higher returns, suggesting that oil price risk exposure can serve as a return predictor in these stock markets Interestingly however, there is no yield evidence of a significant risk premium associated with oil price risk in the presence of firm-level risk factors, suggesting that firm-level factors like firm size and idiosyncratic volatility controls for the oil price risk in returns, rendering the oil factor insignificant in their test

Su-Fang et al (2011), investigate the relationship between oil prices and the

Chinese stock market at the sector level Using the a panel cointegration with structural breaks and Granger causality framework and data collected from July

2001 to December 2010 including monthly real oil price, the real stock price indices for the 13 major sectors and a controlling variable, interest rate Their

findings show that there exist a positive relationship between oil prices and sectoral stock prices in the long run It may also indicate that the impact of other substitute energy sources (e.g., coal) or other internal and domestic factors on these sectoral stocks are more dominant than the increase in oil prices The results of Grange causality tests find a unidirectional, long-run and short-run relationship running from oil prices and sectoral stocks to the interest rate for the period 2001/07–2005/10 Interesting however for period 2005/12–2007/06, there is only the unidirectional long –run Granger causality running from sectoral stocks to oil prices and from sectoral stocks to the interest rate Additional, the long-run Granger causality is bidirectional between oil prices, the interest rate and sectoral stocks for 2007/08–2008/11 and 2009/01–2010/12

2.1.1.3 Insignificant nexus between oil price and stock market

Apergis and Miller (2008) models the impact of oil market shocks to stock market

returns The components of oil market shock determined by modifying the procedure of Kilian (2008a) include oil-supply shocks, global aggregate-demand shocks, and global oil-demand shocks The authors use the monthly data for the eight countries -Australia, Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States – spans 1981 to 2007 and VAR model The results show that different oil-market structural shocks play a significant role in explaining the adjustments in stock-market returns But, the magnitude of such effects proves small The oil-supply and global aggregate demand shocks do not significantly explain the stock return in Australia, whereas the idiosyncratic demand shocks affect the stock return in Canada at a weaker level of significance Further, the Granger temporal causality tests suggest a strong role for idiosyncratic demand shocks leading the stock market returns, whereas the oil-supply and global aggregate-demand shocks do not as a rule temporally lead the stock-market return

Janabi et al (2010) test for the efficient market hypothesis in the six Gulf

Cooperation Council (GCC) countries equity markets—namely Bahrain, Kuwait,

Qatar, Oman, Saudi Arabia, and the United Arab Emirates The dataset used in this study consist of daily observations of the Standard & Poor's (S&P) Emerging Market Indexes for six countries for the period April 03, 2006 through March 28,

2008 and two benchmark indexes for oil and gold Since the data is non-normal with time-varying volatility, the authors apply a new methodology based on the leverage bootstrapped simulation technique The causality test results reveal that neither the oil price index nor the gold price index causes the equity price indexes of the six GCC markets This means that the information contained in the gold and oil price indexes cannot improve the forecast of the equity market index in each of the six GCC states Thus, the possibility of short-term arbitrage is ruled out and the six GCC equity markets can be considered as informationally efficient with respect to oil and gold prices

2.1.1.4 The imperial evidences about the relationship between oil prices and Vietnam stock market

Vo Xuan Vinh (2014) investigates the long and short-run relationship between

Vietnam’s stock prices (VN-Index) and the US stock prices (S&P 500 Index), the

US Dollar - VN Dong exchange rates, gold prices, and crude oil prices The paper uses the daily data from 01/04/2005 to 12/31/2012 and divides the entire sample into two sub-periods to account the effect of 2008 Global financial crisis, the first one is 2005-2007 and the second one is 2008-2012 In the short term the paper indicates a high level of correlation between the VN-Index and the crude oil price The evidences from the bivariate cointegration test show that there exist the long-run relationship between VN index and crude oil prices in the whole periods and the second sub-period In this sub period, there only exist the long run-relationship between VN index and exchange rate The results of Grange causality tests find a unidirectional relationship running from oil prices to the stock market in the entire period and the first sub-period Additional, there is a unidirectional relationship running from Exchange rate to the stock prices in the first sub-period

Narayan and Narayan (2010) model the impact of oil prices on stock prices in

Vietnam stock market The study uses the daily data for period from 2000-2008 of stock prices, nominal exchange rates and oil prices Using the cointegration tests including Johansen test and structural break cointegration tests finds there exists the long run relationship between stock market, oil prices and exchange rates Running the long-run elasticities, the authors find that oil prices and exchange rates have a positive and significant effect on stock prices However, the result is inconsistent with the theoretical expectations Because they think that there are some different factors contributing to the stock market boom in this period such as increasing foreign portfolio investment capital flows and local market participants The study also combines the long –run model with the short-run model by using error correction model The results show the determinants of stock prices are statistically significant in the long run and they are insignificant in the short run

Nguyen and Ishaq (2012) study the dependence structures and/or tail dependence

between oil price changes and stock market indices The tail dependence helps to determine whether the two variables move together in the same or opposite directions This paper employs two relatively new methods, namely the Plots (Kendall or K plot and chi plot) based on nonparametric method and the copula, based on parametric method The daily data sets include WTI crude oil prices and stock prices from China and Vietnam The study period in Vietnam is from 2002 to August 2009 and in China is from 2000 to August 2009 The results show that there

is left tail dependence between international oil price changes and Vietnam’s stock market, meaning that if the international oil price decreases, Vietnam’s stock market will also decrease accordingly While there is no evidence showing that tail dependence in the relationship of international oil price changes and China’s stock market

2.1.2 The relationship between stock market and exchange rate

The exchange rate is used as a controling variable in my study I investigate the nexus between exchange rate and stock market based on two approaches

Firstly, in accordance with Krugman and Obstfeld, (1997, Chapt 16): The connection between the asset market equilibrium and the exchange rate is the interest parity condition

𝑅 = 𝑅∗+ (𝐸𝑒 − 𝐸)/𝐸 where R and R* are interest rates of domestic and foreign currencies and E and 𝐸𝑒denote exchange rate and expected future exchange rate respectively For asset markets to remain in equilibrium, ceteris paribus, a decline in domestic output (hence lower R due to reduced demand for money) must be followed by a currency depreciation (a greater value for E) Aggarwal (1981) has argued that a change in exchange rates could change stock prices of multinational firms directly and those

of the domestic firms indirectly In the case of a multinational firm, a change in exchange rate will change the value of that firm’s foreign operation, which will be reflected on its balance sheet as a profit or a loss Consequently it contributes to current account imbalance Once the profit or a loss is announced, that firm’s stock price will change This argument shows that devaluation could either raise or lower

a firm’s stock price depending on whether that firm is an exporting firm or it is a heavy user of imported inputs If it involves in both activities, its stock price could move in either direction This is true especially when most stock prices are aggregated to investigate the effects of devaluation on stock markets From this viewpoint, exchange rate change is expected to give rise to stock price change Such

a causal relation is known as the traditional approach

Secondly, as capital market become more and more integrated, changes in stock prices and exchange rates may reflect more of capital movement than current account imbalance The central point of such a portfolio approach lies in the following logical deductions: A decrease in stock prices causes a reduction in the

wealth of domestic investors, which in turn leads to a lower demand for money with ensuing lower interest rates The lower interest rates encourage capital outflows ceteris paribus, which in turn is the cause of currency depreciation Under the assumption of the portfolio approach, stock price is expected to lead exchange rate with a negative correlation If a market is subject to the influences of both approaches simultaneously, a feedback loop will prevail with an arbitrary sign of correlation between the two variables

Bahmani et al (1997) are the pioneers of using cointegration and Granger causality

techniques to investigate the interaction between stock prices and FX markets The data they used consist of monthly S&P500 and effective exchange rates of US dollar from December 1973 to December 1983 A two-stage systematic autoregressive procedure was employed developed by Hsiao (1981) They found bidirectional causality in the short run However, there is no long-run relationship between the variables

Nieh and Lee (2001), their major findings from their time-series estimations

supported the results of Bahmani-Oskooee and Sohrabian (1992) and reported no long-run

Ajayi and Mougoue (1996) showed a negative short-run and positive long-run

impact of stock prices on domestic currency value Particular, in a study, recent advances in time-series are applied to examine the intertemporal relation between stock indices and exchange rates for a sample of 8 advanced economies An error correction model (ECM) of the 2 variables is employed to simultaneously estimate the short-run and long-run dynamics of the variables The ECM results reveal significant short-run and long-run feedback relations between the 2 financial markets Specifically, the results show that an increase in aggregate domestic stock price has a negative short-run effect on domestic currency value In the long run, however, increases in stock prices have a positive effect on domestic currency

value On the other hand, currency depreciation has a negative short-run and run effect on the stock market

long-Roll (1992) also studied the US stock prices and exchange rates and found a

positive relationship between the two markets On the other hand, Chow et al (1997) examined the same markets but found no relationship between stock returns and real exchange rate returns They repeated the exercise with a longer time horizons and found a positive relationship between the two variables

Abdalla and Murinde (1997) studied the prices in FX and stock markets in four

less developed countries, namely India, Korea, Pakistan, and Philippines within a VECM (vector error correction model) framework For the period January 1985 to July 1994, they find unidirectional causal linkage between exchange rates and stock prices for Pakistan and Korea The real effective exchange rate Granger cause the stock price index in India, but no causal relationship was found in the case of Philippines

Ajayi et al (1998) examine the interaction between daily stock returns and changes

in the exchange rates for two groups of markets: Advanced economies (including Canada, Germany, France, Italy, Japan, UK, and USA) whose data start from April

1985 to August 1991 and Asian emerging markets (including Taiwan, Korea, the Philippines, Malaysia, Singapore, Hong Kong, Indonesia, and Thailand) whose data cover December 1987 to September 1991 In the case of Indonesia, the Philippines, Taiwan, and all advanced markets, there is one-way causal relationship running from the stock market to the FX market, while in the case of Korea, the relationship

is reverse They also perform causality test on weekly data and find that the results are in line with the daily data for the advanced markets However, a very different result is obtained for emerging markets: unidirectional causal relationship from the stock returns differential to the change in the exchange rate is found for Thailand and Malaysia

Granger et al (2000) apply Granger causality test and IRF to examine the

interaction between stock prices and FX market Nine Asian countries and regions are selected for the empirical analysis: Hong Kong, Indonesia, Japan, South Korea, Malaysia, the Philippines, Singapore, Thailand, and Taiwan Their study employs daily data from 3 January 1986 to 14 November 1997 (3097 observations) Three sub-periods are fractionized from the whole analyzed period: the first period started from the first observation to 30 November 1986; the second period extends from 1 December 1987 to the end of 1994 and it is called after crash period; and the third one covers the rest observations In the first period, by using 10% as significance level, there is no causal linkage for those countries except Hong Kong and South Korea, which have one-way causality from exchange rate to stock price and from stock price to exchange rate, respectively In period 2, the authors find unidirectional causal linkage from FX markets to stock markets in Malaysia and the Philippines and reverse linkage in Taiwan During the last period, it is found that the change in stock prices will lead the change in the exchange rates in Taiwan, and the reverse relationship is found in Japan, Thailand, Singapore, and Hong Kong In the rest markets, bidirectional causal relationships between the two variables were established Moreover, the study shows that the predictable portion of stock price changes can be improved by including the exchange rate variation within the regression

2.2 Overview about Vietnam stock market, oil sector and exchange rate regime

2.2.1 Vietnam stock market

Vietnam is a MSCI frontier market likes Pakistan, Sri Lanka and Bangladesh Frontier markets are investable but have lower market capitalization and liquidity than the more developed emerging markets

Vietnam has two stock exchanges – the Ho Chi Minh Stock Exchange (HOSE) and the Hanoi Stock Exchange (HNX) HOSE is mostly dedicated to equities trading, while HNX trades equities, bonds and over the counter securities

HOSE was initially established as the Ho Chi Minh City Securities Trading Center (HoSTC) in 2000 It was later upgraded and renamed in 2007 Prior to March 1st

2002, shares were only traded on alternate days The Vietnam equities market has come a long way in a short time In January 2006, there were only 34 companies listed on HOSE; this has increased to 303 in 2012, with the market cap expanding from USD 1.1billion in 2006 to USD 29.9 billion in 2012 In 2015, there are 600-

700 companies listed on HOSE with the market capital of USD 60 billion

Figure 2.1 shows the market capitalization to GDP from 2004 to 2016 This ratio significantly increases over year-to-year In 2013-2014, the market capitalization accounted about 31% GDP and increased 24% compared to 2006 However, compared with other emerging markets such as China, India, and South Africa, then Vietnam stock market is relatively less developed

Figure 2.1 Vietnam Stock market capitalization to GDP (%) from 2004-2014 (Source Federal Reserve Economic Data)

0.41 0.61

7.06

18.05 15.95 14.16

17.17 14.07 16.25 31.00 31.50

Figure 2.2 Proportion of sectoral market capitalization in 2015 (source: HOSE

website)

Figure 2.2 expresser the proportion of sectoral market capitalization in 2015 It shows that oil industry has the fifth percentage of market capitalization about 6% Therefore, the volatilities in world oil prices may have an immediate impact on the market

2.2.2 Oil section

Over the past few decades Vietnam has emerged as an important oil and natural gas producer in Southeast Asia Vietnam has boosted exploration activities, allowed for greater foreign company investment and cooperation in the oil and gas sectors, and introduced market reforms to support the energy industry These measures have helped to increase oil and gas production Also, the country’s rapid economic growth, industrialization, and export market expansion have spurred domestic energy consumption

Recent, successful offshore exploration has contributed to a substantial increase in proved crude oil reserves, which grew to 4.4 billion barrels as of January 2012 from 0.6 billion barrels in 2011, according to the Oil and Gas Journal Reserves remained

at 4.4 billion barrels in 2013 and 2014 Ongoing exploration activities could

Real estate Contruction Electricity/ gas Energy Oil industry

Transportantion Others

increase this figure in the future, as Vietnam’s waters remain largely underexplored Vietnam is currently the third-largest holder of crude oil reserves in Asia, behind China and India

Vietnam, the Philippines, Malaysia, China, Taiwan, and Brunei each claim sovereignty over the Spratly Islands in the South China Sea However, Vietnam has reached agreements with several of its neighbors to conduct joint exploration for oil and natural gas resources in the region Disputes with China are yet to be resolved

As recently as May 7th, 2014, tensions between China and Vietnam flared following

a skirmish over a Chinese oil rig that Vietnam claims was planning to illegally drill into the Vietnam's continental shelf In addition, on September 15, 2014, Vietnam and India agreed to expand joint upstream oil and gas activities in the contested waters of the South China Sea, despite China's objections

According to Ministry of Industry and Trade, in September 2015, the nationwide crude oil production is estimated at about 13.9 million tons, increase 9% compared with the same period of 2014

By the way, gas production is estimated at 7.8 billion m3, increase 1.8% compared with the same period of 2014 Petroleum production is estimated 5.1 million tons, increase 25.7% compared with the same period of 2014

Vietnam produced around 353,700 barrels per day (bbl/d) of oil in 2013, which is roughly 3% less than what it produced in 2012 and 12% down from a peak of 403,000 bbl/d in 2004 The Cuu Long Basin has been the primary area for oil production

Vietnam is a net exporter of crude oil, but is a net importer of oil products With oil consumption increasing year-over-year and overall by more than 70% from 238,400 bbl/d in 2004 to 413,000 bbl/d in 2013, the country must import a majority of refined products to satisfy demand

Vietnam has one operating refinery, the 140,000-bbl/d Dung Quat refinery, which came online in 2009 Vietnam's state-owned Vietnam Oil & Gas Corporation (PetroVietnam) is looking to boost crude distillation capacity to around 200,000 bbl/ d by 2017 and to develop Dung Quat's ability to handle sweet and less expensive sour crude oil from Russia, the Middle East, and Venezuela Vietnam plans to offer 49% of Dung Quat's equity to foreign investors in order to finance the upgrade and expansion of Dung Quat In addition, the Nghi Son refinery, which is now under construction, is expected to come online in mid-2017 and the Vung Ro refinery, which will be designed by Japan's JGC Corporation, is expected to be completed in 2019

PetroVietnam is the key company in the oil and natural gas sectors and serves as the primary operator and regulator of the industry Oil and natural gas production is either undertaken by PetroVietnam's upstream subsidiary, PetroVietnam Exploration and Production (PVEP), or through PetroVietnam's joint venture with other companies

International Oil Companies (IOCs) such as ExxonMobil, Chevron, and Zarubezhneft have formed partnerships with PetroVietnam IOCs must receive approval from the Oil and Gas Department of the Prime Minister and must negotiate upstream licenses with PVEP

Average oil price in September 2015 is about 48 USD/ barrel, total exploiting production of PVN by the end of September is estimated at about 21.86 million tons In this period, the group exports 11.9 million tons of crude oil but the revenue only reaches 3.05 billion dollars compared to September 2014 exporting 6.8 million tons, reaching 5.98 billion dollars The crude oil price has decreased leading the exporting volume increases but the exporting revenue still decrease

Vietnam currently holds 24.7 trillion cubic feet (Tcf) of proved natural gas reserves,

up from 6.8 Tcf in 2011, according to OGJ Increased foreign investment since

2007 has led to greater exploration, significantly increasing Vietnam's proved natural gas reserves

Vietnam produced 346 billion cubic feet of marketed natural gas in 2013, all of

which was domestically consumed, according to the BP Statistical Review of World

Energy 2014 The country is currently self-sufficient in natural gas, but

PetroVietnam predicts a growing supply gap characterized by demand surpassing supply, particularly in southern Vietnam Vietnam's 2011 Gas Master Plan includes initiatives to promote natural gas in the primary energy mix, gas production and consumption targets, and detailed infrastructure plans for gas gathering systems, pipelines, and gas processing facilities

The Vietnamese government has considered importing liquefied natural gas (LNG)

in the southern part of the country to meet growing natural gas demand and fill the supply gap PetroVietnam Gas, a subsidiary of PetroVietnam, signed a memorandum of understanding and a front-end engineering and development contract with the Tokyo Gas Company to develop the Thi Vai LNG terminal in the Vung Tau province The terminal is expected to be operational in 2017 PV Gas also signed a gas sales and purchase agreement with Gazprom of Russia on March

6, 2014 Under the agreement, PV Gas will receive 48 Bcf/y via the Thi Vai LNG terminal A second terminal, Son My LNG, is also planned for operations starting in

2018, although construction has yet to begin

2.2.3 Exchange regime

In Vietnam, exchange rate policy can be seen as a part of monetary policy to affect

to supply and demand of foreign currency on exchange market and archive the targets of monetary policy is to control inflation and stabilize the purchasing power

of money, encourage exports, restrict imports and enhance the economic growth Vietnam officially applies the pegged exchange rates with crawling bands in March

1989 According to this exchange regime, the exchange rates of commercial banks

were followed to fluctuate within the limits of 5% of official exchange rate In the period from 1990-1991, commercial banks were followed to determine exchange rates not exceeding 0.5% of official exchange rates

Figure 2.3 Average interbank exchange rates from 2006 to 2015

After Vietnam took part in World Trade Organization in 2007, the foreign investment capital flows into the economy sharply increased in 2007-2008 This led the foreign currency supply exceed demand in the large scale, so the exchange rates

of commercial banks were always at the lower bound of allowed amplitude However, the impacts from financial crisis led the foreign investment capital flows into Vietnam in the second haft of 2008 reverse In addition, the pressure of inflation, increasing trade deficit, large difference between domestic and foreign oil price had the exchange rates of commercial be in the upper bound of allowed amplitude in 2009 State Bank of Vietnam (SBV) had to constantly increase the official exchange rate and loosen the trade band Especially, on 26/11/2009 SBV was forced to officially adjust 5.4% of basic exchange rate, the highest rate per day for 10 years

In the period from 2012 – 2014, the exchange rates were quiet stable and fluctuate

in the allowed rate band Inter-bank average exchange rate was fixed at 20,282 VND/USD over the long time before being slightly adjusted to 21,036 VND/USD since the end of June, 2013 The trade band was fixed at 1% since the beginning of February 2011

However, China strongly devaluates their Renminbi currency and the market investors are being worry about the US Federal Reserve System (Fed) is going to adjust interest rates In order to actively lead the market and deal with the adverse impacts of possibility of increase of interest rate from Fed, SBV adjusts the average interbank exchange rate from 21,673 VND/USD to 21,890 VND/ USD applied from August, 18th 2015 Also, SBV simultaneously issues the 1636/ QD-NHNN decision about VND spot rates of commercial banks, according to it, the band of VND/USD

is adjusted from ±2% to ± 3%

3 Data and research methodology

3.1 Data

Daily dataset consisting of Brent crude oil price, average interbank exchange rate and Vietnam stock market index proxied by VN index is chosen in periods from 01/03/2006 to 08/31/2015 Although Vietnam stock market officially started in

2000, during 2000-2005 the market size only reached 1% GDP and the market activities was fairly quiet From 2006, the stock market had significantly chances about both number of listed companies and trade volume Specifically, the market size had a huge leap to 22.7% GDP and moreover, capital mobilization such IPO, issuing bond, indeed only incurred from 2006 onward Therefore the beginning year

of study period is 2006 to ensure the stock market be able to reflect the economic situation

Many important events has incurred during the study period such as 2007 financial crisis, USA shale oil revolution have enormous impacts on economy, stock market, oil prices, To have a better insight, we divide the study period into 04 phases attached to 02 main events, those are 2007 financial crisis and USA shale oil revolution in 2014 (See the figure 3.5)

The first phase from 01/03/2006 to 12/28/2007 is called pre-crisis phase (see figure3.1): in this phase, the Vietnam stock market was the most flourish and vibrant In the end of 2006, VN-index increased by 2.5 compared to in the beginning of year, led to the market capitalization reaching USD 13.8 billion accounted about 22.7% GDP The number of the listed companies was up about 5 times, VN-index rise further 500 points from 300 points in the late 2005 to 800 in the late 2006 Securities law in effect from January 01st, 2015 has contributed in promoting the development and integration of the stock market The transparency of the listed companies has been strengthened After that VN-index peaked at 1,170.67 and increased by 23.3% compared to the late 2006 It can be said that the

market reached the biggest growth rate in this stage, especially; this rate is 126% just within the first quarter In HCM Exchange, the average of trade volume was more than VND 1,000 billion/ session and it was VND 300 million at HNX

Figure 3.1 Graphical presentation of the series for first phase

The second phase from 01/02/2008 to 12/31/2009 (see the figure3.2) is called crisis phase: Along to the general trend of economy, Vietnam stock market in 2008 sharply declined It was impacted directly by macro-economic instability such as the inflation increase, large trade deficit that forced the government to make a tight monetary policy and squeeze the cash flows into securities Additionally, the

influence of global financial crisis had the VN-index bottomed at 235.5 points as of 02/24/2009

Figure 3.2 Graphical presentation of the series for the second phase.

The third phase from 01/04/2010 to 06/30/2014 is called after crisis phase (see the figure 3.3): The stock market also snappily changes but it was following to a positive trend in 2010-2012 From the beginning of 2013, the market shaped a quiet certain recovery trend As of the end of 2013, VN-index reached 505 points, up 23% Vietnam stock market was considered as one of the 10 markets with the strongest recovery growth and degree compared to the world This is the period that the crude oil price remained at the stable levels more than $100/ barrel

Figure 3.3 Graphical representation of the third phase

The last phase from 07/01/2014 to 08/31/2015 (see the firgure3.4) is called USA shale oil revolution phase: Crude oil prices fell sharply in the fourth quarter of 2014

as robust global production exceeded demand After reaching monthly peaks of

$112 per barrel and $105 per barrel in June, crude oil benchmarks Brent fell to less than $50/ barrel in August In additional, on the August, 18th 2015, State bank SBV adjusts the average interbank exchange rate from 21,673 VND/USD to 21,890 in order to actively lead the market and deal with the adverse impacts of possibility of increase of interest rate from Fed Two events significantly impact to Vietnam economy

Figure3.4 Graphical representation of the fourth phase

Figure 3.5 Graphical representation of the entire sample

Table 3.1 Variable descriptions and sources:

Stock price Vietnam stock market

index – VN-Index It’s transformed by

Loil Website of Energy

Information Administration (EIA): www.eia.doe.gov

Exchange

rate

Inter-bank average exchange rate It’s transformed by logarithm

Lex Website of the State Bank of

Vietnam (SBV):

http://www.sbv.gov.vn/

3.2 Methodology

3.2.1 Gregory and Hansen Test - GH test

GH test was developed by Gregory and Hansen (1996), this is a residual-based test

of cointegration with null hypothesis is no cointegration and alternative hypothesis that there may be one break in the cointegrating vector The test is an extension of the ADF, Zt, and Za test for cointegration and is non-informative with respect to the timing of the break To test break points in cointegration vector, the authors used three alternative models: (i) level shift (only intercept can change), (ii) level shift with trend (only slope coefficients can change) and (iii) regime shift (both intercept and slope coefficients can change)

In this study, I use the third model “regime shift” The dependent variable is lvni and independent variables are lex and loil The standard model of cointegration with

a trend and no structural change is:

Lvni t = α 0 + β 01 Loil t + β 02 Lex t + ε t (1)

To model the structural change, the dummy variables are defined:

in equation (2) and proposed bias-corrected modified ADF*, Zα* and Zt* test:

𝑍𝛼∗ = 𝑖𝑛𝑓𝜏∈𝑇

𝑍𝑡∗ = 𝑖𝑛𝑓𝜏∈𝑇

𝐴𝐷𝐹∗= 𝑖𝑛𝑓

𝜏∈𝑇

Where 𝜏 ∈ T In principle the set T can be any compact subset of (0, 1) In practice,

it will be to be small enough so that all of the statistics discussed here can be calculated A standard choice is T = (0.15, 0.85) Although it contains an uncountable number of points, so it’s important to consider the step functions on T

taking jump only on the points {(i/n), Integer} For the computation purposes, the

test statistic is computed for each break point in the interval T = ([0.15n], [0.85n]) and [ ] denotes integer part The smallest values of the above statistics in equations (3), (4) and (5), across all values of 𝜏 ∈ T will be chosen and compared with their critical values to accept or reject the null hypothesis of no cointegration

3.2.2 Toda-Yamamoto (TY) version of Granger non-causality test

The Granger (1969) approach to the question of whether X causes Y is to see how much of the current Y can be explained by past values of Y and then to see whether adding lagged values of X can improve the explanation Y is said to be Granger-caused by X if X helps in the prediction of Y, or equivalently if the coefficients on the lagged X are statistically significant Note that two-way causation is frequently the case; X Granger causes Y and Y Granger causes X

Toda and Yamamoto (1995) introduced the TY version of Granger non-causality test on the parameter matrices even if the processes may be integrated or cointegrated of an arbitrary order It can be applied without pretesting for cointegration and does not require use of an estimated cointegration equation into the analysis

TY procedure employs a modified Wald test for restriction on the parameters of the VAR (k) It follows an asymptotic Chi-square distribution with k degrees of freedom ( 2(k)) (k is the lag length) And here are the steps of TY procedure:

(i) Determining the maximal order (dmax) of integration of variables by using the ADF test, PP test with the null hypothesis is non-stationary; as well as the KPSS test with the null is stationary It's good to have a cross-check

(ii) Estimating VAR at level of variables and choose the optimum lag length (k) through the usual information criteria, such as AIC, SIC, SB, HQ, MAIC

(iii) Estimating an augmented VAR (k+dmax) in levels of variables and making the residual test such as autocorrelation, heteroscedasticity to make sure this VAR is stable

(iv) Conduct standard Wald test on the first k parameters of other variable in the VAR(k+d) If significant, then reject null of non-causality

The TY version of VAR (k+dmax) in this study is presented as

⌉3𝑋1

The null hypothesis of no Granger causality between Loil, Lex and Lvni and the direction of Granger causality can be detected by applying standard Wald tests to the first ‘k’ VAR coefficient matrix

H01: 𝐴12,1 = 𝐴12,2 = ⋯ = 𝐴12,𝑘 = 0, implies Lex doesn’t Granger cause to Loil H02: 𝐴21,1 = 𝐴21,2 = ⋯ = 𝐴21,𝑘 = 0, implies Loil doesn’t Granger cause to Lex H03: 𝐴13,1 = 𝐴13,2 = ⋯ = 𝐴13,𝑘 = 0, implies Lsen doesn’t Ganger cause to Loil H04: 𝐴31,1 = 𝐴31,2 = ⋯ = 𝐴31,𝑘 = 0, implies Loil doesn’t Granger cause to Lsen, v.v