The effects from the international crude oil returns to Asian frontier oil and gas company stock returns
Science & Technology Development Journal – Economics - Law and Management, 5(2):1535-1548
Research article
Open Access Full Text Article
The effects from the international crude oil returns to Asian
frontier oil and gas company stock returns
Thi Ngan Nguyen1,*, Hoang Trung Nghia1, Truong Huynh Thuy Vi2
ABSTRACT
Asian frontier markets present compelling investment opportunities for investors seeking higher
returns and low correlation with traditional assets. As such, it is important for financial market partic-
Use your smartphone to scan this
QR code and download this article
ipants to understand the volatility transmission mechanism across these markets in order to make
better portfolio allocation decisions. This study investigates the magnitude of return and volatility
spillovers from the international crude oil markets on the Asian frontier oil and gas stock markets.
In particular, we construct mean return and volatility spillover models to discuss whether regional
(DSE, CSE, HNX, HOSE) and global (ICE) market impacts are crucial for the determination of oil & gas
stock returns in Bangladesh, Sri Lanka, and Vietnam by employing ARMA(1,1)-GARCH(1,1) model.
Using daily returns from January 4, 2010 to December 31, 2019, the findings of this paper show that
the Brent oil and WTI crude oil markets influence the Sri Lanka and Vietnamese oil and gas stock
markets. WTI price changes, however, have a relatively minor impact on Sri Lanka companies. For
Bangladesh, it is noticeable that none of the spillover effects is statically significant. The results are
explained by different levels of the reform process in the energy sector as well as by the importance
of oil in these markets. In general, these frontier markets, especially the Bangladesh and Sri Lanka
may offer promising diversification benefits due to low correlations with developed equity markets.
These results are important for economic policymakers and investors in understanding the magni-
tude of volatility spillover effects of the international crude oil on these markets. Investors can use
this information to make better portfolio allocation decisions to reduce risks and enhance returns.
Key words: Spillover, volatility effect, crude oil futures price, oil company stock returns, Asian
frontier markets
1University of Economics and Law,
Vietnam National University Ho Chi
Minh City, Vietnam
2SSI Securities Corporation, Vietnam
political, geopolitical tensions and unforeseen events.
Oil prices constantly adjusted when worries about the
INTRODUCTION
Crude oil is arguably the most influential physical
health of the world’s economy and increasingly uncer-
commodity in the world and plays a prominent role in
tain trade relations due to the fact that U.S.- China
an economy, thus it has become one of the major in-
Correspondence
trade battle would have crimped global oil demand
dicators of economic activities of the world. Oil price
Thi Ngan Nguyen, University of
Economics and Law, Vietnam National
University Ho Chi Minh City, Vietnam
whereas U.S. inventories and oil exports continued to
has become a fundamental factor of today’s market
increase. en, oil prices surged by 14.3% aꢀer only
economy as it influences financial markets as well as
2 days (September 15 and 16, 2019), when a drone at-
consumers, corporations and governments. Oil fluc-
Email: ngannt@uel.edu.vn
tacked upon a refinery of Saudi Aramco Group which
cut off 5 percent of daily global oil supply for weeks.
However, oil prices were soon corrected, as produc-
tion activities fully recovered in the following weeks.
Aꢀerwards, the positive movements came from the
U.S- China trade talks in late 2019. e frequent up-
heavals in crude oil market have entailed the shocks
to the international stock market.
In view of the crucial role of oil in the global economy
and its spectacular price fluctuations in recent years,
it is worth concerning about the impact of the price
of oil on stock prices, especially on oil-related listed
companies. ere are a number of previous works
which have studied the interactions between oil prices
tuation has not only a tremendous impact over the
stock markets but also a major influence on the global
economy: oil is needed for industrial purpose such as
power generation, chemical products, transportation
etc. In particular oil demand and supply drive volatil-
ity and any upward or downward price movements is
tracked by any financial market player as it directly in-
fluences future outlook and real growth of exporting
and importing countries.
Over the past few years, a rising demand from devel-
oping economies and limited supplies from oil pro-
ducing countries due to political tensions have fre-
quently pushed oil prices to dramatically high levels.
In 2019, the global crude oil market witnessed the per-
History
• Received: 29-12-2021
• Accepted: 13-5-2021
• Published: 18-5-2021
DOI : 10.32508/stdjelm.v5i2.749
Copyright
© VNU-HCM Press. This is an open-
access article distributed under the
terms of the Creative Commons
Attribution 4.0 International license.
plexing movements of oil prices due to escalation in and stock markets. However, very little attention is
Cite this article : Nguyen T N, Nghia H T, Vi T H T. The effects from the international crude oil returns to
Asian frontier oil and gas company stock returns. Sci. Tech. Dev. J. - Eco. Law Manag.; 5(2):1535-1548.
1535
Science & Technology Development Journal – Economics - Law and Management, 5(2):1535-1548
paid to the direct impact of the oil price shocks on
stock returns of oil-related companies.
LITERATURE REVIEW
In recent years, the on-going liberalization of capital
mobility along with advances in information technol-
ogy has caused international financial markets to be-
come highly integrated and interdependent. Conse-
quently, the deeper the level of global financial inte-
gration, the more connected between stock markets
On the one hand, frontier markets are increasingly
sought by investors in search of higher returns and
low correlations with global markets. In particular,
outsized gains in the equity markets have resulted in
increased investment portfolio allocations in frontier
assets. As such, it is important for financial partici- increases, resulting in a strong spillover effect across
markets. Volatility spillover between markets or as-
sets is a tendency for volatility to change in one mar-
ket or asset following a change in the volatility of an-
the process of volatility spillover to exhibit the spread
of news from one market that affects the volatility
spillover process of another market.
pants to understand the volatility transmission mech-
anism across these markets in order to make bet-
ter portfolio allocation decisions. Despite the grow-
ing attention to frontier markets among the invest-
ment community, very little research actually includes
them.
Focusing on Asian frontier markets, this paper at-
tempts to empirically examine the level of spillover
effects from the international crude oil markets on
oil and gas company stock returns in the three Asian
Frontier Markets (Bangladesh, Sri Lanka, Vietnam),
based on crude oil futures returns and the oil and
gas company stock returns. For the daily returns
from 2010 to 2019 of the international crude oil fu-
tures prices from the Brent markets, the WTI mar-
kets and Bangladeshi, Sri Lankan as well as Viet-
namese oil & gas stock prices are analyzed utilizing
the ARMA(1,1)-GARCH(1,1). In particular, the re-
turn spillover is modeled using ARMA(1,1), volatility
spillover is estimated using a two-step GARCH(1,1)
e comprehensive influence of oil price shocks on
economies is not only an important issue among var-
ious regulatory agencies, enterprise managers and
market participants, but also under scrutiny by many
economists. Many studies have been done on inter-
national transmission of stock returns in the context
of the mean and/or volatility spillover effects. Most of
them show some evidences of international transmis-
sion from major markets, such as the US and Japanese
markets, toward the other developed and emerging
markets.
of stock returns and volatility between the U.S. and
Japanese stock markets, a two-step GARCH approach
model. By employing a mean and volatility spillover is utilized. By using futures prices on the S&P500
and Nikkei225 stock indexes, they found that there are
unidirectional contemporaneous return and volatility
spillovers from the U.S. to Japan. Specifically, the U.S.
influence on Japan in returns is approximately four
times as large as the other way around. ere are also
no significant lagged spillover effects in both returns
and volatility from the Japan to the U.S. while a signif-
icant lagged volatility spillover is observed from the
U.S. to Japan.
model that deals with the Brent oil and WTI oil stock
market shocks as exogenous variables in ARMA(1,1),
GARCH(1,1) for the Asian Frontier Markets to an-
alyze the international transmission between these
markets. e empirical results in this research may be
helpful for academics, domestic policy makers, and fi-
nancial participants understanding the magnitude of
volatility spillover effects of the international crude oil
on these markets. Moreover, this study contributes
to the growing literature on the spillover effects and
volatility transmission of equity returns.
e rest of the paper is organized as follows. eo-
retical overview and literature review on the study of
return and volatility spillover across markets is pre-
sented in the next section. Section 3 gives details
about research data, the descriptive statistics and fi-
nancial model for estimating volatility transmissions
and spillover effects and as well as estimation proce-
dure. e empirical results are given in section 4 and
finally, in the last chapter, the paper closes with con-
cluding comments.
cients to stock market returns in order to examine
how the market crash in the U.S. influenced the stock
markets in Japan and the U.K. by using the GARCH
model, co-integration tests, and the probability of spe-
cific events. e results show that the U.S. stock mar-
ket crash significantly increased the correlation coef-
ficients between multiple markets.
Expanding this issue to the context of oil and stock
markets is also of great concern due to the important
role of oil in the global economy. e international
crude oil market is the source of the primary feedstock
for creating refined petroleum products produced in
1536
Science & Technology Development Journal – Economics - Law and Management, 5(2):1535-1548
oil refineries across the world. Crude oil has been shocks cause stock markets to fall. With regard to
deemed the life blood of industrial economics. Crude the effect of oil price shocks on stock market volatil-
oil is arguably the most influential physical commod- ity, Malik and Ewing11 find evidence of significant
ity in the world and plays a prominent role in an econ- transmission of volatility between oil and some sec-
omy. erefore, oil prices fluctuation clearly affects the tors in the US stock market, Vo12 shows that there
world economy in many different ways. Rising crude is inter-market dependence in volatility between U.S.
oil prices raises the cost of production of goods and ser- stock and oil markets, and Arouri et al.13 report that
vices, transportation and heating cost. As a result, it there is volatility transmission from oil to European
provokes concerns about inflation and restricted discre- stock markets. Degiannakis et al.14 show that a rise
tionary spending of consumer and produces a negative in price of oil associated with increased aggregate de-
effect to financial markets, consumer confidence, and mand significantly raises stock market volatility in Eu-
rope, and that supply-side shocks and oil specific de-
e value of stock prices in an equity pricing model mand shocks do not affect volatility
theoretically equals the discounted earning expecta- More specifically, as studies particularly focus on the
tion of companies or future cash flows. erefore, oil effects of oil price changes on oil & gas stock market.
price shocks influence stock prices through expected e study by Jones and Kaul5 was the first contribu-
cash flow and discount rate. Since oil is one of the tion to examine the reaction of stock markets to oil
crucial inputs for goods and services production, a shocks. e authors consider four developed markets
rise in oil prices without substitute inputs increases (Canada, Japan, UK and US) and draw empirical re-
production costs; which in turns decrease cash flows sults from a standard present value model. ey find
and stock prices. In addition, rising oil prices affects that changes in stock prices can be partially accounted
the discount rate by influencing the inflation pressures for by the effect of oil price movements on the current
which also leads to the decision making by the central and future cash-flow.
bank to raise interest rate. erefore, the corporate in- Subsequently, Sadorsky6 and Apergis and Miller15
,
vestment decision can be affected directly by change among others, also provided evidence of significant
in the discount rate and change in stock price relative responses of stock returns to oil shocks from mak-
to book value. However, it is worth noting that not ing use of various approaches such as vector autore-
all companies will react the same way to changes in gressive (VAR) model, international multifactor as-
crude oil prices. Indeed, the direction of stock price set pricing models, cointegration, and vector error-
reactions will depend on whether the company is an correction model (VECM).
oil producer or an oil consumer. Oil producers will Arouri et al.13 studied on return and volatility trans-
profit from an oil price increase while oil consumers mission between world oil prices and stock markets of
will suffer from it. Overall, since the great majority of the GCC countries. is paper investigated the return
companies are oil consumers, it is logical to expect a links and volatility transmission between oil and stock
negative effect of oil prices on stock prices.
markets in the Gulf Cooperation Council (GCC)
From an empirical perspective, a number of previous countries over the period 2005–2010. ey em-
papers have observed and provided explanation of the ployed a recent generalized VAR-GARCH approach
oil price and stock market relationship and the nega- which allows for transmissions in return and volatil-
tive impact of oil price on stock markets. Early pa- ity. In addition, they analyzed the optimal weights
pers finding a negative relationship between oil prices and hedge ratios for oil-stock portfolio holdings. On
and stock market returns include Jones and Kaul5, the whole, their results point to the existence of sub-
for Canada and the U.S., Sadorsky6 for the U.S., and stantial return and volatility spillovers between world
negative connection between oil prices and global in- crucial for international portfolio management in the
oil prices leads to a higher probability of a declining For frontier markets, Gomes and Chaibi16 examined
S&P index. In an important contribution, Kilian and volatility spillovers between oil prices and stock re-
oil prices on the stock market, it is essential to iden- a bivariate BEKK-GARCH(1,1) model to simultane-
tify the underlying source of the oil price shocks. Kil- ously estimate the mean and conditional variance be-
ian and Park10 show that oil price increases driven tween equity stock markets (twentyone national fron-
by aggregate demand cause U.S. stock markets to rise tier stock indices and two broad indices – the MSCI
and that those driven by oil-market specific demand Frontier Markets and the MSCI World) and oil prices.
1537
Science & Technology Development Journal – Economics - Law and Management, 5(2):1535-1548
ey examined weekly returns from February 8, 2008 Yt = Φo + Φ1Yt−1 + Φ2εt−1 + ut
to February 1, 2013 and find significant transmission
of shocks and volatility between oil prices and some of
the examined markets. Moreover, this spillover effect
is sometimes bidirectional.
e implication of the AR (1) is that the time series
behaviour of Yt is largely determined by its own value
in the preceding period. So, what will happen in t is
largely dependent on what happened in t-1. Also, the
MA (1) shows that Yt depends on the value of the im-
mediate past error, which is known at time t.
investigated the asymmetric impact of the oil price
fluctuation on the Vietnamese stock market in the
short run and long run aꢀer the financial crisis in
2008. Using non-linear autoregressive distributed
lag model (ARDL) associated with the bound test to
monthly data of VN-Index, crude oil Brent price, in-
dustrial production index, and money supply. e re-
sults showthat in the long run, the oil price has the sig-
nificant negative impact on the domestic stock market
and the stock market in the short run has an opposite
response with the oil price fluctuation compared with
in the long run.
GARCH(1,1) model
Mainly financial and economic time series have a fea-
ture of volatility clustering, meaning that conditional
heteroskedasticity exists. While the generalized con-
ditional heteroskedasticity GARCH model can cap-
ture better the relevant features of financial data.
e GARCH model allows the conditional variance
to be dependent upon previous own lags, so that the
conditional variance equation in GARCH(1,1) model
is given by:
σ2 = α0 + α1u2t−1+ α2σ2
Surprisingly up to this period, there is a very limited
amount of literature work based on the relationship
between oil price and oil-related company stock price,
especially in the three Asian Frontier markets. is
paper, therefore, aims to extend the current literature
on the relationship.
t
t−1
In GARCH(1,1) model σ2 is known as the condi-
t
tional variance since it is a one-period ahead esti-
mate for the variance calculated based on any past
information thought relevant. Using the GARCH
model it is possible to interpret the current fitted vari-
ance as a weighted function of a long-term average
value (dependent on α0), information about volatility
during the previous period (α1u2t−1) and the fitted
variance from the model during the previous period
(α2σ2t−1).
Under GARCH, the conditional mean equation which
describes how the dependent variable, Yt, varies over
time, could take the form of ARMA(1,1). e ARMA-
GARCH (1,1) model is given by:
METHODOLOGY
ARMA(1,1) model
e family of autoregressive integrated moving aver-
age (ARIMA) model, usually associated with Box and
Jenkins isan important class of time series models. An
analysis of a single time series such as financial data
like a series of daily stock returns is called a univariate
time series which are a class of specifications where
one attempts to model and to predict financial vari-
ables using only information contained in their own
past values and possibly current and past values of an
error term.
rt = Φo + Φ1rt−1 + Φ2εt−1 + εt
εt ~ N(0,σ2
)
t
σ2 = α + α ε2t−1 + α2σ2
t
0
1
t−1
e GARCH model incorporating ARMA process, on
one hand, can eliminate the conditional heteroskedas-
ticity; on the other hand, it can be used to distinguish
different factors causing financial data fluctuation.
the term deriving from:
AR =autoregressive
I = integrated
MA = moving average.
The two-stage GARCH model
e paper employs the idea of the two-stage GARCH
transmissions of the mean and volatility the interna-
tional crude oil returns to the three Asian frontier oil
and gas company stock returns. e GARCH model
allows observing the “conditional” volatility of the
stock returns by accounting for volatility clustering
and leptokurtosis which are properties of the data.
e general ARIMA model is called an ARIMA(p, d,
q), with p being the number of lags of the dependent
variable (the AR terms), d being the number of dif-
ferences required to take in order to make the series
stationary, and q being the number of lagged terms of
the error term (the MA terms).
e ARMA (1, 1) model is the autoregressive of order
one model (p= 1), stationary time series (d= 0), and
the moving average of order one (q= 0), which has the In general, a GARCH(1,1) model would be sufficient to
form:
capture the volatility gathering in the data, and rarely
1538
Science & Technology Development Journal – Economics - Law and Management, 5(2):1535-1548
is any higher order model estimated or even entertained be reflected in the Banladeshi, Sri Lankan and Viet-
in the academic finance literature (Brooks1). e (1,1) namese stock markets until day t+1. As a result, the
in parentheses is a standard notation in which the appropriate pairing is time t–1 for the ICE stock mar-
first number refers to how many autoregressive lags, kets and time t for the Asian frontier markets.
or ARCH terms, appear in the equation, while the at is, our model is given by:
second number refers to how many moving average rt,i = Φo + Φ1rt−1,i + Φ2εt−1,i + λBRENT eBRENT,t−1
lags are specified, which is oꢀen called the number of + λWTIeWTI,t−1 + εt,i
GARCH terms. e conditional variance is a linear
ε )
t,i ~ N(0,σ2
t,i
function of 1 lag of the squares of the error terms (εt) σ2
=
α0
+
α1ε2
+
α2σ2
t−1,i
+
t,i
t−1,i
(also referred to as the “news” from the past) and 1 γBRENT e2BRENT,t−1 + γWTIe2
lag of the past values of the conditional variances (σt) Where eWTI,t−1 (eBRENT,t−1
WTI,t−1
)
and e2
WTI,t−1
or the GARCH terms, and a constant ω. erefore, (e2BRENT,t−1) are the residual and the square of
the model used in our research is the ARMA(1,1)- the residual for the Brent crude oil return and WTI
GARCH(1,1) and can be summarized as below.
crude oil return collected from equations (1) and
In the first stage, Brent and WTI returns are estimated (2). e coefficients λBRENT and λWTI capture the
through the following ARMA(1,1)-GARCH(1,1) mean spillover effect and the coefficients γLCOc1 and
model with the mean and variance equations:
γDJUSEN capture the volatility spillover effect from
the Brent crude oil return and WTI crude oil return.
On the other hand, a statistically significant value for
Φ1, α2 suggest the dependence on own-mean and
rt,i = Φo,i + Φ1rt−1,i + Φ2εt−1,i + εt,i (1)
ε
t,i ~ N(0,σ2
)
t,i
σ2t,i = α0,i + α1ε2t−1,i + α2σ2t−1,i (2)
Where rt is the daily stock index return; i represents own-volatility previous values.
for Brent and WTI; and εt is the residual which has
DATA
standard properties with mean zero and variance σ2.
e model specification assumes that the ICE stock
market returns are not affected by other markets, i.e.,
no international transmission exists. e residual εt
is the short-term fluctuation which expresses the un-
expected events, new information or innovation in
Brent and WTI returns and spreads to the twelve oil
Data employed in the thesis are daily adjusted closing
for Brent crude futures prices (LCOc1), WTI crude
futures prices (WTCLc1) and the twelve oil and gas
companies in three Asian frontier markets namely
Bangladesh, Sri Lanka and Vietnam over the sample
period from January 4, 2010 to December 31, 2019. It
& gas companies in the three Asian frontier markets is worth noting that in Bangladesh and Sri Lanka, oil
stock returns. Consequently, the residual series is em- and gas industry is managed largely by state-owned
ployed to capture the spillover effects from interna- corporations. To improve efficiency and develop this
tional petroleum markets to the three Asian frontier sector, government has gradually been showing ef-
petroleum markets. e larger the residuals are, the forts to restructure by privatising state enterprises
more likely they spread.
as well as loosening restrictions of regulation frame-
In the second stage, on the assumption that the twelve work. However, this transformation is running slow
oil & gas companies in the three Asian Frontier Mar- so that the number of publicly held companies is rel-
kets stock returns could be affected by information atively small. As listed below, these are all oil and gas
about volatility (ε) of Brent crude oil return and WTI companies available on Bangladeshi and Sri Lankan
crude oil return. Hence, the international transmis- stock markets.
sion from the ICE market to the three Asian frontier e data are retrieved from omson Reuters and ex-
markets could be existed in terms of the mean and pressed in local currencies with the only exception
volatility effects. To capture this, we use an appropri- of Brent and WTI crude oil futures prices, which are
ate ARMA(1,1)-GARCH(1,1) model for Banladeshi, denominated in USD per barrel. It is important to
Sri Lankan and Vietnamese oil company stock re- use high frequency data for any series modeled by
turns, where the mean and volatility equations include GARCH. As a result, daily data would be ideal to cap-
the residuals and residual squares obtained in the first ture most of the possible interactions.
stage GARCH model as exogenous variables. e For global crude oil price, this paper uses Brent and
residual is derived from equation (1) and its square WTI crude oil futures prices which are instruments
from equation (2).
of the two most popular grades of Crude Oil bench-
As shown in Table 2, the closing time for the ICE mar- marks. Brent and WTI contracts are traded through
ket is earlier than the three Asian frontier markets. ICE Futures Europe, ICE’s London-based, futures ex-
us, a shock in ICE stock marketduring day t will not change for the global energy markets. All ICE energy
1539
Science & Technology Development Journal – Economics - Law and Management, 5(2):1535-1548
products are cleared at ICE Clear Europe, ICE’s Lon- not be reflected in the Asian frontier stock markets
don based clearing house, which clears an average of until day t+1. us, the appropriate pairing is time t–
more than 3 million energy contracts every day. 1 for Brent and WTI returns and time t for the twelve
For the Bangladesh market, the five Oil & Gas compa- oil and gas company stock returns.
nies traded on Dhaka stock exchange are used. ey e unit of measurement for each market is trans-
are JAMUNAOIL (Jamuna Oil Company Limited), formed to a daily rate of return as below, which is de-
EMERALDOIL (Emerald Oil Industries Ltd.), PAD- fined as the natural logarithmic returns in two con-
MAOIL (Padma Oil Co. Ltd.), TITASGAS (Titas Gas secutive trading days:
Transmission & Dist. Co. Ltd.) and MPETROLEUM rt = ln(pt) - ln(pt−1 )= ln(pt / pt−1
)
(Meghna Petroleum Limited). Four of them (ex- Where rt is the daily log return, pt and pt−1 are the
cept for Mpetroleum) are the top fuel compaines with daily adjusted closing prices at time t and t-1.
maximum market capitalisation, market liquidity and e plots for the daily log returns fluctuate around a
fundamental stability at Bangladeshi stock exchange. zero mean (see Figure 1). Each of all series appears to
Regarding Sri Lanka’s market, the two Oil & Gas com- show the signs of ARCH effects in that the amplitude
panies traded on Colombo stock exchange are cho- of the returns varies over time. ese financial data
sen namely LAUGFS GAS PLC (LGL) and LANKA also exhibit “volatility clustering” or “volatility pool-
IOC PLC (LIOC). For the Vietnamese market, the ing”. Volatility clustering describes the tendency of
five oil & gas company stock prices traded on Ho large changes in asset prices (of either sign) to follow
Chi Minh Stock Exchange (HOSE) and Ha Noi Stock large changes and small changes (of either sign) to fol-
Exchange (HNX) are used. ey are major enter- low small changes. In other words, the current level of
prises with large market capitalization and cover all volatility tends to be positively correlated with its level
three types of activities known as the upstream, mid- during the immediately preceding periods. is phe-
stream, and downstream in the Vietnam’s oil and gas nomenon is demonstrated in Figure 1. As reflected
industry. ey are composed of PetroVietnam Gas from the time series data set of oil returns, all of the
Joint Stock Corporation (GAS, HOSE), Viet Nam Na- variables show significant volatility clustering with a
tional Petroleum Group (PLX, HOSE), PetroVietnam lot of abnormal spikes during 2010–2019, indicating
Technical Services Corporation (PVS, HNX), Petro- that the GARCH class model should also be used to
Vietnam Drilling & Well Services Corporation (PVD, describe their volatility process.
HOSE), Petrovietnam Fertilizer & Chemicals Cor- Table 2 and Table 3 present a wide range of descriptive
poration (DPM, HOSE). Upstream group includes statistics for the daily stock returns of Bent crude oil
PVS and PVD. ey are enterprises invested with futures, WTI crude oil futures and the twelve oil stock
large capital and considerably affected by interna- prices from January 2010 to December 2019.
tional oil price fluctuation. GAS belongs to mid- According to Table 2 and Table 3, out of twelve com-
stream and plays transporting and distributing roles, pany stock returns, seven display negative daily re-
whereas PLX and DPM represent downstream group. turns, more specifically, four out of five companies
e more upstream the enterprise, the greater the ef- in Bangladesh, half of companies in Sri Lanka and
fect of oil price on its operating income.
two out of five companies in Vietnam. While for
e number of observations is approximately 5160 Brent and WTI returns, the mean returns are nega-
for crude oil market, 4586 for Sri Lanka, 8320 for tive at -0,00007 and -0,00011, respectively. e high-
Bangladesh and 9876 for Vietnam. e paper analyzes est daily mean return is posted by GAS in Vietnam
the exogenous effects of Brent and WTI returns and (0,00065), while the lowest average daily return comes
volatilities on the twelve oil company stock returns from TITAS in Sri Lanka (-0,00037). e volatility
and volatilities.
represented by standard deviation of Brent crude oil
e stock indices and their home countries are pre- returns WTI crude oil returns are lower than almost
sented in Table 1. Also presented are their trading all oil companies. EMOI in Bangladesh has the high-
hours in both local and UTC time for the purpose of est standard deviation (0.038720), whereas Brent has
studying the same effects. Each stock market oper- lowest (0,01903). e high degrees of kurtosis reveal
ates in different time zones with different opening and a fat-tailed distribution of both returns and the skew-
closing times, so that the daily rates of return repre- ness coefficient is different from zero, indicating the
sent the returns in different real time periods. As can rejection of the normality condition for the data se-
be seen from the table (Trading-UTC column), the ries.
ICE market closes later than the Asian frontier mar- Strong evidence of autocorrelations and conditional
kets; therefore, a shock in this market during day t will heteroskedasticity for both markets is provided by
1540
Science & Technology Development Journal – Economics - Law and Management, 5(2):1535-1548
Table 1: Indices, home countries, time-zones and trading hours in local and UTC time
Index
Country
Time-
Zone
Trading – local time
Trading - UTC
Open
1:00
Close
23:00
23:00
14:30
Open
1:00
Close
BRENT
WTI
e U.K.
e U.K.
UTC
UTC
23:00
23:00
08:30
1:00
1:00
Bangladesh oil and gas stock Bangladesh UTC+6
prices
10:30
04:30
Sri Lanka oil and gas stock prices
Sri Lanka
UTC+5,5 09:30
UTC+7 9:00
14:30
15:00
04:00
2:00
09:00
8:00
Vietnamese oil and gas stock Vietnam
prices
Figure 1: The daily returns of indices
the Ljung-Box (LB) statistic for lags 12 and 24 for sider the (Generalized Autoregressive Conditional
returns as well as squared returns. e Ljung-Box Heteroskedasticity) GARCH type models that can ac-
(LB) Q statistics for daily stock returns of several commodate time-varying and persistent behavior of
assets namely MEGP, JOCL, PDOC, TITA in the volatility of returns. Besides, the p-value of Arch Test
Bangladeshi stock market are highly significant at shown in the last row are all zero to both places, re-
five-percent level suggesting that the residuals are se- soundingly rejecting the “no ARCH” hypothesis.
rially correlated. Furthermore, the presence of highly
significant autocorrelations in the squared series indi-
cates the time-varying volatility.
Furthermore, the presence of serial correlations and
time-varying volatility make the traditional OLS re-
gression inefficient. ese features of the data lead
e significant autocorrelation among squared re- us to consider the GARCH type models that can
turns and excess kurtosis are compatible with the accommodate time-varying and persistent behavior
volatility clustering phenomenon that lead to con- of volatility of returns. We start modeling with
1541
Science & Technology Development Journal – Economics - Law and Management, 5(2):1535-1548
Table 2: Descriptive statistics for daily stock returns of the oil and the Vietnamese markets
BRENT
-0,00007
0,13639
-0,08963
0,01903
0,10734
6,67998
15,409
0,118
WTI
GAS
0,00065
0,0675
-0,0725
0,0214
-0,0486
4,9914
19
DPM
PLX
0,0004
0,0675
-0,0723
0,022
-0,056
4,787
7
PVS
0,00017
0,0953
-0,015
0,025
-0,1067
5,324
8
PVD
-0,00035
0,0677
-0,073
0,024
0,042
3,758
19
Mean
-0,00011
0,13694
-0,0907
0,02049
0,19909
6,48738
10,609
0,389
-0,00002
0,08483
-0,11093
0,01825
0,02177
5,69237
19,222
0,038
Max
Min
Std,dev
Skewness
Kurtosis
LB(12)
0,08
0,8
0,7
0,06
38
LB(24)
23,466
0,376
24,455
0,32
38
35,341
0,036
17
19
0,02
0,8
0,7
0,03
445
LB2(12)
LB2(24)
520,99
0,0
512,5
835
334,54
0,0
279
568
0,0
0,0
0,0
0,0
0,0
756,12
0,0
794,96
0,0
1007
0,0
450,25
0,0
395
686
527
0,0
0,0
0,0
ARCH
235,47
0,0
225,433
0,0
318
403
116
181,47
0,0
203
Test (12)
0,0
0,0
0,0
0,0
Notes: GAS is PetroVietnam Gas Joint Stock Corporation
DPM is Petrovietnam Fertilizer & Chemicals Corporation
PLX is Viet Nam National Petroleum Group
PVS is PetroVietnam Technical Services Corporation
PVD is PetroVietnam Technical Services Corporation
ARMA(1,1)-GARCH(1,1).
(PLX, DPM, LIOC, LGGL). Coefficients Φ1 of DPM
and LGGL are positive and significant at 1%, suggest-
ing that these stock returns today are affected by stock
returns of the previous day. e negative and signif-
icant coefficients for the others indicate that there is
no impact of return on the previous day on the today
return. Current returns on the international oil fu-
tures market are also insignificantly affected by their
past values. Likewise, coefficients Φ2 are found not
to have significant explanatory power on their current
values except for PLX and WTI returns. is shows
that current values do not depend on the value of the
immediate past error.
Taking a close look at mean equations of Brent and
WTI futures price returns, as can be seen, current
Brent crude oil return is not affected by its own one-
lagged return as well as short-term fluctuation which
expresses the unexpected events, new information in
the preceding day. It can be obviously explained that
the price of crude oil is traditionally determined by
EMPIRICAL RESULTS AND
DISCUSSION
Empirical models for the twelve oil & gas companies
in the three Asian frontier stock returns are given be-
low:
rt,i = Φo + Φ1rt−1,i + Φ2εt−1,i + λBRENT eBRENT,t−1
+ λWTIeWTI,t−1 + εt,i
ε
t,i ~ N(0,σ2
)
t,i
σ2
=
α0
+
α1ε2
+
α2σ2
t−1,i
+
t,i
t−1,i
γBRENT e2BRENT,t−1 + γWTIe2
WTI,t−1
Tables 4, 5 and 6 show the estimation results of two-
stage ARMA-GARCH model for the twelve pairs of
oil- stock market returns in the three Asian fron-
tier markets; Sri Lanka, Bangladesh and Vietnam, to-
gether with statistical tests applied to standardized
residuals.
e results for the conditional mean equations show
none of the autoregressive terms denoted by Φ1 co-
efficients in the return-generating process for stock supply and demand for itself. Moreover, as a deriva-
markets is insignificant except for the four oil and tive market which is based on underlying asset, oil fu-
gas company stock returns in Sri Lanka and Vietnam tures market prices also rely on a continuous flow of
1542
Science & Technology Development Journal – Economics - Law and Management, 5(2):1535-1548
Table 3: Descriptive statistics for daily stock returns of the Bangladeshi and the Sri Lanka markets
LIOC
0,00006
0,15751
-0,12282
0,02205
0,78881
9,21128
9,6149
0,475
LGGL
-0,00019
0,19845
-0,25172
0,02718
0,79982
13,48646
12,403
0,155
MEGP
0,00027
0,16413
-0,22816
0,02331
-0,06119
16,01569
25,116
0,005
JOCL
PDOC
-0,00004
0,16356
-0,32406
0,025064
-1,065930
30,07906
20,045
0,029
TITA
EMOI
-0,0011
0,18232
-0,20972
0,038720
0,293496
6,618004
10,176
0,425
Mean
-0,00015
0,15952
-0,19047
0,02297
-0,16342
13,51456
18,095
0,053
-0,00037
0,1366
-0,16343
0,021854
-0,097159
10,06254
19,155
0,038
Max
Min
Std,dev
Skewness
Kurtosis
LB(12)
LB(24)
18,983
0,646
21
44,044
0,002
40,137
0,01
64,905
0,00
36,739
0,025
27,848
0,181
0,521
LB2(12)
LB2(24)
66,7998
0,0
291,21
0,0
214,57
0,0
335,24
0,0
256,45
0,0
278,65
0,0
160,2
0,0
795
366,08
0,0
352,64
0,0
530,18
0,0
371,18
0,0
387,05
0,0
201,23
0,0
0,0
ARCH
235
302
133,68
0,0
189,54
0,0
169,13
0,0
174,29
0,0
83,39
Test (12)
0,0
0,0
0,0
Notes: LIOC is LANKA IOC PLC based in Sri Lanka
LGGL is LAUGFS GAS PLC based in Sri Lanka
MEGP is Meghna Petroleum Limited based in Bangladesh
JOCL is Jamuna Oil Company Limited based in Bangladesh
PDOC is Padma Oil Company Limited based in Bangladesh
TITA is Titas Gas Transmission & Distribution Company Limited based in Bangladesh
EMOI is Emerald Oil Industries Ltd. based in Bangladesh
asset. e information such as political, geopolitical Bangladesh, it is noticeable that none of the spillover
tensions and unforeseen events in the oil-producing effects is statically significant. is indicates that
countries is absorbed and reflected in futures prices Bangladesh market behaves independently from the
quickly. erefore, this characteristic may cause wild international oil markets. However, the LB Q-
fluctuations in crude oil returns. Last but not least, statistics test shows the model does not fit well.
speculation of investors in their expectation of oil Regarding the results of the estimation for Sri Lanka
market partly determines crude oil futures prices. showed in Table 5, the LB Q-statistics confirms that
However, these factors cannot be so easily explained the model fits the data well since there is no serial
and require further study for explanation. Likewise, correlation in the residual series. It is clear that no
current WTI return is not affected by its own one- evidence of mean spillover is observed from the in-
lagged return but affected by short-term fluctuation in ternational oil markets to oil and gas stock market in
the day before compared to Brent return. e reason Sri Lanka since the coefficients λWTI and λBRENT are
here appears because of more commonly referenced insignificant. is seems to suggest that a shock orig-
Brent price benchmark which means the more crude inating from the oil markets generally has no impact
oil storage Brent has, the less dependence on the past on stock market returns in the Sri Lankan market. In
shocks Brent does than WTI.
terms of the volatility spillover, there is only a positive
As for the estimates of ARCH and GARCH coeffi- significant volatility spillover effects between WTI oil
cients, which capture shock dependence and volatility market and the Sri Lankan oil and gas stock market.
persistence in the conditional variance equations are It means that the conditional variances of the two oil
highly significant for all oil and stock return series at and gas companies in Sri Lanka are affected by inno-
the 1% level.
vations of WTI crude oil returns. e effect however
1543
Science & Technology Development Journal – Economics - Law and Management, 5(2):1535-1548
is weak and significant only at 10% for LIOC. While by a state-owned national energy utility with a legally
the impact from Brent oil returns is absent.
endowed monopoly and an integrated supply chain.
e rationale for this structure partly appears to min-
imize the impact of international market shocks. Be-
cause of the inefficiency of this structure, many devel-
oping countries have embarked on energy sector re-
forms by privatizing several state entities. In general,
this restructure have been implemented in an attempt
to ensure increased efficiency, transparency, auton-
omy, accountability, competition, and financial via-
bility. However, each country has been experienced
a different degree of transformation. erefore, the
higher the level of privatization, the less the extent of
government interference, the more the price fluctua-
tion of listed companies. e other reason is role of
crude oil in a country. Almost all Asian countries
are importers of crude oil and petroleum products.
erefore, it is clear that demand for oil in the Asian
frontier countries contributes to the level of depen-
dence on the global oil market.
In Bangladesh, none of the spillover effects from Brent
or WTI market is statically significant. is can be ex-
plained due to the low level of privatization in the en-
ergy sector which is dominated by state-owned com-
panies, as well as negligible role of oil in Bangladesh.
In other words, there are only five oil and gas pub-
lic companies in the Dhaka Stock Exchange and do-
mestic coal and natural gas are primary energy supply
sources.
In like manner, the evidence of volatility spillover is
observed from the WTI oil market to oil and gas stock
market in Sri Lanka, but the effect is weak and small.
is is perhaps due to the slow transformation of the
energy sector reform and the relatively high country’s
petroleum requirement in this country. In particular,
there are only two oil and gas listed companies and Sri
Lanka’s oil import bill accounts for an estimated 27%
of total imports.
e magnitude of the spillover effects from the inter-
national crude oil returns to the Vietnamese market is
the largest among the Asian frontier countries. Possi-
ble reasons are that this market has the highest degree
of privatization in oil and gas industry as well as de-
mand for crude oil and petroleum products.
namese market, these results are opposite to those for
the Sri Lanka stock market due to the emergence of
mean spillover from the conditional mean equations
and the absence of volatility spillover from the con-
ditional volatility equations of the five Vietnamese oil
and gas company stock returns. Regarding the extent
of mean transmission between oil and stock markets,
the results show that returns on the Brent (λBrent) sig-
nificantly affect stock market returns in four out of
five companies (GAS, PLX, PVS, PVD), while returns
on the WTI (λWTI) are significant in three out of five
(GAS, PVS, PVD), indicating that a high return in the
two those markets are followed by high returns in sev-
eral Vietnamese oil and gas companies. e magni-
tude of the mean spillover effects from the interna-
tional crude oil returns to these influenced compa-
nies is as expected that the closer upstream the en-
terprise, the more it is affected. On the other hand,
oil’s shocks, represented by γBrent and γWTI from the
conditional volatility equations have no significant ef-
fects on the stock market. is finding suggests that
there are no cross-volatility spillover effects between
oil and the Vietnamese market. As noticeable, port-
manteau LB statistics evaluate the serial correlations
in the raw and squared standardized residuals of the
model up to lags 5 and 9 and find that most of the con-
ditional dependence in the return has been modeled
reasonably well.
In general, the volatility of the Asian frontier mar-
kets is mainly explained by their own volatility rather
than oil’s. As can be observed, the estimates of ARCH
and GARCH parameters are all highly significant and
the sum of these two coefficients is close to unity, es-
pecially the two markets in Sri Lanka and Vietnam.
Specifically, the estimated conditional volatility series
do not change very rapidly under the fluctuation of re-
turn innovations given the small size of ARCH coeffi-
cients. ey tend instead to evolve gradually over time
with respect to substantial effects of past volatility as
indicated by the large values of GARCH coefficients.
Taking these results into account might help investors
to better forecast future stock volatility as well as di-
versify their portfolio investment.
It seems that there are two main reasons for difference
in spillover effects of the international crude oil mar-
kets between the Asian frontier markets. Firstly, the
difference is based on the level of privatization and the
extent of government interference (supporting price)
in the energy sector of each market. In many devel-
oping countries, particularly in Asia, energy sector re-
CONCLUSION
is paper has investigated the transmission of mean
return and volatility from the international crude oil
returns to the three Asian frontier oil and gas com-
pany stock returns, using daily data from January
2010 to December 2019. ese developing countries
form starts from a market structure that is dominated have increased their economic integration in recent
1544
Science & Technology Development Journal – Economics - Law and Management, 5(2):1535-1548
Table 4: Empirical results for Stock Returns in Bangladesh
MEGP
JOCL
PDOC
TITA
EMOI
Conditional Mean Estimates
Φ0
-0,00032
0,01548
0,01558
-0,01285
0,04679
-0,00018
0,00816
0,008133
-0,03299
0,04849
0,00002
0,00109*
-0,43742
0,48336
-0,18104
0,16128
-0,00078
-0,027
-0,029
0,061
Φ1
0,016855
0,016844
0,068273
-0,07260
Φ2
λBRENT
λWTI
0,039
Conditional Variance Estimates
α0
0,00049***
0,13105***
0,57251***
-0,03261
0,00047***
0,13900***
0,56884***
-0,03127
0,00058***
0,14220***
0,58396***
-0,04091
0,00029***
0,09381***
0,33882***
-0,02311
0,00010***
0,16921***
0,76525***
0,05471
α1
α2
γBRENT
γWTI
-0,03087
-0,03052
-0,04038
-0,00055
-0,02872
LB Q-statistics
LB(5)
LB(9)
LB2(5)
LB2(9)
10,249**
11,617
0,8713
5,1
5,4435
10,021**
11,953*
1,8988
5,338
7,7393
76,551**
107,18***
79,637***
150,78***
18,538***
36,915***
75,027***
127,69***
2,4867
4,1742
Parentheses include the p-value, *, ** and *** indicate significance at 10, 5 and 1% levels, respectively.
years, and their stock markets have achieved remark- sification benefits due to low correlations with devel-
able development. By adopting a two- stage GARCH oped equity markets.
construct mean return and volatility spillover mod-
els to discuss whether regional (DSE, CSE, HNX,
HOSE) and global (ICE) market impacts are crucial
for the determination of oil & gas stock returns in
Bangladesh, Sri Lanka and Vietnam. e findings of
this paper show that, the Brent oil and WTI crude
ese results are important for economic policy mak-
ers in order to safeguard the energy sector from in-
ternational oil shocks. e investors can use this in-
formation for making better portfolio allocation deci-
sions to reduce risks and enhance returns. It also may
be helpful for academics, domestic policy makers, and
financial participants understanding the magnitude
oil markets influence the Sri Lanka and Vietnamese of volatility spillover effects of the international crude
oil and gas stock market. e results also support oil on these markets. Moreover, this study contributes
significant feedback relationships in mean return be- to the growing literature on the spillover effects and
tween Brent, WTI crude oil stock returns and com- volatility transmission of equity returns.
panies in the Vietnamese. Meanwhile, WTI price
changes have an influence on volatility of Sri Lanka
companies however it is weak and has small magni-
tude. For Bangladesh, it is noticeable that none of
the spillover effects is statically significant. is indi-
cates that Bangladesh market behaves independently
from the international oil markets. e results are ex-
However, recent trends of international capital inte-
gration, including the existence of multinational fi-
nancial institutions, could intensify the other way.
us, careful examination on such mutual interde-
pendence among capital markets must be needed in
future research. Further research is necessary for
investigating the mean and volatility transmission
plained by different levels of reform process in energy through multivariate GARCH (M-GARCH) models.
sector as well as the importance of oil in these mar- e ability of capturing cross-market spillovers in-
kets. In general, these frontier markets, especially the creases with MGARCH specification because of its
Bangladesh and Sri Lanka may offer promising diver- advantages.
1545
Science & Technology Development Journal – Economics - Law and Management, 5(2):1535-1548
Table 5: Empirical results for Oil & Stock Returns in Sri Lanka
BRENT
Conditional Mean Estimates
WTI
LIOC
LGGL
Φ0
0,00019
-0,04421
-0,00358
0,00015
-0,00043
-0,32734*
0,22221
0,02218
-0,03071
-0,00052
0,32427***
-0,45439**
0,05499
Φ1
-0,69891***
0,65491***
Φ2
λBRENT
λWTI
-0,05299
Conditional Variance Estimates
α0
0,00000***
0,06165***
0,93193***
0,00000***
0,06520***
0,92588***
0,00011***
0,14652***
0,63780***
-0,04205***
0,02831*
0,00006***
0,14652***
0,76037***
-0,06937***
0,05428***
α1
α2
γBRENT
γWTI
LB Q-statistics
LB(5)
4,8766
9,2327
5,5829
7,1387
1,9834
3,327
3,0224
8,1805
1,1898
2,6783
6,6328
13,232
0,5509
0,9057
LB(9)
LB2(5)
LB2(9)
5,6104
8,5478
Parentheses include the p-value, *, ** and *** indicate significance at 10, 5 and 1% levels, respectively.
is research is funded by Vietnam National Uni- the paper.
versity HoChiMinh City (VNU-HCM) under grant
REFERENCES
1. Brooks C. Introductory econometrics for finance. Cambridge
number C2019-34-09
ABBREVIATIONS
2. Pan MS, Paul H.
Transmission of Stock Returns and
ARCH: Autoregressive Conditionally Heteroscedastic
ARMA: Autoregressive–Moving-Average
GARCH: Generalized Autoregressive Conditionally
Heteroscedastic
Volatility between the U.S. and Japan: Evidence from the
Stock Index Futures Markets. Asia-Pacific Financial Markets.
1998;5(3):211–225.
3. Mervyn. A King and Sushil Wadhwani. Transmission of
Volatility between Stock Markets, Review of Financial Studies.
1.5.
4. Mork KA. Business Cycles and the Oil Market. The Energy
5. Jones CM, Kaul G. Oil and the stock markets. The Journal of
6. Sadorsky P. Oil price shocks and stock market activity. Energy
7. Papapetrou E. Oil price shocks, stock market, economic activ-
ity and employment in Greece. 2001;23(5):511–532. Available
8. Nandha M, Faff R. Does oil move equity prices? A global view.
Energy Economics. 2008;30(3):986–997. Available from: https:
9. Chen SS. Do higher oil prices push the stock market into bear
territory? Energy Economics. 2010;32(2):490–495. Available
LB: Ljung-Box
COMPETING INTERESTS
e authors declare that they have no conflicts of in-
terest.
AUTHORS’ CONTRIBUTIONS
e research is conducted by Nguyen i Ngan,
Hoang Trung Nghia and Truong Huynh uy Vi, in
which Nguyen i Ngan and Hoang Trung Nghia
are mainly responsible for this research. Nguyen i
Ngan and Hoang Trung Nghia are responsible for
conceiving and designing the analysis, contributing
data and analysis tools, performing the analysis and
writing the paper. Truong Huynh uy Vi is respon-
sible for collecting data; interpreting data and writing
1546
Science & Technology Development Journal – Economics - Law and Management, 5(2):1535-1548
Table 6: Empirical results for Stock Returns in Vietnam
GAS
PLX
DPM
PVS
PVD
Conditional Mean Estimates
Φ0
0,00075*
0,00000
0,00008
-0,00005
0,05258
-0,00025
-0,11198
0,14670
Φ1
-0,26312
0,28544
-0,98064**
0,99438**
0,23421*
-0,10979
0,90778***
-0,92512***
0,02501
Φ2
-0,09296
0,14817***
0,09897***
λBRENT
λWTI
0,11816**
0,12394**
0,07844*
0,16251***
-0,01761
Conditional Variance Estimates
α0
0,00002***
0,11961***
0,83641***
-0,01187
0,000**
0,00000***
0,15267***
0,82853***
-0,01025
0,00002***
0,12451***
0,84728***
0,00726
0,00005***
0,11991***
0,79016***
0,01380
α1
0,07781***
0,90335***
0,00353
α2
γBRENT
γWTI
LB Q-statistics
LB(5)
LB(9)
LB2(5)
LB2(9)
0,01196
-0,00481
0,01180
-0,00757
-0,01319
0,6397
3,5621
8,4067**
9,1779
2,9898
6,4087
4,1352
8,7634
6,1566
9,5561
4,2990
8,4638
8,9765**
10,834
4,0163
5,4861
1,8148
5,5039
2,4517
10,081
Parentheses include the p-value, *, ** and *** indicate significance at 10, 5 and 1% levels, respectively.
10. Kilian L, Park C. The impact of oil price shocks on the U.S. stock
market, International Economic Review. 2009;50(4):1267–
11. Malik F, Ewing BT. Volatility transmission between oil prices
and equity sector returns. International Review of Financial
12. Vo M. Oil and stock market volatility: A multivariate stochastic
volatility perspective. Energy Economics. 2011;33(5):956–965.
13. Arouri MEH, et al. The volatility spillovers between oil and
stock marketsin Europe, EnergyEconomics. 2012;34:611–617.
14. DegiannakisS, FilisG, KizysR. TheEffectsofOilPriceShockson
Stock Market Volatility: Evidence from European Data. The En-
15. Apergis N, Miller SM. Do structural oil-market shocks affect
stock prices?, Energy Economy. 2009;31:569–575. Available
16. Gomes M, Chaibi A. Volatility Spillovers Between Oil Prices
And Stock Returns: A Focus On Frontier Markets. Journal of
Applied Business Research (JABR). 2014;30(2):509–526. Avail-
17. Trinh PTT, Dan VL. The asymmetric impact of the oil price fluc-
tuation on the Vietnamese stock market: employ non-linear
autoregressive distributed lag model (ARDL), Journal of Asian
Business and Economic Studies. 2018;29(9):36–52.
18. Box GEP, George GM. Time Series Analysis: Forecasting and
Control. 1970;.
19. Liu YA, Pan MS. Mean and volatility spillover effects in the US
and Pacific-Basin stock markets. Multinational Finance Jour-
nal. 1997;1:47–62. Available from: https://doi.org/10.17578/1-
1-3.
1547
Tạp chí Phát triển Khoa học và Công nghệ – Kinh tế-Luật và Quản lý, 5(2):1535-1548
Bài nghiên cứu
Open Access Full Text Article
Tác động của thị trường dầu thô quốc tế đến tỷ suất lợi nhuận của
các công ty dầu khí ở các nước cận biên khu vực châu Á
Nguyễn Thị Ngân1,*, Hoàng Trung Nghĩa1, Trương Huỳnh Thúy Vi2
TÓM TẮT
Các thị trường cận biên giới khu vực châu Á mang đến cơ hội đầu tư hấp dẫn cho các nhà đầu tư
nhằm tìm kiếm lợi nhuận cao hơn và mối tương quan thấp so với các tài sản truyền thống. Việc
Use your smartphone to scan this
QR code and download this article
nghiên cứu cơ chế truyền dẫn sự biến động giữa các thị trường này có vai trò quan trọng trong
việc đưa ra quyết định phân bổ danh mục đầu tư. Bài báo xem xét mức độ lan tỏa của tỷ suất lợi
nhuận và độ biến động từ thị trường dầu thô quốc tế tác động đến thị trường chứng khoán dầu
khí ở các nước cận biên khu vực châu Á. Ứng dụng mô hình ARMA (1,1)-GARCH (1,1), bài viết xây
dựng mô hình lan truyền trong tỷ suất lợi nhuận và độ biến động nhằm đánh giá tác động của
thị trường khu vực (DSE, CSE, HNX, HOSE) và thị trường quốc tế (ICE) đến tỷ suất lợi nhuận của các
công ty dầu khí ở Bangladesh, Sri Lanka và Việt Nam. Sử dụng tỷ suất lợi nhuận hàng ngày trong
giai đoạn từ 04/01/2010 đến 31/12/2019, bài nghiên cứu cho thấy thị trường dầu Brent và WTI có
ảnh hưởng đến thị trường chứng khoán dầu khí ở Việt Nam và Sri Lanka. Tuy nhiên, mức độ ảnh
hưởng của WTI đến độ biến động của các công ty ở Sri Lanka không đáng kể. Đối với thị trường
Bangladesh, điều đáng chú ý là không tìm thấy tác động lan truyền có ý nghĩa thống kê. Kết quả
có thể được giải thích bởi sự khác nhau trong tiến trình cải cách khu vực năng lượng cũng như tầm
quan trọng của thị trường dầu ở những quốc gia này. Kết quả nghiên cứu cho thấy các thị trường
cận biên, cụ thể là Bangladesh và Sri Lanka có tiềm năng mang đến lợi ích đa dạng hóa bởi mức độ
tương quan với các thị trường phát triển thấp. Bài báo góp phần cung cấp thêm thông tin cho các
nhà hoạch định chính sách, nhà kinh tế trong việc đánh giá mức độ lan truyền của thị trường dầu
thô quốc tế đến thị trường các nước cận biên. Các nhà đầu tư có thể tận dụng lợi thế của chiến
lược đa dạng hóa ở những thị trường cận biên trong việc tối đa hóa lợi nhuận đầu tư.
Từ khoá: Lan truyền, tác động độ biến động, hợp đồng tương lai dầu thô, tỷ suất lợi nhuận các
công ty dầu khí, thị trường cận biên khu vực châu Á
1Trường Đại học Kinh tế - Luật, Đại học
Quốc gia TP.HCM, Việt Nam
2CTCP Chứng khoán SSI, Việt Nam
Liên hệ
Nguyễn Thị Ngân, Trường Đại học Kinh tế -
Luật, Đại học Quốc gia TP.HCM, Việt Nam
Email: ngannt@uel.edu.vn
Lịch sử
• Ngày nhận: 29-12-2021
• Ngày chấp nhận: 13-5-2021
• Ngày đăng: 18-5-2021
DOI : 10.32508/stdjelm.v5i2.749
Bản quyền
© ĐHQG Tp.HCM. Đây là bài báo công bố
mở được phát hành theo các điều khoản của
the Creative Commons Attribution 4.0
International license.
Trích dẫn bài báo này: Ngân N T, Nghĩa H T, Vi T H T. Tác động của thị trường dầu thô quốc tế đến tỷ
suất lợi nhuận của các công ty dầu khí ở các nước cận biên khu vực châu Á. Sci. Tech. Dev. J. - Eco.
Law Manag.; 5(2):1535-1548.
1548
14 trang
04/04/2022
6040
Bạn đang xem tài liệu "The effects from the international crude oil returns to Asian frontier oil and gas company stock returns", để tải tài liệu gốc về máy hãy click vào nút Download ở trên
File đính kèm:
- the_effects_from_the_international_crude_oil_returns_to_asia.pdf
