Volatility in stock return series of vietnam stock market

MINISTRY OF EDUCATION AND TRAINING
UNIVERSITY OF ECONOMICS HOCHIMINH CITY
--- oOo ---

NGUYỄN THỊ KIM NGÂN

VOLATILITY IN STOCK RETURN SERIES
OF
VIETNAM STOCK MARKET

MASTER THESIS

Ho Chi Minh City – 2011


MINISTRY OF EDUCATION AND TRAINING
UNIVERSITY OF ECONOMICS HOCHIMINH CITY
----------o0o---------

NGUYỄN THỊ KIM NGÂN

VOLATILITY IN STOCK RETURN SERIES
OF
VIETNAM STOCK MARKET

MAJOR: BANKING AND FINANCE
MAJOR CODE: 60.31.12
MASTER THESIS
INSTRUCTOR: Dr. VÕ XUÂN VINH

Ho Chi Minh City – 2011

ACKNOWLEDGEMENT
At first, I would like to show my sincerest gratitude to my supervisor, Dr. Vo Xuan
Vinh, for his valuable time and enthusiasm. His whole-hearted guidance,
encouragement and strong support during the time from the initial to the final phase
are the large motivation for me to complete my thesis.
I also would like to thank all of my lecturers at Faculty of Banking and Finance,
University of Economics Hochiminh City for their English program, knowledge and
teaching during my master course at school.
In addition, my thanks also go to my beloved family for creating good and
convenient conditions for me throughout all my studies at University as well as
helping me overcome all the obstacles to finish this thesis.
Lastly, I offer my regards and blessings to all of those who supported me in any
respects during the completion of the study.

i


ABSTRACT
This thesis studies the features of the stock return volatility and the presence of
structural breaks in return variance of VNIndex in the Vietnam stock market by
using the iterated cumulative sums of squares (ICSS) algorithm. The relationship
between Vietnam stock market’s volatility shifts and impacts of global crisis is also
detected. Using a long-span data, the results show that daily stock returns can be
characterized by GARCH and GARCH in mean (GARCH-M) models while
threshold GARCH (T-GARCH) is not suitable. About structural breaks, when
applying ICSS to the standardized residuals filtered from GARCH (1, 1) model, the
number of sudden jumps significantly decreases in comparison with the raw return
series. Events corresponding to those breaks and altering the volatility pattern of

stock return are found to be country-specific. Not any shifts are found during global
crisis period. In addition, because the research is not able to point out exactly what
events caused sudden changes, the analysis on relationship between these
information and shifts is just in relative meaning. Further evidence also reveals that
when sudden shifts are taken into account in the GARCH models, reduction in the
volatility persistence is found. It suggests that many previous studies may have
overestimated the degree of volatility persistence existing in financial time series.
The small value of coefficients of the dummies representing breakpoints in
modified GARCH model implies that the conditional variance of stock return is
much affected by past trend of observed shocks and variance.
Our results have important implications regarding advising investors on decisions
concerning pricing equity, portfolio investment and management, hedging and
forecasting. Moreover, it is also helpful for policy-makers in making and
promulgating the financial policies.

ii


TABLE OF CONTENTS
ACKNOWLEDGEMENT..................................................................................................... i
ABSTRACT.......................................................................................................................... ii
TABLE OF CONTENTS .................................................................................................... iii
LIST OF FIGURES.............................................................................................................. v
LIST OF TABLES............................................................................................................... vi
ABBREVIATIONS ............................................................................................................ vii
1: INTRODUCTION............................................................................................................ 1
2: LITERATURE REVIEW ................................................................................................ 5
2.1. Common characteristics of return series in the stock market ........................................ 5
2.2. Volatility models suitable to the stock return characteristics ........................................ 6
2.3. Identification of breakpoints in volatilities and influence of the regime changes........... 7

2.4. Events related to regime changes ................................................................................. 9
2.5. Sudden changes in economic recession? .................................................................... 10
2.6. Overstatement of ICSS algorithm in raw returns series .............................................. 10
3: HYPOTHESES .............................................................................................................. 12
4: RESEARCH METHODS............................................................................................... 13
4.1. Stationarity ................................................................................................................ 13
4.2. Testing for stationarity............................................................................................... 14
4.2.1. Autocorrelation diagram ..................................................................................... 14
4.2.2. Unit root test ....................................................................................................... 15
4.3. GARCH model ........................................................................................................... 16
4.3.1. ARMA ................................................................................................................. 16
4.3.1.1. Moving average processes - MA(q) .............................................................. 17
4.3.1.2. Autoregressive processes - AR(p) ................................................................. 17
4.3.1.3. ARMA processes........................................................................................... 18
4.3.1.4. Information criteria for ARMA model selection ............................................ 19
4.3.2. ARCH & GARCH Model ..................................................................................... 20
4.3.2.1. ARCH Model................................................................................................ 20
4.3.2.2. GARCH Model ............................................................................................. 21
4.4. TGARCH Model ........................................................................................................ 22
4.5. GARCH-M model ...................................................................................................... 23

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4.6. ICSS algorithm .......................................................................................................... 24
4.7. Combination of GARCH model and sudden changes .................................................. 26
5: DATA AND EMPIRICAL RESULTS .......................................................................... 27
5.1. Data .......................................................................................................................... 27
5.2. Empirical results........................................................................................................ 29
5.2.1. Suitable models for stock return series of Vietnam. ............................................. 29

5.2.1.1. Choosing suitable ARMA model ................................................................... 29
5.2.1.2. Test for ARCH effect..................................................................................... 30
5.2.1.3. GARCH models ............................................................................................ 31
5.2.2. Identification of break points and detection of related events .............................. 33
5.2.2.1. Breakpoints in raw returns ........................................................................... 33
5.2.2.2. Breakpoints in filtered returns ...................................................................... 38
5.2.2.3. Analysis of each volatility period .................................................................. 44
5.2.2.4. General comments on events and volatility corresponding to sudden
changes detected by ICSS algorithm ......................................................................... 57
5.2.3. Combined model after including dummies ........................................................... 57
6: CONCLUSION .............................................................................................................. 60
Implications of the research.............................................................................................. 60
Limitations of the study..................................................................................................... 61
REFERENCE ..................................................................................................................... 62
APPENDIX......................................................................................................................... 66
Table A1. Descriptive statistics of Vietnam stock market’s daily stock return.................... 66
Table A2. Correlogram and Q-statistic of VNIndex daily rate of return ............................ 67
Table A3. Unit Root Test on VNIndex’s daily return ......................................................... 68
Table A4. Summary for estimation results of all ARMA models......................................... 69
Table A5. Statistically significant ARMA models with C constants .................................... 70
Table A6. Statistically significant ARMA models without C constants ............................... 72
Table A7. Estimation results of GARCH models ............................................................... 74
Table A8. Estimation results of GARCH-M models ........................................................... 77
Table A9. Estimation result of TGARCH model ................................................................ 79
Table A10. Estimation result of GARCH model modified with sudden changes ................. 80
Table A11. ICSS code on WINRAT ................................................................................... 81

iv

LIST OF FIGURES

Figure 5.1. Daily return series on HOSE .............................................................................. 29
Figure 5.2. Structural breakpoints in volatility in raw returns .............................................. 38
Figure 5.3. Structural breakpoints in volatility in filtered returns ......................................... 39

v


LIST OF TABLES

Table 5.1. Descriptive statistics of Vietnam stock market’s daily return series ...................... 27
Table 5.2. Unit Root Test on VNIndex’s daily return............................................................ 28
Table 5.3. Empirical results of different ARMA models......................................................... 30
Table 5.4. ARCH effect at 7th lag .......................................................................................... 31
Table 5.5. Empirical results of different GARCH-family models ........................................... 32
Table 5.6. Breakpoints detected by ICSS algorithm in the raw returns .................................. 33
Table 5.7. Breakpoints detected by ICSS algorithm in the filtered returns ............................. 40

vi


ABBREVIATIONS
CPI

Consumer Price Index

GARCH

Generalized Autoregressive Conditional Heteroscedasticity

GARCH-M

GARCH in Mean

GDP

Gross Domestic Product

HOSE

Ho Chi Minh City Stock Exchange

HOSTC

Ho Chi Minh City Securities Trading Center

ICSS algorithm Iterated Cumulative Sums of Squares algorithm
SSC

State Securities Committee of Vietnam

TGARCH

Threshold GARCH

VND

Vietnam Dong

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Volatility in Stock Return Series of Vietnam Stock Market

1: INTRODUCTION
Volatility is a fundamental concept in the discipline of finance. It can be described
broadly as anything that is changeable or variable. It is associated with
unpredictability, uncertainty or risk. Volatility is unobservable in financial market
and it is measured by standard deviation or variance of return which can be directly
considered as a measure of risk of assets. Considerable volatilities have been found
in the past few years in mature and emerging financial markets worldwide. As a
proxy of risk, modelling and forecasting stock market volatility has become the
subject of vast empirical and theoretical investigations over the past decades by
academics and practitioners. Substantial changes in the volatility of financial market
returns are capable of having significant effects on risk averse investors.
Furthermore, such changes can also impact on consumption patterns, corporate
capital investment decisions, leverage decisions and other business cycle. Volatility
forecasts of stock price are crucial inputs for pricing derivatives as well as trading
and hedging strategies. Therefore, it is important to understand the behavior of
return volatility.
In addition to return volatility, some relevant problems attracting much interest of
researchers have been whether or not major events may lead to sudden changes in
return volatility and how unanticipated shocks will affect volatility over time.
Concerning these factors, persistence term should be considered. Persistence in
variance of a random variable refers to the property of momentum in conditional
variance or past volatility can explain current volatility in some certain levels. The
larger the persistence is, the higher the past volatility can be explained for the
current volatility. The persistence in volatility is a key ingredient for accurately
predicting how events will affect volatility in stock returns and partially determines

stock prices. Poterba and Summers (1986) showed that the extent to which stockreturn volatility affects stock prices (through a time-varying risk premium) depends
critically on the permanence of shocks to variance. Hence, the degree to which

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Volatility in Stock Return Series of Vietnam Stock Market

conditional variance is persistent or permanent in daily stock-return data is an
important economic issue.
ARCH models proposed by Engle and Bollerslev (1982) and generalized by
Bollerslev (1986) and Taylor (1986) have been proved to be sufficient in capturing
properties of time-varying stock return volatility as well as volatility persistence.
Literature has found many evidences in supporting the capability of GARCH
models in volatility estimation (Akgiray (1989) and Pagan, Adrian R. et al. (1989))
rather than other non-GARCH models. Since the introduction of simple GARCH
models, a huge number of extensions and alternative specifications such as GARCH
in mean (GARCH-M), Threshold GARCH (Glosten, Jagannathan et al. (1993)), has
been proposed in attempt to better capture the characteristics of return series.
Meanwhile, a procedure based on an iterated cumulative sums of squares (ICSS) by
Inclan and Tiao (1994) is commonly used to detect number of significant/ sudden
changes in variance of time series, as well as to estimate the time points and
magnitude of each detected sudden change in the variance.
While studies on stock markets in mature and emerging markets are widely
available, so far not many researches have focused on Vietnam. Although being set
up much later than many countries in the world, since the establishment of the first
securities trading center of Vietnam Stock Market in Ho Chi Minh City (HOSTC)
on 28 July 2000, Vietnam stock market has been growing rapidly with improved
transaction volume and market capitalization. At the opening trading session, only
two stocks with a total market capitalization of VND986 billion (about 0.28% of

GDP of Vietnam) were traded at the market. Vietnam stock market was then
characterized by the illiquidity of stocks, incomplete legal framework and
insufficient corporate governance system. However, over time, along with the
development and world integration of Vietnam’s economy, it has gradually become
a critical channel in terms of mobilizing and distributing capital for short and longterm investments, which contribute to the expansion of business operations as well
as development of overall domestic economy. Over 10 years of operation (until the

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Volatility in Stock Return Series of Vietnam Stock Market

end of 2010), along with equitization itinerary, the number of listed companies has
increased to 280 firms with a total market capitalization of VND591 trillion. The
market capitalization represents about 30% of the country’s GDP in 2010
(equivalent to VND1,980,000 billion by the General Statistic Office), much higher
than the amount in 2000. Total stock value bought by foreign investors reached over
VND15 trillion. The stocks in HOSTC can be represented by VNIndex which is a
market-value-weighted index of all commons stocks on the HOSTC. The high and
rapid growth of Vietnam stock market is, of course, very appealing to domestic and
foreign investors.
The main objective of this study is to investigate and to model the characteristics of
stock return volatility in Vietnam stock market. The Generalized Autoregressive
Conditional Heteroscedasticity (GARCH(p, q)) model is used to capture the nature
of volatility; GJG model (or TGARCH) and GARCH-in-mean (GARCH-M) are for
examining leverage effects and risk – return premium respectively. Meanwhile, a
procedure based on iterated cumulative sums of squares (ICSS) is used to detect
number of (significant) sudden changes in variance in time series, to estimate the
time points and magnitude of each detected sudden changes in the variance. Major
events surrounding the time points of increased volatility are also analyzed. At the

same time, the linkage between volatility shifts in Vietnam stock market with
impacts from global crisis in US in 2008 is also mentioned. These detected
volatility regimes are then included in the standard GARCH model to calculate the
"true" estimate of volatility persistence.
To solve the problem mentioned above, four research questions needed to be
answered are:
Question 1: What are characteristics of return volatility in Vietnam’s stock market?
Are they similar to the results gained from previous researches?
Question 2: Which volatility models are suitable to the stock return characteristics
found out?

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Volatility in Stock Return Series of Vietnam Stock Market

Question 3: How many break points/ regime shifts are founded by using ICSS
algorithm? Are there any sudden changes found in global economic crisis period?
And what are notable events corresponding to those regime shifts?
Question 4: How do these regime shifts in stock return variance affect volatilities in
models? And what is the change of persistence in variance after breakpoints are
modified in models?
The remainder of this thesis is organized as separate sections instead of chapters as
in the conventional way of Vietnam. The first reason is that each issue is not large
enough to set up a distinct chapter. The second one is the structure of this study is
followed the method guideline of Brooks (2008). Thus, the research will be as
follows: Section 2 gives a brief literature review; Section 3 formulates hypotheses;
Section 4 focuses on the econometric methodology of selected models that had
described in literature review and applied in reality by other countries. The data and
empirical results are then reported in Section 5. Summary and concluding remarks

are presented in the last Section.

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Volatility in Stock Return Series of Vietnam Stock Market

2: LITERATURE REVIEW
2.1. Common characteristics of return series in the stock market
Many studies have documented evidence showing that financial time series have a
number of important common features to financial data such as volatility clustering,
leptokurtosis and asymmetry. Volatility clustering indicates volatility tendencies in
financial markets occur in bunches. That means large stock price changes are
expected to follow large price changes, and small price changes are followed by
periods of small price changes. Leptokurtosis means that the distribution of stock
returns is not normal but exhibits fat-tails. In other words, leptokurtosis signifies
high probability for extreme values than the normal law predict in a series.
Asymmetry, also known as leverage effect, means that a fall in return is followed by
an increase in volatility greater than the volatility induced by an increase in return.
Fama (1965) investigated the behavior of daily stock-market prices in a wide range
(from end of 1957 to September 26, 1962) for each of thirty stocks of the DownJones Industrial Average. The author found that there was some evidence of
bunching in large value of return series and return changes were leptokurtosis in
frequency distribution. Also on the US stock market but from January 1, 1970
through December 22, 1987, Baillie and DeGennaro (1990) studied the dynamics of
daily expected stock returns and volatility and pointed out high persistence and
deviation from normal distribution with leptokurtosis and negative skewness in the
data. Poon and Taylor (1992) in attempt to identify the relationship between stock
returns and volatility on the UK’s Financial Times All Share Index within 1965 –
1989 indicated clustering and high persistence in conditional volatility in this
market. These common characteristics of stock returns series continued to be

discovered in many following researches. And recently, Emenike (2010) has found
out the similar features as in previous researches like volatility clustering,
leptokurtosis and leverage effects when the author examined the volatility of stock
market returns in Nigeria Stock Exchange (NSE) from January 1985 to December
2008.

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Volatility in Stock Return Series of Vietnam Stock Market

2.2. Volatility models suitable to the stock return characteristics
To capture the volatility characteristics in financial time-series, several models of
conditional volatility have been proposed. A popular class of model was first
introduced by Engle (1982). Engle (1982) proposed to model time-varying
conditional variance with Auto-Regressive Conditional Heteroskedasticity (ARCH)
processes using squared lagged values of disturbances. This was later generalized
by Bollerslev (1986) to GARCH (generalized ARCH) model by including the lags
of conditional variance itself. The GARCH model given by Bollerslev (1986) has
been extensively used to study high-frequency financial time series data. However,
both the ARCH and GARCH models capture volatility clustering and leptokurtosis,
but as their distribution are symmetric, they fail to model the leverage effect. To
fulfill this requirement, many nonlinear extensions of GARCH have been proposed.
Some of the models include exponential GARCH (EGARCH) originally proposed
by Nelson (1991), GJR-GARCH model (or also known as Threshold GARCH
(TGARCH)) introduced by Glosten, Jagannathan et al. (1993) and Zakoian (1994).
Moreover, ARCH-M specification was also suggested by Engle, Lilien et al. (1987)
to capture relationship between risk and return. Many researchers applied the above
models.
Hamilton, Susmel. et al. (1994) studied US stock returns and reported that ARCH

effects were presented when the stock return series were observed at a high
frequency (daily or weekly returns). Bekaert and Harvey (1997) examined
thoroughly the behaviour of the volatility of stock indexes’ returns in 20 emerging
capital markets (Argentina, Chile, Colombia, Philippines, Portugal, Taiwan, …) for
the period January 1976 to December 1992. With GARCH (1, 1) and asymmetric
GARCH models, they found the volatility difficult to model in this context since
each country exhibited a specific behaviour. F.Lee, Chen et al. (2001) used GARCH
and EGARCH models for daily returns of Shanghai and Shenzhen index series over
1990 to 1997 to study characteristics of stock returns and volatility in four of
China’s stock exchanges. They pointed out evidence of time-varying volatility and

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Volatility in Stock Return Series of Vietnam Stock Market

showed high persistence and predictability of volatility. In addition, no relationship
between expected returns and expected risks was also reported as a result of
detecting GARCH-M model. Also, Alberga, Shalit et al. (2008) characterized
volatility by analyzing Tel Aviv Stock Exchange (TASE) indices using various
GARCH models like EGARCH, GJR and APARCH. Their results showed that the
asymmetric GARCH model with fat-tailed densities improved overall estimation for
measuring conditional variance. Similarly, by utilizing GARCH-type models,
Floros (2008) modeled volatility and explained financial market risk on daily data
from Egypt (CMA General index) and Israel (TASE-100 index) markets during
period from 1997 to 2007. The paper used various time series methods, including
the simple GARCH model, as well as EGARCH, TGARCH, and so on. The
conclusion was that the above models could characterize daily returns and that the
fluctuation of risk and return were not necessarily on the same trend.
2.3. Identification of breakpoints in volatilities and influence of the regime

changes
Relevant to stock market volatility, there are many works aimed at identifying the
points of change in a sequence of independent random variables. Many authors have
found that when the regime changes were taken into account, the above-mentioned
highly persistent ARCH/GARCH effects were reduced. Lamoureux and Latrapes
(1990) were among the first to study the consequences of jumps in the
unconditional variance when the time series is conditionally heteroscedastic. They
analyzed 30 exchange-traded stocks from January 1, 1963 to November 13, 1979
via GARCH (1, 1) to examine the persistence of variance in daily stock return.
Their studies pointed out that the standard GARCH model’s parameters when no
regime shifts in variance were augmented were overstated and not reliable. For lack
of a methodology such as ICSS algorithm, time point detection in sudden variance
change was conducted by dividing the study periods into equally spaced, nonoverlapping intervals, within which the variance might be different. A relatively
recent approach to test volatility shifts was Inclan and Tiao (1994)’s iterative

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Volatility in Stock Return Series of Vietnam Stock Market

cumulative sums of squares (ICSS) algorithm. This algorithm allows for
systematically detecting multiple breakpoints in variance of a sequence of
independent observations in an iterative way. On the foundation that most of
financial time series did not follow assumption of constant variance, they
considered series that had stationary behavior for some time and then suddenly the
variability of the error term changes; it remained constant again for some time at
this new value until another change occurred. Results gained from the ICSS
algorithm for moderate size (i.e., 200 observations and beyond) was comparable to
those obtained by a Bayesian approach or by likelihood ratio tests. Furthermore,
reducing the heavy computational burden required by these approaches was also a

motivation for the design of ICSS algorithm. According to them, this algorithm
could also be used for time series models. By applying the ICSS algorithm to
residuals of autoregressive processes, obtained results were similar to those gained
from ICSS algorithm to sequences of independent observations. Following the
method of Inclan and Tiao (1994), Aggarwal, Inclan et al. (1999) detected volatility
shifts of stock returns in emerging markets like Japan, Hong Kong, Singapore,
Taiwan, Philippines, Thailand, India, Brazil… over 10 years from May 1985 to
April 1995. The same conclusion was reported that volatility persistence was
declined if the breakpoints/ regime shifts were supplemented into the GARCH(1,1)
model. Similarly, clear effects of regime changes gained from ICSS algorithm on
volatility of stock return and reduction in highly persistent volatility of stock return
were presented in the studies of Malik and Hassan (2004) for five major Down
Jones stock indexes in financial, industrial, consumer, health and technology sectors
from January 1, 1992 to August 6, 2003; Malik, Farooq et al. (2005) for the
Canadian stock returns; Wang and Moore (2009) for the stock markets of new
European Union (EU) members (including the Czech Republic, Hungary, Poland,
Slovakia and Slovenia which were experienced during the period of economy
transition and of integration into the EU) during the period April 11, 1994 to March

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Volatility in Stock Return Series of Vietnam Stock Market

27, 2006; and Long (2008) for VNIndex in the Vietnam stock market from July
2000 to May 2007.
2.4. Events related to regime changes
In addition to interest in high volatility feature of stock markets and influence of
regime shifts on volatility persistence, many works concerned about whether global
or local events were more important in making major shifts in variance of stock

return and whether these events tended to be social, political or economic. In
empirical study on what kind of events corresponding to regime shifts, Aggarwal,
Inclan et al. (1999) found that high volatility periods were associated with important
political, social and economic events in each country rather than global events and
that important political events tended to be corresponding to sudden changes in
volatility. And in their research, the October 1987 crash was the only global event
in the last decade that caused a significant jump in the volatility of several emerging
stock markets like Mexico, Singapore, Malaysia, Hong Kong, US and UK.
Aggarwal, Inclan et al. (1999)’s findings were the same as those discovered by
Bekaert and Harvey (1997) and Susmel (1997), and Bailey and Chung (1995)
respectively. Bacmann and Dubois (2002) examined stock market indexes returns of
Argentina, Mexico, Malaysia, Philippines, South Korea, Taiwan and Thailand from
January 1, 1988 until January 5, 2001 and had similar conclusion as Aggarwal,
Inclan et al. (1999) that the jumps were country specific and could be diversified. In
recent paper surveying Vietnam stock market, Long (2008) proved that detected
regime changes seemed to coincide with the changes in the stock market operating
mechanism, in the financial market opening for foreign investors, or in political
events around that time.
Contrary to the above findings, after studying five major Down Jones stock indexes
in financial, industrial, consumer, health and technology sectors in the overall US
market during 1992 – 2003, the conclusion drawn from the research of Malik and
Hassan (2004) was that most volatility breaks were associated with global events
rather than sector-specific news. Hammoudeh and Li (2006) also presented the

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Volatility in Stock Return Series of Vietnam Stock Market

same viewpoint that major global events were the dominant factors for Gulf Arab

stock markets.
2.5. Sudden changes in economic recession?
Of all events studied by some authors, impacts of crises on volatility changes of
stock return has still remained a large concern of many investors and researchers.
Fernandez (2006) analyzed whether the Asian crisis in Thailand in July 1997 and
the terrorist attacks of September 11 caused permanent volatility shifts in the world
stock markets. Both the iterative cumulative sum of squares (ICSS) algorithm and
wavelet-based variance analysis were used to detect structural breaks in volatility
during 1997–2002 on eight Morgan Stanley Capital International (MSCI) stock
indices, comprising developed and emerging economies such as the World, Pacific,
Far East, G7, Emerging Asia, North America, Europe, and Latin America. The final
results showed that all indices presented breakpoints around the Asian crisis, but
only Europe appears to have been affected around the days following the 9/11
attacks. Also, with the same method – ICSS algorithm, Wang and Moore (2009)
proved that the evolution of emerging stock markets, exchange rate policy changes
and financial crises seemed to cause sudden changes in volatility. These papers
implied real influence of crises on stock markets despite at different levels.
2.6. Overstatement of ICSS algorithm in raw returns series
As being discussed above, ICSS algorithm has been used widely in many authors’
works. However, recent literature has shown that the ICSS algorithm tends to
overstate the number of actual variance shifts. This originated from ICSS algorithm
proposed by Inclan and Tiao (1994) aiming to detect structural breaks in the
unconditional variance of time-series. This algorithm requires the time-series to be
independent while stock returns are known to violate this assumption because these
series are conditionally heteroscedastic. Hence, in Bacmann and Dubois (2002)’s
paper, they pointed out one way to circumvent this problem. That was by filtering
the return series by a GARCH (1,1) model, and applying the ICSS algorithm to the

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Volatility in Stock Return Series of Vietnam Stock Market

standardized residuals obtained from the estimation. Filtering returns through
GARCH (1, 1) model helped partly remove both serial correlation and ARCH
effects in return series. Therefore, by applying this procedure (and an alternative
one they proposed) to stock market indices of ten emerging markets, Bacmann and
Dubois obtained results that differed considerably from Aggarwal et al. (1999). That
was “jumps in variance are less frequent than previously believed”. The results
gained from Bacmann and Dubois (2002)’s research was then applied by some
other authors like Fernandez (2006) and Long (2008), of which Fernandez (2006)
compared results from using ICSS to both raw and filtered returns and also
concluded that the number of shifts substantially decreased in case of filtered return.
From the above literature review, this work will continue to enrich the existing
empirical literature on exploiting characteristics of stock return volatility in
Vietnam stock market. It will also extend the sample data to cover the period when
global economic crisis occurred to evaluate the impacts of such important external
events on changes on volatility patterns of stock returns as well as relationship
between global recession and Vietnam stock market.

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Volatility in Stock Return Series of Vietnam Stock Market

3: HYPOTHESES
Basing on the mentioned research questions and the above literature review, the
hypotheses are formulated as follows:
Volatility characteristics of return series and corresponding models:
Literature review pointed out that volatility pooling, high persistence and nonnormality distribution are common features to many series of financial asset returns.

These phenomena are parameterized by GARCH, GARCH-M and TGARCH
models. Therefore, the hypotheses are proposed as below:
Hypothesis 1: Return volatility in Vietnam stock market has similar characteristics
as found in financial theory. (Answer in Section 5.1 and 5.2.1.3)
Hypothesis 2: GARCH models are suitable to characterize volatility of Vietnam
stock market’s return series. (Answer in Section 5.2.1.3)
Breakpoint identification and influence of regime shifts on volatility
persistence:
To identify sudden jumps in return variance, ICSS algorithm proposed by Inclan
and Tiao (1994) is one of methods that has been applied so popularly in recent
studies (Aggarwal, Inclan et al. (1999), Malik, Farooq et al. (2005), Long (2008),
Wang and Moore (2009), etc). Events contributing to sudden changes in volatility
were found to be local or global, depending on particular situation of each country.
Some stock markets were discovered to have breakpoints around the crisis periods
while others were not. An interesting thing is that the variance persistence was
reduced when regime shifts were combined into standard GARCH model. Hence,
for Vietnam stock market, two following hypotheses are suggested:
Hypothesis 3: Many breakpoints (including in economic crisis period) are found by
ICSS algorithm in research periods. All sudden changes are corresponding to
remarkable events. (Answer in Section 5.2.2.1 and 5.2.2.2)
Hypothesis 4: These regime shifts in stock return variance strongly affect
volatilities and reduce persistence in variance in modified models. (Answer in
Section 5.2.3)

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Volatility in Stock Return Series of Vietnam Stock Market

4: RESEARCH METHODS

To conduct the research, the thesis firstly examine the data for autocorrelation and
stationarity of Vietnam stock market’s return series on the basis of the Ljung-Box
(LB) and Augmented Dickey-Fuller (ADF) test statistics to check whether the data
can be meaningful in modeling forecast. Based on the results gained from
autocorrelation diagram and reference to Akaike information criterion (AIC) and
Schwarz’s (1978) Bayesian information criterion (SBIC), we will estimate and
choose a suitable model for mean equation of return in form of autoregressive
moving average (ARMA(p,q)) models.
The next step is testing for the presence of ARCH effects and estimating GARCH
models. Appropriate models are then selected also on the basis of AIC and SBIC.
After that, following the previous studies of Aggarwal, Inclan et al. (1999), Malik
and Hassan (2004) and so on, shifts in return volatility are detected with the iterated
cumulative sums of squares (ICSS) algorithm. At last, suitable GARCH model is
estimated with dummy variables corresponding to the breakpoints to check changes
in parameters of models if any.
The following are the methods and models applied in this research. Most of them
are based on literature of Brooks (2008).
4.1. Stationarity
The first concept is whether a series is stationary or not. According to literature of
Brooks (2008), a stationary series can be defined as one with a constant mean,
constant variance and constant autocovariances for each given lag. An examination
of whether a series can be viewed as stationary or not is essential for the following
reasons:
 The stationarity or otherwise of a series can strongly influence its behaviour
and properties. To illustrate this feature, the term ‘shock’ is usually used to denote a
change or an unexpected change in a variable or perhaps simply the value of the
error term during a particular time period. ‘Shocks’ to the system will gradually die

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Volatility in Stock Return Series of Vietnam Stock Market

away in a stationary series. Particularly, a shock during time t will have a smaller
effect in time t +1, a smaller effect still in time t + 2, and so on.
 The use of non-stationary data can lead to spurious regressions. If standard
regression techniques are applied to non-stationary data, the end result could be a
regression that ‘looks’ good under standard measures (significant coefficient
estimates and a high R2), but which is really valueless. Such a model would be
termed a ‘spurious regression’.
Gujarati (2003) claimed that if a series is non-stationary, its behavior is studied only
in the time period covered by the paper. Therefore, generalization for other periods
can not be reached. For forecasting purpose, non-stationary series will not have
realty value because in forecasting time series, volatility trends of past and current
data are assumed to be maintained for future phases. And therefore, forecast for
future time can not be implemented if the data itself often changes. Hence, the basic
condition for forecast of a time series is its stationarity.
4.2. Testing for stationarity
Two popular methods for testing stationarity are autocorrelation diagram and unit
root test.
4.2.1. Autocorrelation diagram
Autocorrelation measures the relationship between the current stock return and its
value in the previous period. It is calculated as:
pk




N k

t 1

(rt  r )(rt  k  r )



N

t 1

(rt  r ) 2

where pk is the serial correlation coefficient of stock returns of lag k, N is the
number of observations, rt is the stock return over period t, rt+k is the stock return
over period t+k, r is the sample mean of stock returns and k is the lag of the period.

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Volatility in Stock Return Series of Vietnam Stock Market

The above equation is called autocorrelation function and denoted as ACF. The
autocorrelation test aims to determine whether the serial-correlation coefficients are
significantly different from zero. We have two hypotheses as:
H0: pk =0
H1: pk  0
If a time series is random, autocorrelation coefficients are random variables with
normal distribution and mean 0 and their variances are 1/N. Therefore, with
standard error of autocorrelation coefficient of 1 / N , we can create a confidence
interval for pk. If pk is out of that confidence interval, the null hypothesis is rejected.

To test the joint hypothesis that all autocorrelations are simultaneously equal to
zero, the Ljung–Box portmanteau statistic (Q) is used. The last two columns in
autocorrelation plot are Ljung–Box Q-statistics and corresponding probability
respectively. The Ljung–Box Q-statistics are given by:
k

Q LB  N ( N  2)
j 1

p 2j
Nj

Where p j is the jth autocorrelation and N is the number of observations. Under the
null hypothesis

of zero autocorrelation at the

first

k

autocorrelations

( p1  p 2  p3  ...  p k  0) , the Q-statistic is distributed as chi-squared with degrees
of freedom equal to the number of autocorrelations (k)
4.2.2. Unit root test
Unit root test is popularly used test to verify whether a time series is stationary or
not. The early and pioneering work on testing for a unit root in time series was done
by Dickey and Fuller (Fuller (1976); Dickey and Fuller (1979)). The basic objective
of the test is to examine the null hypothesis that  = 1 in

yt   yt 1  u t

 1    1

(4.1)

against the one-sided alternative  < 1. Thus the hypotheses of interest are

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Volatility in Stock Return Series of Vietnam Stock Market

 H0:  = 1 (series contains a unit root )
 H1:  < 1 (series is stationary)
In practice, the following regression is employed, rather than (4.1), for ease of
computation and interpretation
(4.2)

y t   y t 1  u t

so that a test of  = 1 is equivalent to a test of  = 0 (since  − 1 =  ). And the
above hypotheses become:
 H0:  = 0 (series contains a unit root )
 H1:  < 0 (series is stationary)
Dickey - Fuller (DF) tests are also known as τ - tests, and can be conducted
allowing for an intercept, or an intercept and deterministic trend, or neither, in the
test regression. The null hypothesis of a unit root is rejected in favour of the
stationary alternative in each case if the test statistic is more negative than the
critical value.

4.3. GARCH model
There are two equations estimated in a basic model, one for the mean which is a
simple ARMA model and another for the variance which is identified by a
particular ARCH specification.
4.3.1. ARMA
Time series models are an attempt to capture empirically relevant features of the
observed data that may have arisen from a variety of different (but unspecified)
structural models. AutoRegressive Integrated Moving Average (ARIMA) model is
an important class of time series models, firstly introduced by Box and Jenkins
(1976).

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