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 (TGARCH) 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

iii


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
th

Table 5.4. ARCH effect at 7 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 stock-return 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 long-term 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 marketvalue-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, non-overlapping 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 waveletbased 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

13


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
2

estimates and a high R ), 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:

p

tN k (rt  r )(rt k 
 1 N
r)k
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

QLB  N(N  2)
j1

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  p2  p3  ...
 pk  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  ut

11

(4.1)

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