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Sự biến động lợi nhuận cổ phiếu trên thị trường chứng khoán Việt Nam

Bài báo này nghiên cứu các đặc điểm của sự biến động lợi nhuận của VNIndex qua việc sử dụng mô hình GARCH và nghiên cứu sự hiện diện của các điểm gãy cấu trúc trong phương sai của chuỗi lợi nhuận đó thông qua việc sử dụng thuật toán ICSS. Sử dụng dữ liệu trong một khoảng thời gian dài, mô hình GARCH và GARCH-M tỏ ra hiệu quả trong việc mô tả các đặc điểm của lợi nhuận chứng khoán hàng ngày.



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TAÏP CHÍ PHAÙT TRIEÅN KH&CN, TAÄP 14, SOÁ Q3- 2011
VOLATILITY IN STOCK RETURN SERIES OF VIETNAM STOCK MARKET
Vo Xuan Vinh, Nguyen Thi Kim Ngan
University of Economics Ho Chi Minh City
(Manuscript Received on April 04th, 2011, Manuscript Revised September 21st, 2011)

ABSTRACT: This paper studies the features of the stock return volatility using GARCH models
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. Using a long-span data, GARCH and
GARCH in mean (GARCH-M) models seems to be effective in describing daily stock returns’ features.
About structural breaks, when applying ICSS to standardized residuals filtered from GARCH (1, 1)
model, the number of volatility shifts 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. Further evidence also reveals that
when sudden shifts are taken into account in the GARCH models, volatility persistence remarkably
reduces and 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.
Keywords: ARCH/ GARCH, ICSS algorithm, break points, sudden changes
corporate

1. INTRODUCTION

capital

investment

decisions,

Volatility is a fundamental concept in the

leverage decisions and other business cycle.

discipline of finance. Considerable volatilities

Volatility forecasts of stock price are crucial

have been found in the past few years in mature

inputs for pricing derivatives as well as trading

and emerging financial markets worldwide. As

and

a proxy of risk, modelling and forecasting

important to understand the behavior of return

stock market volatility has become the subject

volatility.

of vast empirical and theoretical investigations

hedging

strategies.

Therefore,

it

is

In addition to return volatility, some relevant

over the past decades by academics and

problems

practitioners.

the

researchers have been whether or not major

volatility of financial market returns are

events may lead to sudden changes in return

capable of having significant effects on risk

volatility and how unanticipated shocks will

averse investors, on consumption patterns,

affect volatility over time. Concerning these

Substantial

changes

in

attracting

much

interest

of

Trang 5

Science & Technology Development, Vol 14, No.Q3- 2011
factors, persistence term should be considered.

points and magnitude of each detected sudden

The persistence in volatility is a key ingredient

changes in the variance.

for accurately predicting how events will affect
volatility

in

stock

returns

and

While studies on stock markets in mature and

partially

emerging markets are widely available, so far

determines stock prices. Poterba and Summers

not many researches have focused on Vietnam.

(1986) showed that the extent to which stock-

Although being set up much later than many

return volatility affects stock prices (through a

countries in the world, Vietnam stock market

time-varying risk premium) depends critically

has been growing rapidly. Therefore, main

on the permanence of stocks to variance.

objective of this paper is to investigate and to

Hence, the degree to which conditional

model the characteristics of stock return

variance is persistent or permanent in daily

volatility in Vietnam stock market. The

stock-return data is an important economic

Generalized

issue.

Heteroscedasticity (GARCH(p, q)) model is

ARCH models proposed by Engle and

Autoregressive

Conditional

used to capture the nature of volatility; GJG

by

model (or TGARCH) and GARCH-in-mean

Bollerslev (1986) and Taylor (1986) have been

(GARCH-M) are for examining leverage

proved to be sufficient in capturing properties

effects and risk – return premium respectively.

of time-varying stock return volatility as well

Meanwhile, a procedure based on iterated

as volatility persistence. Literature has found

cumulative sums of squares (ICSS) is used to

many evidences in supporting the capability of

detect number of (significant) sudden changes

GARCH

estimation

in variance in time series, to estimate the time

(Akgiray (1989) and Pagan, Adrian R. et al.

points and magnitude of each detected sudden

(1989)) rather than other non-GARCH models.

changes

Since the introduction of simple GARCH

surrounding the time points of increased

models, a huge number of extensions and

volatility are also analyzed. At the same time,

alternative specifications such as GARCH in

the linkage between volatility shifts in Vietnam

mean

GARCH

stock market with impacts from global crisis in

(Glosten, Jagannathan et al. (1993)), has been

US in 2008 is also mentioned. These detected

proposed in attempt to better capture the

volatility regimes are then included in the

characteristics of return series. Meanwhile, a

standard GARCH model to calculate the "true"

procedure based on an iterated cumulative

estimate of volatility persistence.

Bollerslev

(1982)

models

and

in

(GARCH-M),

generalized

volatility

Threshold

in

the

variance.

Major

events

sums of squares (ICSS) by Inclan and Tiao

The remainder of this paper is organized as

(1994) is commonly used to detect number of

follows: Section 2 presents a brief literature

(significant) sudden changes in variance in

review. Section 3 describes the adopted

time series, as well as to estimate the time

econometric methodology. The data description

Trang 6

TAÏP CHÍ PHAÙT TRIEÅN KH&CN, TAÄP 14, SOÁ Q3- 2011
and empirical results are then reported in

Engle (1982). Engle (1982) proposed to model

Section 4. Summary and concluding remarks

time-varying conditional variance with Auto-

are presented in the last Section.

Regressive

2. LITERATURE REVIEW

(ARCH) processes using squared lagged values

2.1. Common characteristics of return

Many studies have documented evidence
showing that financial time series have a
number of important common features to much
financial data such as volatility clustering,
leptokurtosis and asymmetry. The studies of
Mandelbrot (1963), Fama (1965) and Black
highlighted

leptokurtosis,

and

Heteroskedasticity

of disturbances. This was later generalized by
Bollerslev (1986) to GARCH (generalized

volatility in the stock market

(1976)

Conditional

volatility

clustering,

leverage

effects

characteristics of stock returns. Baillie and
DeGennaro (1990) and Poon and Taylor (1992)
investigated the dynamics of expected stock
returns and of volatility in the US and UK
stock markets respectively and found out

ARCH) model. However, both the ARCH and
GARCH models 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 risks and returns.

clustering, predictability and persistence in

Hamilton, Susmel. et al. (1994) found that

conditional volatility in these markets. These

ARCH effects were presented when the stock

common characteristics of stock returns series

returns series were observed at a high

also continued to be discovered in many

frequency (daily or weekly returns). Bekaert

following researches. And recently, Emenike

and Harvey (1997) examined thoroughly the

(2010) has found out the features as volatility

behaviour of the volatility of stock indexes

clustering, leptokurtosis and leverage effects

returns in emerging markets. They found the

when the author examined the volatility of

volatility difficult to model in this context since

stock

each country exhibited a specific behaviour.

market

returns

in

Nigeria

Stock

F.Lee, Chen et al. (2001) used GARCH and

Exchange (NSE).
2.2. Volatility models suitable to the stock

and Shenzhen index series over 1990 to 1997

return characteristics
To capture the volatility characteristics in
financial

time-series,

EGARCH models for daily returns of Shanghai

several

models

of

conditional volatility have been proposed. A
popular class of model was first introduced by

and pointed out evidence of time-varying
volatility, high persistence and predictability of
volatility. In addition, no relationship between
expected returns and expected risks was also

Trang 7

Science & Technology Development, Vol 14, No.Q3- 2011
reported as a result of detecting GARCH-M

ICSS algorithm, time point detection in sudden

model. Also, Alberga, Shalit et al. (2008)

variance change was conducted by dividing the

characterized a volatility model by analyzing

study periods into equally spaced, non-

Tel Aviv Stock Exchange (TASE) indices

overlapping

using various GARCH models like EGARCH,

variance might be different. A relatively recent

GJR and APARCH. Their results showed that

approach to test for volatility shifts was Inclan

the asymmetric GARCH model with fat-tailed

and Tiao (1994)’s iterative cumulative sums of

densities improved overall estimation for

squares (ICSS) algorithm. This algorithm

measuring conditional variance. Similarly, by

allows for systematically detecting multiple

utilizing GARCH-type models for daily data

breakpoints in variance

from Egypt (CMA General index) and Israel

independent observations in an iterative way.

(TASE-100 index) markets during period from

Results gained from the ICSS algorithm for

1997 to 2007, Floros (2008) concluded that

moderate size (i.e., 200 observations and

simple GARCH model, as well as EGARCH,

beyond) was comparable to those obtained by a

TGARCH, and so on could characterize daily

Bayesian approach or by likelihood ratio tests.

returns and that the fluctuation of risk and

According to them, this algorithm could also be

return were not necessarily on the same trend.

used for time series models. By applying the

intervals,

within

of a

which

the

series of

in

ICSS algorithm to residuals of autoregressive

volatilities and influence of the regime

processes, obtained results were similar to

changes

those gained from ICSS algorithm to sequences

2.3.

Identification

of

breakpoints

Relevant to stock market volatility, there are

of independent observations. Following Inclan

many works aimed at identifying the points of

and Tiao (1994), clear effects of regime

change in a sequence of independent random

changes gained from ICSS algorithm on

variables. Many authors have found that when

volatility of stock market return and reduction

the regime changes were taken into account,

in highly persistent volatility of stock return

the

persistent

were presented in the papers of Aggarwal,

reduced.

Inclan et al. (1999), Susmel (2000), Malik and

Lamoureux and Latrapes (1990) were among

Hassan (2004), Malik, Farooq et al. (2005),

the first to study the consequences of jumps in

Wang and Moore (2009), and Long (2008).

the unconditional variance when the time series

2.4. Events related to regime changes

is conditionally heteroscedastic. Their paper

Many papers concerned about whether global

pointed out that the standard GARCH model’s

or local events were more important in making

parameters when no regime shifts in variance

major shifts in variance of stock return and

were augmented were overstated and not

whether these events tended to be social,

reliable. For lack of a methodology such as

political or economic. Aggarwal, Inclan et al.

above-mentioned

ARCH/GARCH

Trang 8

effects

highly
were

TAÏP CHÍ PHAÙT TRIEÅN KH&CN, TAÄP 14, SOÁ Q3- 2011
(1999)’s empirical work found that high

return has still remained a large concern of

volatility

with

many investors and researchers. Fernandez

important political, social and economic events

(2006) analyzed whether the Asian crisis in

in each country rather than global events and

Thailand in July 1997 and the terrorist attacks

that important political events tended to be

of September 11 caused permanent volatility

corresponding to sudden changes in volatility.

shifts in the world stock markets. Both ICSS

Aggarwal, Inclan et al. (1999)’s findings were

algorithm and wavelet-based variance analysis

the same as those discovered by Bekaert and

were used to detect structural breaks in

Harvey (1997) and Susmel (1997), and Bailey

volatility during 1997–2002 on eight Morgan

and Chung (1995) respectively. Bacmann and

Stanley Capital International (MSCI) stock

Dubois (2002) examined stock market indexes

indices. The final results showed that all

returns

Malaysia,

indices presented breakpoints around the Asian

Philippines, South Korea, Taiwan and Thailand

crisis, but only Europe appears to have been

from 01/01/1988 until 05/01/2001 and had

affected around the days following the 9/11

similar conclusion that the jumps were country

attacks. Also, with the same method – ICSS

specific and could be diversified. In recent

algorithm, Wang and Moore (2009) proved that

paper surveying Vietnam stock market, Long

the evolution of emerging stock markets,

(2008) proved that detected regime changes

exchange rate policy changes and financial

seemed to coincide with the changes in the

crises seemed to cause sudden changes in

stock market operating mechanism, in the

volatility. These papers implied real influence

financial market opening for foreign investors,

of crises on stock markets despite at different

or in political events around that time.

levels.

periods

of

were

Argentina,

associated

Mexico,

Contrary to the above findings, after studying
five major Down Jones stock indexes in the

2.6. Overstatement of ICSS algorithm in
raw returns series

overall US market, the conclusion drawn from

Despite being used widely in many works,

the research of Malik and Hassan (2004) was

recent literature has shown that the ICSS

that most volatility breaks were associated with

algorithm tends to overstate the number of

global events rather than sector-specific news.

actual variance shifts. This originated from

Hammoudeh and Li (2006) also presented the

ICSS algorithm proposed by Inclan and Tiao

same viewpoint about the dominance of major

(1994) aiming to detect structural breaks in the

global events.

unconditional variance of time-series. This

2.5. Differences in periods before and after
economic recession?
Of all events studied by some authors,
impacts of crises on volatility changes of stock

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

Trang 9

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