Stock market volatility and return analysis: A systematic literature review - IARAS Journals
←
→
Page content transcription
If your browser does not render page correctly, please read the page content below
International Journal of Economics and Management Systems
Roni Bhowmik, Shouyang Wang http://www.iaras.org/iaras/journals/ijems
Stock market volatility and return analysis:
A systematic literature review
RONI BHOWMIK1, 2, a, SHOUYANG WANG3, b
1
Department of Business Administration, Daffodil International University, BANGLADESH
2
School of Economics and Management, Jiujiang University, CHINA
3
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, CHINA
Abstract: In the field of business research method, literature review is more relevant than ever. Even
though there has been lack of integrity and inflexibility in traditional literature reviews while some
questions can be raised about the quality and trustworthiness of these types of reviews. This research
provides literature review, using a systematic database examine and cross-reference snowballing. In this
paper, previous studies featuring a GARCH family-based model stock market return and volatility have
been reviewed. The stock market plays a pivotal role in today's world economic activities and it’s called a
“barometer” and “alarm” for economic and financial activities in a country or region. In order to prevent
the uncertainty and risk in the stock market, it is particularly important to effectively measure the
volatility of stock index returns. However, the main purpose of this review is to examine studies which
especially use to analyze return and stock market volatility. The secondary purpose of this review study is
to conduct the content analysis of return and volatility literature review over a period of twelve years
(2008 - 2019) and in 50 different papers. The study found that there has been a significant change of
research work within the past ten years and most of the researchers worked for developed stock markets.
Keywords: Stock returns; Volatility; GARCH family model; Financial time series forecasting;
market has become the focus of attention.
Therefore, it is of great theoretical and literature
1. Introduction significance to correctly measure the volatility
of stock index returns.
In the context of economic globalization, Volatility is a hot issue in economic and
especially after the impact of the contemporary financial research. Volatility is one of the most
international financial crisis, the stock market important characteristics of financial markets. It
has experienced unprecedented fluctuations. is directly related to market uncertainty and
This volatility increases the uncertainty and risk affects the investment behavior of enterprises
of the stock market and is detrimental to the and individuals. The study of the volatility of
normal operation of the stock market. To reduce financial asset returns is also one of the core
this uncertainty, it is particularly important to issues in modern financial research, and this
accurately measure the volatility of stock index volatility is often described and measured by the
returns. At the same time, due to the important variance of the rate of return. The traditional
position of the stock market in the global econometric model often assumes that the
economy, the healthy development of its stock variance is constant, that is, the variance is kept
ISSN: 2367-8925 217 Volume 5, 2020International Journal of Economics and Management Systems
Roni Bhowmik, Shouyang Wang http://www.iaras.org/iaras/journals/ijems
constant at different times. The accurate interest rate liberalization on risk-free interest
measurement of the fluctuation of the rate of rates. Looking at the major global capital
return is directly related to the correctness of markets, the change in risk-free interest rates has
portfolio selection, the effectiveness of risk a greater correlation with the current stock
management and the rationality of asset pricing. market. In general, when interest rates continue
However, with the development of financial to rise, the risk-free interest rate will rise, and
theory and the deepening of empirical research, the cost of capital invested in the stock market
it is found that this assumption is not reasonable. will rise simultaneously. As a result, the
And the volatility of asset prices is one of the economy is expected to gradually pick up during
most puzzling phenomena in financial the release of the reform dividend, and the stock
economics. Understanding volatility is still we market is expected to achieve a higher return on
face a very big challenge. investment.
Literature reviews act as a significant part as Volatility is the tendency for the prices to
a basis for all kinds of research work. Literature change unexpectedly (Harris, 2003), however
reviews serve as a foundation for knowledge not all volatility is bad. At the same time,
progress, make guidelines for plan and practice, financial market volatility is also a direct impact
provide grounds of an effect, and, if well guided, on macroeconomic and financial stability. The
have the capacity to create new ideas and important economic risk factors are generally
directions for a particular area (Snyder, 2019). highly valued by governments around the world.
As similar, they carry out as the basis for future Therefore, research on the volatility of financial
research and theory work. This paper conducts markets has always been the focus of attention
the literature review of relationship between of financial economists and financial
stock returns and volatility. Volatility refers to practitioners. Nowadays, a large number of
the degree of dispersion of random variables. literatures have studied some characteristics of
Financial market volatility is mainly reflected in the stock market, such as the leverage effect of
the deviation of expected future value of assets. volatility, the short-term memory of volatility
The possibility, that is, volatility, represents the and the GARCH effect, etc., but some researcher
uncertainty of the future price of an asset. This show that when adopt short-term memory by the
uncertainty is usually characterized by variance GARCH model it describes the model, there is
or standard deviation. For the relationship usually a confusing phenomenon, as the
between this two, there are currently two main sampling interval tends to zero. The
explanations in the academic world: the leverage characterization of the tail of the yield generally
effect and the volatility feedback hypothesis. assumes an ideal situation, that is, obeys the
Leverage often means that unfavorable news normal distribution, but this perfect situation is
appears, stock price falls, leading to an increase generally not established.
in leverage factor, and the degree of stock Researchers have proposed different
volatility increases. Conversely, the degree of distributed model in order to better describe the
volatility weakens; volatility feedback can be thick tail of the daily rate of return. Engle (1982)
simply described as unpredictable stock first proposed an autoregressive conditional
volatility will inevitably lead to higher risk in heteroscedasticity model (ARCH model) to
the future. characterize some possible correlation of the
There are many factors that affect the price conditional variance of the prediction error.
movements in the stock market: first, the impact Bollerslev (1986) extended it to form a
of monetary policy. Generally speaking, the generalized autoregressive conditional
impact of monetary policy on the stock market is heteroskedastic model (GARCH model). Later,
very heavy. If a loose monetary policy is the GARCH model was rapidly expanded and a
implemented that year, the probability of a stock GARCH family model was created. Bollerslev
market rise will increase. On the contrary, if a proposes a GARCH model of the t-distribution
relatively tight monetary policy is implemented of unknown degrees of freedom k, and the
that year, the probability of a stock market degree of freedom k can be estimated from the
decline will increase. Secondly, the impact of data. When 4International Journal of Economics and Management Systems
Roni Bhowmik, Shouyang Wang http://www.iaras.org/iaras/journals/ijems
greater than the normal distribution, and when series. There are other review studies on return
k→∞, the distribution converges to the normal and volatility analysis and GARCH family based
state. Another distribution that characterizes the financial forecasting methods such as (Hussain
thick tail is the generalized error distribution et al., 2019), (Dhanaiah & Prasad, 2017),
(GED) proposed by Nelson (1991). The (Reddy & Narayan, 2016), (Mamtha &
distribution density is characterized by the shape Srinivasan, 2016), (Scott, 1991). Consequently,
parameter r. When r=2, the distribution is the aim of this manuscript is to put forward the
normal; when r2, the distribution tail is thinner selection as well as the model selection and
than the normal. contribute understanding to the academic
When employing GARCH family model in researchers and financial practitioners.
analyze and forecast return volatility, selection Systematic reviews have most notable been
of input variables for forecasting is as crucial as expand surrounded by medical science as a way
the appropriate and essential condition will be to synthesize research recognition in a
given for the method to have a stationary systematic, transparent, and reproducible
solution and perfect matching. It has been shown process. Notwithstanding all the opportunity of
in several findings that the unchanged model can this technique, its exercise has not been overly
produce suggestively different results when fed widespread in business research, but it is
with different inputs. Thus, another key purpose expanding day by day. In this paper we used
of this literature review is to observe studies systematic review process because the target of a
which use directional prediction accuracy model systematic review is to determine all empirical
as a yardstick since from the realistic point of indication that fits the pre-decided inclusion
understanding it is the core objective of the criteria or standard to response a certain research
forecast of financial time series in stock market question or hypothesis.
return. Leung et al. (2000) and Bhowmik et al. The main contribution of this paper is found
(2017) an estimate with little forecast error in the following three aspects: (1) the manuscript
namely measured as mean absolute deviation considers the very recent years ’papers, 2008 to
(MAD), root mean squared error (RMSE), mean 2019, which have not been covered in previous
absolute error (MAE) and mean squared error studies. (2) By this study using both the
(MSE) does not essentially interpret into a qualitative as well as quantitative processes for
capital gain. Yao & Tan (2000) mention that, it examining the literature involving to stock
does not stuff whether the predictions are precise returns. (3) The manuscript provides the study
or not in terms of NMSE (normalized mean based on journals which will help the
squared error). It means that finding low root academicians and researchers to recognize
mean squared error does not feed high returns, in important journals which they can denote for
another words the relationship is not linear literature review, recognize factors motivating
between two. analysis stock returns volatility and can publish
Consequently, in this manuscript it is their worth study manuscripts.
proposed to categorize studies not only for their
model selection standards but also for the inputs
used for the return volatility and also how 2. Methodology
precise is spending them in terms of return
directions. In this investigation, we will repute The study was conducted a structured based
studies which use percentage of success trades literature review, following the suggestions of
benchmark procedures for analysis the proposed Easterby-Smith et al. (2015), and Tranfield et al.
model. From this theme of view, this study (2003). In this manuscript was led a systematic
authentic approach is compared earlier models database search, surveyed by cross-reference
in literature review for their input variables used snowballing, as demonstrated in Figure-1, which
for forecast volatility and how precise they are one adapted from Geissdoerfer et al. (2017).
in analyzed the direction of the related time
ISSN: 2367-8925 219 Volume 5, 2020International Journal of Economics and Management Systems
Roni Bhowmik, Shouyang Wang http://www.iaras.org/iaras/journals/ijems
Search string-based database search
Define an initial sample of relevant papers
based on relevance of abstract
Include Exclude
Cross reference search
Look at title in references of samples article
Look at place of reference & content described
Look at abstract of referenced article
Literate until new articles
are not contributing
significantly to answer the
Decide about relevance article research question
Exclude Include When no new articles are
included in the sample, the
content of the selected
Inclusion in the sample for literature review articles is reviewed
Figure. 1. Literature review method
At first stage, a systematic literature return”, “forecasting stock return” and GARCH
search was managed. As shown in Table – 1, model*, “financial market return and volatility”
the search strings, “market return” in ‘Title ’ in ‘Topic ’separately ‘Article title, Abstract,
respectively “stock market return”, “stock Keywords ’were used to search for reviews of
market volatility”, “stock market return articles in English on the Elsevier Scopus and
volatility”, “GARCH family model* for stock Thomson Reuters Web-of-Science databases.
Table – 1. Literature search strings
Search string Search field Number of non-exclusive results
Scopus Web-of- Last
Science updated
Market return Title/Article title 1540 1148 17/01/2020
Market volatility Topic/Article title, Abstract, 13892 13767 17/01/2020
Keywords
Stock market return Topic/Article title, Abstract, 11567 13440 17/01/2020
Keywords
ISSN: 2367-8925 220 Volume 5, 2020International Journal of Economics and Management Systems
Roni Bhowmik, Shouyang Wang http://www.iaras.org/iaras/journals/ijems
Stock market volatility Topic/Article title, Abstract, 5683 6853 17/01/2020
Keywords
market return and volatility Topic/Article title, Abstract, 3241 6632 17/01/2020
Keywords
GARCH family model* for stock Topic/Article title, Abstract, 53 41 17/01/2020
return Keywords
Forecasting stock return and GARCH Topic/Article title, Abstract, 227 349 17/01/2020
model* Keywords
Financial market return and volatility Topic/Article title, Abstract, 2212 2638 17/01/2020
Keywords
At second stage, suitable cross-references developed earlier. Every published article, three
were recognized in this primary sample by first group are specified. Those groups are considered
examining the publications ’title in the reference index and forecast time period, input elements,
portion and their context and cited content in the econometric models, and study results. The first
text. The abstracts of the recognized further class namely “considered index and forecast
publications were examined to determine time period with input elements” is considered
whether the paper was appropriate. Appropriate since market situation like emerging, frontier
references were consequently added to the and, developed markets are important
sample and analogously scanned for appropriate parameters of forecast and also the length of
cross-references. This method was continual evaluation is a necessary characteristic for
until no additional appropriate cross-references examining robustness of the model. And input
could be recognized. elements are comparatively essential parameters
At third stage, the ultimate sample was for a forecast model because the analytical and
assimilated, synthesised, and compiled into the diagnostic ability of the model is mainly
literature review presented in the subsequent. supported on the inputs what variable used. In
The method was revised on few days before of the “model” class, forecast models proposed by
the submission. authors and other models for assessment are
listed. At the last class which is important to our
examination for comparing studies in
relationships of proper guiding return and
3. Review of Different Studies volatility acquired by using recommended
estimate models is the “study results” class.
In this article, a massive number of Literatures of the eligible papers are summarized
articles were studied but only a small number of in a table format for future studies.
them well thought-out to gather the quality
ISSN: 2367-8925 221 Volume 5, 2020International Journal of Economics and Management Systems
Roni Bhowmik, Shouyang Wang http://www.iaras.org/iaras/journals/ijems
Table – 2. Different literature studies
Authors Data Set Econometric Models Study results
Alberg et al. Daily returns data, TASE GARCH, EGARCH, GJR, Findings suggest that one can
(2008) indices, the TA25 index and APARCH model improve overall estimation by using
period October 1992 to the asymmetric GARCH model; and
May 2005 and TA100 index EGARCH model is a better predictor
period July 1997 to May than the other asymmetric models.
2005
Singh et al. Fifteen world indicesfor the AR-GARCH, bivariate There is significant positive
(2008) period of January 2000 to VAR, Multivariate GARCH volatility spillover from other
February 2008 have been (BEKK) model markets to Indian market, mainly
considered from Hong kong, Korea, Japan and
Singapore and US market. Indian
market affects negatively the
volatility of US and Pakistan.
Rao (2008) Daily returns data from MGARCH and VAR Arabian Gulf Cooperation Council
February 2003 to January models markets exhibit significant own and
2006, Arabian Gulf cross spillover of innovations and
Cooperation Council equity volatility spillover and persistence in
markets data these markets.
Olowe (2009) Daily returns over the EGARCH in mean model Nigerian stock market returns show
period January 2004 to that volatility is persistent and there
March 2009 is a leverage effect. The study found
little evidence open the relationship
between stock returns and risk as
measures by its aim volatility.
Girard & Examine the interaction of GARCH model They found that information size and
Omran (2009) volatility and volume in 79 direction have a negligible effect on
traded companiesinCairo conditional volatility and, as a result,
and Alexandria Stock the presence of noise trading and
Exchange speculative bubbles is suspected.
Neokosmidis Six years’ data fromMarch ARCH, GARCH (1, 1), The study concludes that EGARCH
(2009) 2003 to March 2009 for EGARCH (1, 1) model is that best fitted process for
four US stock indices i.e., Multivariate volatility all the sample data based on AIC
DOWJONES, NASDAQ, models minimum criterion. It is observed
NYSE, S & P500 that there are high volatility periods
at the beginning and at the end of
our estimation period for all stock
indices.
Tripathy & Daily OHLC values of NSE Rolling window moving A GARCH and VIX models, proved
Alana (2010) index returns from 2005- average estimator, EWMA, to be the best methods. Extreme
2008 GARCH models, Extreme value models fail to perform because
value indicators, and of low frequency data.
Volatility index (VIX)
ISSN: 2367-8925 222 Volume 5, 2020International Journal of Economics and Management Systems
Roni Bhowmik, Shouyang Wang http://www.iaras.org/iaras/journals/ijems
Liu & Hung Taiwanese stock index GARCH type models: They demonstrate that the EGARCH
(2010) futures prices, daily data GARCH, GJR-GARCH, model provides the most accurate
April 2001 to December QGARCH, EGARCH, daily volatility forecasts, while the
2008 IGARCH, CGARCH performances of the standard
GARCH model and the GARCH
models with highly persistent and
long-memory characteristics are
relatively poor.
Maniya and Five stock indices: S&P ARCH, GARCH models Time varying correlation increases
Magnnsson 500, NIKKE 225, KSE 100, GARCH-BEKK model in bearish spells whereas bullish
(2010) BSE 30, Hang seng. Daily correlation, unit root tests, periods do not have a big
closing Index and data from granger-causality test "Statistical" impact on correlation.
January 1989 to December
2009
Joshi (2010) Daily closing price from BDS Test, ARCH-LM test, Persistence of volatility is more than
January 2005 to May 2009 and GARCH (1, 1) model Indian stock market
Princ (2010) Daily returns of Prague DCC-MVGARCH model The study found an existence of
stock exchange Index and increasing trend in conditional
other 11 major stock indices correlations among a whole
during 1994 to 2009 European region. Results show the
unidirectional influence of foreign
markets affecting Czech market.
Yong et al. daily data of Japanese stock BEKK-GARCH model They found that news shocks in the
(2011) over the study period 1994- Japanese currency market account
2007 for volatility transmission in eight of
the ten industrial sectors considered.
They also found that significant
asymmetric effects in five of these
industries.
Athukoralalage Weekly stock market data M-GARCH Model, Positive return spillover effects are
(2011) of Australia, Singapore, Diagonal BEKK model only unidirectional and run from
UK, US for the period from ARCH and GARCH both US and UK (the bigger
Jan 1992 to June 2010 techniques markets) to Australia and Singapore
(the smaller markets). Shocks arising
from the US market can impact on
all of the other markets in the
sample.
Kouki et al. Five sectors daily data VAR Framework one lag, International financial markets are
(2011) covering period from BEKK (1, 1) model not integrated in all the sectors.
January 2002 to October Results find that three highly
2009 integrated sectors; bank, real estate
and oil.
Wong & Hong Kong stock market GARCH family models The EGARCH and AGARCH
Cheung (2011) from 1984 to 2009 models can detect the asymmetric
effect well in response to both good
news and bad news. By comparing
different GARCH models, they find
that it is the EGARCH model that
best fits the Hong Kong case.
ISSN: 2367-8925 223 Volume 5, 2020International Journal of Economics and Management Systems
Roni Bhowmik, Shouyang Wang http://www.iaras.org/iaras/journals/ijems
Chang et al. Taiwan Stock Exchange GJR-GARCH model (1, 1) There is a significant price
(2011) (TAIEX), the S&P 500 transmission effect and volatility
Index, and the NASDAQ asymmetry among the TAIEX, the
Composite Index for the US spot index and the US index
period of January, 2000 to futures.
January, 2004
Walid et al. The weekly closing stock Markov-Switching- Results provide strong evidence that
(2011) indexes and local currency EGARCH model the relationship between stock and
and exchange rates used for foreign exchange market is regime
four emerging markets, data dependent and stock price volatility
from December 1994 to responds asymmetrically to events in
March 2009 the foreign exchange market.
Koutmos (2012) Shanghai stock exchange Volatility estimation AR (1) Time varying beta risk of industry
Ten industries sector EGARCH (1, 1) sector indices in Shanghai stock
indices daily data ranging Results industries respond positively
from January 2009 to June to rises in such non-diversifiable
2012 risk. Reports on the volatility
persistence of the various industry
sectors and identifies which
industries have high and low
persistence.
Chen (2012) New York, London and Granger causality test, VAR Evidence shows that five stock
Tokyo as well as those of model, VEC model, markets are in the process of
Hong Kong, Shanghai and Variance decomposition, increasing integration. The periodic
Shenzen the period of Impulse response function, break down of co-integrating
January 1993 to March Co-integration and GARCH relationship is advantageous to
2010 models foreign investors.
Abdalla & Saudi stock market by using GARCH (1, 1) model, The results provide evidence of the
Suliman(2012) (Tadawul All Share Index; including both symmetric existence of a positive risk premium,
TASI) over theperiod of and asymmetric models which supports the positive
January 2007 to November correlation hypothesis between
2011 volatility and the expected stock
returns.
Maheshchandra Daily closing price of BSE ARFIMA and FIGARCH Absence of long memory in return
(2012) and NSE stock indices models series of the Indian stock market.
period of January 2008 to Strong evidence of long memory in
August 2011 conditional variance of stock
indices.
Li & Wang China stock indices, six ARMA and GARCH family The paper examined the leverage
(2013) industry indexes, January model, GARCH (1, 1), effect and information symmetry.
2006 to June 2012 TGARCH (1, 1), EGARCH Both ARCH and GARCH models
(1, 1) can explain volatility clustering
phenomena and have been quite
successful in modeling real data in
various applications.
ISSN: 2367-8925 224 Volume 5, 2020International Journal of Economics and Management Systems
Roni Bhowmik, Shouyang Wang http://www.iaras.org/iaras/journals/ijems
Katzke (2013) Daily closing prices of six AR (1) model, MV- The results show that global and
largest industrial sector GARCH models, DCC domestic economic uncertainty as
composite total return models, VECH and BEKK well as local asset market segment
indices during January 2002 techniques, and GJR- significantly influences both the
to April 2013 GARCH model short run dynamics and the
aggregate level of co- movement
between local sector pairs.
Hou (2013) Daily closing prices of the Parametric GARCH family An asymmetric effect of negative
SHCI and SZCI indices models news exists in the Chinese stock
from January 1997 to markets. The EGARCH and the GJR
August 2007 models tend to overestimate the
volatility and returns in the high-
volatility periods.
Purohit et al. Daily closing data for ADF Test, Johansen’s co- Empirical results found that one-
(2014) November 2009 to March integration test, and month futures do not bring volatility
2013, NIFTY and NIFTY GARCH (1, 1) model in the VIX.
Junior indices
Shalini (2014) Daily data of sectoral ARMA (1, 1), and GARCH Return of the BSE sectoral indices
indices for the period of (1, 1) models exhibit characteristics of normality,
January 2001 to June 2014 stationarity and heteroscedasticity.
Ghorbel and MENA stock market GARCH family models MENA region’s markets are higher
Attafi (2014) indices of daily between extremes than between
observations for the period ordinary observations registered
January 2007 to March during normal periods, but they offer
2012 many opportunities to investors to
diversify their portfolio and reduce
their degree of risk aversion.
Dependence between markets
increases during volatile periods.
Joshi (2014) BSE Sensex dailydata from GARCH (1, 1), EGARCH Stock market exhibits the
January 2010 to July 2014 (1, 1), and GJR-GARCH (1, persistence of volatility, mean
1) models reversion behavior and volatility
clustering. The results show the
presence of leverage effect implying
impact of good and bad news is not
name.
Gupta et al. The daily closing prices of GARCH, TGARCH and The result of that volatility varies
(2014) S&P CNX500 of National EGARCH models over time and constant variance
Stock Exchange for the assumption is inconsistent. The
period from January 2003 empirical evidence indicated the
to December 2012 presence of time varying volatility.
Nadhem et al. S&P500 market daily GARCH family models Results of ANN models will be
(2015) returns the sample period compared with time series model
from July 1996 to May using GARCH family models. The
2006 use of the novel model for
conditional stock markets returns
volatility can handle the vast amount
of nonlinear data, simulate their
relationship and give a moderate
solution for the hard problem.
ISSN: 2367-8925 225 Volume 5, 2020International Journal of Economics and Management Systems
Roni Bhowmik, Shouyang Wang http://www.iaras.org/iaras/journals/ijems
Banumathy & The daily closing prices of Both symmetric and The result proves that GARCH (1,1)
Azhagaiah S&P CNX Nifty Index for asymmetric models and TGARCH (1,1) estimations are
(2015) the period from January GARCH (1, 1) found to be most appropriate model
2003 to December 2012 to capture the symmetric and
asymmetric volatility respectively.
Okičić (2015) Central and Eastern Europe Both symmetric and Study indicate that existence of the
region for the period from asymmetric GARCH leverage effect in case of stock
October 2005 to December models, i.e.; GARCH, markets from the CEE region, which
2013 IGARCH, EGARCH, GJR indicates that negative shocks
and PGARCH increase the volatility more than
positive shocks.
Lum and Islam Australian share markets GARCH family models Findings support asymmetric effects
(2016) data for the period of in the Australian share markets, and
January 1988 to December by incorporating them into the
2004 GARCH-M models yield better
results in both financial and
econometric terms.
Jebran and Asian countries, i.e., GARCH model Result revealed absence of any
Iqbal (2016) Pakistan, India, Sri Lanka, spillover effect of volatility across
China, Japan, and Hong Indian and Chinese stock markets.
Kong. The daily data was However, bidirectional and
considered from the period unidirectional spillover effects have
January 1999 to January been established across other Asian
2014 markets.
Yang et al. CSI 300 index consider for GARCH, EGARCH, The PTTGARCH models both with
(2016) the period of July 2013 to APARCH and PTTGARCH single regime and Markov regime
January 2016 model switching outperform the other
models in estimation and prediction
of the volatilities of the return series
within the sample and out-of-
sample.
Varughese and India stock market daily GARCH, EGARCH, The existence of volatility clustering
Mathew (2017) data for the period of April TARCH and leverage effect in the market and
2003 to March 2015 the investment activities of foreign
portfolio investment have had a
significant impact on the volatility of
stock market.
Peng et al. TAIEX and Nikkei from Bi- EGARCH model The past returns on NIKKEI
(2017) both indices over the period influenced significantly current
of January, 2000 to March, period returns of TAIEX, yet there
2016 was no such influence flowing from
past returns of TAIEX to the current
returns on NIKKEI index. Further,
the two stock markets are more
sensitive to falling rather than rising
trends of each other, implying that
there is a mutual tendency between
these markets to crash due to a
retreat in the counterpart market.
ISSN: 2367-8925 226 Volume 5, 2020International Journal of Economics and Management Systems
Roni Bhowmik, Shouyang Wang http://www.iaras.org/iaras/journals/ijems
Pati et al. (2017) India NIFTY Volatility GARCH family models The study finds that volatility index
Index (IVIX) and CNX is a biased forecast but possesses
NIFTY Index (NIFTY), relevant information in explaining
Australia S&P/ASX 200 future realized volatility. GARCH
Volatility Index (AVIX) family models suggest that it
and S&P/ASX 200 Index contains relevant information in
(ASX), and Hong Kong describing the volatility process.
Hang Seng Volatility Index
(VHSI) and HSI, consider
the period of January 2008
to July 2016
Bhowmik et al. Emerging six Asian stock GARCH model, Granger The volatility and return spillovers
(2018) markets daily stock market Causality Tests and VAR behave very differently over time,
index data from January model during the pre-crisis, crisis, and post
2002 to December 2016 crisis periods. Importantly, Asian
emerging stock markets interaction
is less before the global financial
crisis period.
Kim and Lee Daily negative returns of PTTGARCH models Article demonstrates its validity
(2018) the Google’s stock price through a simulation study and real
and DowJones index, data analysis. The result indicates
November 2004 to that for practical applications, the
November 2016 underlying innovation distribution
should be modeled in a more refined
manner.
Amudha and NSE from the period of GARCH family models The findings reported an evidence of
Muthukamu April 2003 to September volatility, which exhibited the
(2018) 2015 clustering and persistence of stocks.
The return series of the stocks
selected for the study were found to
react on the good and bad news
asymmetrically.
Chronopoulos et US stock return a daily GARCH family models The SVI variable exhibits the best
al. (2018) frequency S&P 500 index performance among all considered
covering the period from models and SVI variable offers the
January 2004 to December highest gains for investors.
2016
Fan and Di Shanghai Composite Index GARCH family models The best model is GARCH (1,1) and
(2018) and the exchange rate of the asymmetric effect is not
Chinese RMB against the significant.
US dollar, from January
2004 to November 2016
Bhowmik and BSE 30, SSE composite, GARCH family models and The returns and volatility linkages
Wang (2018) DSEX, FBMKLCI, PSEi, VAR model exist between the emerging Asia and
KOSPI indices data of daily the developed stock markets. The
closing prices for the period volatilities to unexpected shocks in
of January 2007 to 2016 various markets, especially, come
from neighboring country markets
and more developed country
markets.
ISSN: 2367-8925 227 Volume 5, 2020International Journal of Economics and Management Systems
Roni Bhowmik, Shouyang Wang http://www.iaras.org/iaras/journals/ijems
Wang et al. High frequency data, stock GARCH-M and EGARCH- The results show that China’s stock
(2019) market policies issued M models market was mainly driven by
related news, January 2014 government policies rather than
to August 2015 economic fundamentals, as
measured by GDP, PPI, and PMI.
Shanthi and Nifty 50 and BSE Sensex GARCH, TGARCH, The study indicates that symmetric
Thamilselvan daily data from both indices EGARCH models information is not suitable for
(2019) over the period of January certain period considered in this
1995 to December 2015 study. The TGARCH model
outperformed all the models due to
the information availability.
Bhowmik and The data consists of daily, Unit root tests, serial Study suggests that none of the
Wang (2019) weekly, and monthly correlation test, runs test, sample Asian emerging stock
closing prices of six VR tests, ARMA, GARCH markets follow Random-walk and
emerging stock market model, and BDS test hence all are weak-form efficient
indexes in Asian countries markets except South Korean
from the period of 2007 to Markets. Additionally, short-term
2016 variants of the technical trading rules
have better predictive ability than
long-term variants.
Dixit and BSE and NSE daily data of GARCH family models The study suggested that P-GARCH
Agrawal (2019) the closing value from April model is most suitable to predict and
2011 to March 2017 forecast the stock market volatility
for BSE and NSE markets.
Kumar and Brazil, India, Indonesia and GARCH family models The result confirms the presence of
Biswal (2019) Pakistan stock markets volatility clustering and leverage
return of the average price effect that is the good news affects
(open, close, high, and low) the future stock market than bad
for January 2014 to October news.
2018
A literature review must be necessary some rigorous literature reviews and, in the long
for scholars, academics and practitioners. run, simply for better research.
However, assessing various kinds of literature
reviews can be challenging. No matter how
outstanding and demanding the literature review 4. Conclusion
article, if it does not give sufficient of a
contribution, something that is latest, it will not Working a literature review is hard work.
be published. Too often, literature reviews are This paper presents a comprehensive literature
fairly descriptive overviews of research carry has mainly focused on studies on return and
out among particular years, draw such data as volatility of stock market using systematic
the number of articles published, subject matter review methods on various financial markets
covered, authors represented, and maybe around the world stock markets. We offer the
methods used, without conducting any deeper raw data from the literature together with
investigation. However, conducting a literature explanations of this data and key fundamental
review and examine its standard can be concepts.
challenging, which is why this article provide Stock market return and volatility analysis is
a relatively very important and emerging field of
the research. There have been a large number of
ISSN: 2367-8925 228 Volume 5, 2020International Journal of Economics and Management Systems
Roni Bhowmik, Shouyang Wang http://www.iaras.org/iaras/journals/ijems
researches on financial market volatility and 1. Abdalla, S. Z. S., & Suliman, Z. (2012).
return because of increasing easily accessibility Modelling stock returns volatility: Empirical
and availability of researchable data and evidence from Saudi Stock Exchange.
computing capability. Altogether research International Research Journal of Finance and
papers were selected by systematic analysis for Economics, 85, 166-179.
the purpose of volatility and return analysis and 2. Alberg, D., Shalit, H., & Yosef, R. (2008).
review. The popularity of various ARCH family Estimating stock market volatility using
models has increased in the in recent times. To asymmetric GARCH models. Applied
sum up, reviewed papers few scholars suggest Financial Economics, 18(15), 1201-1208.
that GARCH family model combined with 3. Amudha, R. & Muthukamu, M. (2018).
another statistical technique yield better results. Modeling symmetric and asymmetric
Additionally, few researchers using multivariate volatility in the Indian stock market. Indian
GARCH model statistical techniques for Journal of Finance, 12(11), 23-36.
analysis the market volatility and returns, they 4. Athukoralalage, K. P. I. (2011). Modelling
show that a more accurate and better results Australian stock market volatility. 1-161.
found by multivariate GARCH family models. https://ro.uow.edu.au/cgi/viewcontent.cgi?ref
Asymmetric GARCH models, for instance and erer=https://scholar.google.com/&httpsredir=
like, EGARCH, GJR GARCH and TGARCH 1&article=4435&context=theses
etc. have been introduced to capture effect of 5. Banumathy, K., & Azhagaiah, R. (2015).
bad news on the change in volatility of stock Modelling Stock Market Volatility: Evidence
returns. This study all though it is short and from India. Managing Global Transitions:
particular attempts to give the scholar a concept International Research Journal, 13(1), 27-42.
of different methods found in this systematic 6. Bhowmik, R., Ghulam, A., & Wang, S.
literature review. (2018). Return and volatility spillovers
Whenever there is assurance that the effects: study of Asian emerging stock
scholars are built on high accuracy, it will be markets. Journal of Systems Science and
plentiful easier to recognize genuine research Information, 6(2), 97-119.
gaps instead of merely conducting the same 7. Bhowmik, R., & Wang, S. (2018). An
research again and again, to progress better and investigation of return and volatility linkages
more appropriate hypotheses and research among stock markets: A study of emerging
questions, and, consequently, to raise the Asian and selected developed countries.
standard of research for the future generation. Journal of International Trade & Commerce,
This study actually will be beneficial for the 14(4), 1-29.
researchers, scholars, stock exchanges, the 8. Bhowmik, R., & Wang, S. (2019). Is the
regulators, government, investors and other emerging Asian stock markets really
concerned parties. The current study also predictable- based on the Operations and
contributes the scope for further research in the Information Management. International
area of stock volatility and returns. The content Journal of Supply Chain Management, 8(1),
analysis can be executed taking the literature of 600-621.
last decades. It is determined that a lot of 9. Bhowmik, R., Wu, C., Kumar, J. R., & Wang,
methodologies like GARCH models, Johansen S. (2017). A study on the volatility of the
models, VECM, Impulse response functions, Bangladesh stock market — based on
Granger causality tests are practiced broadly in GARCH type models. Journal of Systems
examining the stock market volatility and return Science and Information, 5(3), 193-215.
analysis across countries and also among sectors 10. Bollerslev, T. (1986). Generalized
with in a country. autoregressive conditional heteroskedasticity.
Journal of Econometrics, 31(3), 307-327.
11. Chang, C. H., Cheng, H. I., Huang, I.
References H., & Huang, H. H. (2011). Lead‐lag
relationship, volatility asymmetry, and
ISSN: 2367-8925 229 Volume 5, 2020International Journal of Economics and Management Systems Roni Bhowmik, Shouyang Wang http://www.iaras.org/iaras/journals/ijems overreaction phenomenon. Managerial market behavior in NSE. International Finance, 37(1), 47-71. Journal of Innovations in Engineering and 12. Chen, X. (2012). Empirical Management, 3(1), 16-20. investigations into stock market integration 23. Harris, L. (2003). Trading and and risk monitoring of the emerging Chinese exchanges: Market microstructure for stock markets. University of St Andrews, 1- practitioners. Oxford University Press. 314. https://research-repository.st- 24. Hou, A. J. (2013). Asymmetry effects of andrews.ac.uk/handle/10023/3208 shocks in Chinese stock markets volatility: A 13. Chronopoulos, D. K., Papadimitriou, F. generalized additive nonparametric approach. I., & Vlastakis, N. (2018). Information Journal of International Financial Markets, demand and stock return predictability. Institutions and Money, 23, 12–32. Journal of International Money and Finance, 25. Hussain, S., Murthy, K. V. B., & Singh, 80, 59–74. A. K. (2019). Stock market volatility: A 14. Dhanaiah, G., & Prasad, S. R. (2017). review of the empirical literature. IUJ Journal Volatility and co-movement models: A of Management, 7(1), 96-105. literature review and synthesis. International 26. Jebran, K., & Iqbal, A. (2016). Journal of Engineering and Management Examining volatility spillover between Asian Research, 7(1), 1-25. countries ’stock markets. China Finance and 15. Dixit, J. & Agrawal, V. (2019). Economic Review, 4(6), 1-13. Foresight for stock market volatility: A study 27. Joshi, P. (2010). Modeling volatility in in the Indian perspective. Foresight, 22(1), 1- emerging stock markets of India and China. 13. Journal of Quantitative Economics, 8(1), 86- 16. Easterby-Smith, M., Thorpe, R., & 94. Jackson, P. (2015). Management and 28. Joshi, P. (2014). Forecasting volatility Business Research. Sage. of Bombay stock exchange. International 17. Engle, R. F. (1982). Autoregressive Journal of Current Research and Academic Conditional Heteroskedasticity with Review, 2(7), 222-230. Estimates of the Variance of U.K. Inflation. 29. Katzke, N. (2013). South African Sector Econometrica, 50, 987–1008. Return Correlations: using DCC and ADCC 18. Fan, M. & Di, W. (2018). Volatility of Multivariate GARCH techniques to uncover China Shanghai a stock price-exchange rate. the underlying dynamics. University of Asia Pacific Journal of Advanced Business Stellenbosch, Stellenbosch Economic and Social Studies, 4(1), 2017, 146-153. Working Papers:17/13, 1-31. 19. Geissdoerfer, M., Savaget, P., Bocken, 30. Kim, M., & Lee, S. (2018). Test for tail N. M. P., & Hultink, E. J. (2017). The index constancy of GARCH innovations circular economy - A new sustainability based on conditional volatility. Annals of the paradigm? Journal of Cleaner Producation, Institute of Statistical Mathematics, 71(4), 143(b), 757-768. 947-981. 20. Ghorbel, A., & Attafi, Z. (2014). 31. Kouki, I., Harrathi, N., & Haque, M. Dependence between stock markets of (2011). A volatility spillover among sector MENA countries after sub-prime crisis using index of international stock markets. Journal bivariate extreme value theory. International of money, investment and banking, 22(1), 32- Journal of Applied Management Science, 45. 6(4), 343-364. 32. Koutmos, D. (2012). Time-varying 21. Girard, E. & Omran, M. (2009). On the behavior of stock prices, volatility dynamics relationship between trading volume and and beta risk in industry sector indices of the stock price volatility in CASE. International Shanghai Stock Exchange. Accounting & Journal of Managerial Finance, 5(1), 110- Finance Research, 1(2), 109-125. 134. 33. Kumar, A. & Biswal, S. K. (2019). 22. Gupta, R. K., Jindal, N., & Gupta, A. Impulsive clustering and leverage effect of (2014). Conditional volatility and stock emerging stock market with special reference ISSN: 2367-8925 230 Volume 5, 2020
International Journal of Economics and Management Systems Roni Bhowmik, Shouyang Wang http://www.iaras.org/iaras/journals/ijems to Brazil, India, Indonesia, and Pakistan. financial crisis. 1-22. Journal of Advanced Research in Dynamical http://www.lse.ac.uk/europeanInstitute/resear and Control Systems, 11(Special Issue), 33- ch/hellenicObservatory/pdf/4th_%2 37. 0Symposium/PAPERS_PPS/APPLIED_ECO 34. Leung, M. T., Daouk, H., & Chen, A. S. NOMICS/NEOKOSMIDIS.pdf (2000). Forecasting stock indices: A 44. Olowe, R. A. (2009). Stock return comparison of classification and level volatility, global financial crisis and the estimation models. International Journal of monthly seasonal effect on the Nigerian stock Forecasting, 16(2), 173-190. exchange. African Review of Money Finance 35. Li, W., & Wang, S. S. (2013). Empirical and Banking, 73-107. studies of the effect of leverage industry 45. Okičić, J. (2015). An empirical analysis characteristics. WSEAS Transactions on of stock returns and volatility: the case of Business and Economics, 10(4), 306–315. stock markets from Central and Eastern 36. Liu, H. C., & Hung, J. C. (2010). Europe. South East European Journal of Forecasting S&P-100 stock index volatility: Economics and Business, 9(1), 7-15. The role of volatility asymmetry and 46. Pati, P. C., Barai, P., & Rajib, P. (2017). distributional assumption in GARCH models. Forecasting stock market volatility and Expert Systems with Applications, 37(7), information content of implied volatility 4928-4934. index. Applied Economics, 50(23), 2552– 37. Lum, Y.C. & Islam, S. M. N. (2016). 2568. Time varying behavior of share returns in 47. Peng, C. L., Chung, C. F., Tsai, C. C., & Australia: 1988–2004. Review of Pacific Wang, C. T. (2017). Exploring the returns Basin Financial Markets and Policies, 19(1), and volatility spillover effect in Taiwan and 1650004-14. Japan stock markets. Asian Economic and 38. Mamtha, D., & Srinivasan, K. S. (2016). Financial Review, 7(2), 175-187. Stock market volatility: Conceptual 48. Princ, M. (2010). Relationship between perspective through literature survey. Czech and European developed stock Mediterranean Journal of Social Sciences, markets: DCC MVGARCH analysis. Charles 7(1), 208-212. University Prague, Faculty of Social 39. Maniya, R. S., & Magnusson, F. (2010). Sciences, Institute of Economic Studies, Bear Periods Amplify Correlation: A Working Papers IES 2010/09, 1-35. GARCH BEKK Approach. rapport nr.: 49. Purohit, H., Chhatwal, H., & Puri, H. Master Degree Project 2010: 129, 1-55. (2014). An empirical investigation of https://gupea.ub.gu.se/bitstream/2077/22675/ volatility of the stock market in India. Pacific 1/gupea_2077_22675_1.pdf Business Review International, 7(4), 64-73. 40. Maheshchandra, J. P. (2012). Long 50. Rao, A. (2008). Analysis of volatility memory property in return and volatility: persistence in Middle East emerging equity Evidence from the Indian stock markets. markets. Studies in Economics and Finance, Asian Journal of Finance & Accounting, 4(2), 25(2), 93-111. 218-230. 51. Reddy, Y. V., & Narayan, P. (2016). 41. Nadhem, S., Samira, C., & Nejib, H. Literature on stock returns: A content (2015). Forecasting returns on a stock market analysis. Amity Journal of Finance, 1(1), 194- using Artificial Neural Networks and 207. GARCH family models: Evidence of stock 52. Shalini, A. P. (2014). An emperical market S&P 500. Decision Science Letters, study of volatility of sectoral indices (India). 4(2), 203-210. Indian Research Journal, 1(4). 42. Nelson, D. B. (1991). Conditional 53. Shanthi, A. & Thamilselvan, R. (2019). heteroskedasticity in asset returns: A new Univariate GARCH models applied to the approach. Econometrica, 59(2), 347-370. bombay stock exchange and national stock 43. Neokosmidis, I. (2009). Econometric exchange stock indices. International analysis of realized volatility: evidence of ISSN: 2367-8925 231 Volume 5, 2020
International Journal of Economics and Management Systems Roni Bhowmik, Shouyang Wang http://www.iaras.org/iaras/journals/ijems Journal of Management and Business Shijian/System Engineering Theory and Research, 9(4), 22-33. Practice, 36(9), 2205-2215. 54. Singh, P., Kumar, B., & Pandey, A. 64. Yao, J., Tan, C. L. (2000). A case study (2008). Price and volatility spillovers across on using neural networks to perform technical North American, European and Asian stock forecasting of forex. Neurocomputing, 34(1- markets: With special focus on Indian stock 4), 79-98. market. Indian Institute of Management 65. Yong, F. T., Holmes, M. & Choi, D. Ahmedabad, 1-45. (2011). Volatility transmission and 55. Scott, L. O. (1991). Financial market asymmetric linkages between the stock and volatility: A survey. Staff Papers foreign exchange markets: A sectoral (International Monetary Fund), 38(3), 582- analysis. Studies in Economics and Finance, 625. 28(1), 36-50. 56. Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339. 57. Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence-informed management knowledge by means of systematic review. British Journal of Management, 14(3), 207- 222. 58. Tripathy, T., & Gil-Alana, L. A. (2010). Suitability of volatility models for forecasting stock market returns: A study on the Indian National Stock Exchange. American Journal of Applied Sciences, 7(11), 1487-1494. 59. Varughese, A. & Mathew, T. (2017). Asymmetric volatility of the Indian stock market and foreign portfolio investments: An empirical study. Indian Journal of Finance, 11(6), 36-49. 60. Walid, C., Chaker, A., Masood, O., & Fry, J. (2011). Stock market volatility and exchange rates in emerging countries: A Markov-state switching approach. Emerging Markets Review, 12(3), 272–292. 61. Wang, Y. C., Tsai, J. J., & Li, X. (2019). What drives China’s 2015 stock market Surges and Turmoil? Asia-Pacific Journal of Financial Studies, 48(3), 410–436. 62. Wong, A., & Cheung, K. Y. (2011). Measuring and visualizing the asymmetries in stock market volatility: Case of Hong Kong. International Research Journal of Applied Finance, 2(1), 1-26. 63. Yang, J., Feng, Y., & Wang, H. (2016). Estimation of volatility of CSI 300 index based on regime switching PTTGARCH model. Xitong Gongcheng Lilun yu ISSN: 2367-8925 232 Volume 5, 2020
You can also read
