Modeling volatility on the Karachi Stock Exchange, Pakistan
| Date | 01 August 2016 |
| Pages | 253-275 |
| Published date | 01 August 2016 |
| DOI | https://doi.org/10.1108/JABS-05-2015-0060 |
| Author | Shahan Akhtar,Naimat U. Khan |
| Subject Matter | Strategy,International business |
Modeling volatility on the Karachi Stock
Exchange, Pakistan
Shahan Akhtar and Naimat U. Khan
Shahan Akhtar is a
Post-Graduate Scholar
and Naimat U. Khan is
Assistant Professor both
are based at Institute of
Management Studies,
University of Peshawar,
Peshawar, Pakistan.
Abstract
Purpose –The current paper aims to fill a gap in the literature by analyzing the nature of volatility on the
Karachi Stock Exchange (KSE) 100 index of the KSE, and develop an understanding as to which model
is most suitable for measuring volatility among those used. The study contributes significantly to the
literature as, compared with the limited previous studies of Pakistan undertaken in the past, it covers
three types of data (i.e. daily, weekly and monthly) for the whole period from the introduction of the KSE
100 index on November 2, 1991 to December 31, 2013. In addition, to analyze the impact of global
financial crises upon volatility, the data have been divided into pre-crisis (1991-2007) and post-crisis
(2008-2013) periods.
Design/methodology/approach –This study has used an advanced set of volatility models such as
autoregressive conditional heteroskedasticity [ARCH (1)], generalized autoregressive conditional
heteroskedasticity [GARCH (1, 1)], GARCH in mean [GARCH-M (1, 1)], exponential GARCH [E-GARCH
(1, 1)], threshold GARCH [T-GARCH (1, 1)], power GARCH [P-GARCH (1, 1)] and also a simple
exponentially weighted moving average (EWMA) model.
Findings –The results reveal that daily, weekly and monthly return series show non-normal distribution,
stationarity and volatility clustering. However, the heteroskedasticity is absent only in the monthly
returns making only the EWMA model usable to measure the volatility level in the monthly series. The
P-GARCH (1, 1) model proved to be a better model for modeling volatility in the case of daily returns,
while the GARCH (1, 1) model proved to be the most appropriate for weekly data based on the Schwarz
information criterion (SIC) and log likelihood (LL) functionality. The study shows high persistence of
volatility, a mean reverting process and an absence of a risk premium in the KSE market with an
insignificant leverage effect only in the case of weekly returns. However, a significant leverage effect is
reported regarding the daily series of the KSE 100 index. In addition, to analyze the impact of global
financial crises upon volatility, the findings show that the subperiods demonstrated a slightly low
volatility and the global economic crisis did not cause a rise in volatility levels.
Originality/value –Previously, the literature about volatility modeling in Pakistan’s markets has been
limited to a few models of relatively small sample size. The current thesis has attempted to overcome
these limitations and used diverse models for three types of data series (daily, weekly and monthly). In
addition, the Pakistani economy has been beset by turmoil throughout its history, experiencing a range
of shocks from the mild to the extreme. This paper has measured the impact of those shocks upon the
volatility levels of the KSE.
Keywords Pakistan, Global financial crisis, GARCH, Volatility, ARCH, EWMA
Paper type Research paper
1. Introduction
Financial markets play a very significant role in the economic development of countries by
providing smooth channels via which financial resources can flow easily from the saving
sector of an economy to the users of capital. These markets provide a way to access the
economic prosperity and the economic health of the country. The subject matter of the
current study is a financial market of the Karachi Stock Exchange (KSE) of Pakistan where
light is thrown on the modeling and understanding of the volatile behavior of the KSE 100
index. Volatility is a common behavior on stock exchanges all over the world. It is the
fluctuation – the up and down movement – in the price of shares. The presence of volatility
Received 20 May 2015
Revised 20 October 2015
Accepted 5 January 2016
DOI 10.1108/JABS-05-2015-0060 VOL. 10 NO. 3 2016, pp. 253-275, © Emerald Group Publishing Limited, ISSN 1558-7894 JOURNAL OF ASIA BUSINESS STUDIES PAGE 253
makes it possible to earn higher returns, but the fact is that a high level of volatility also
leads to hesitancy amongst investors as it creates more risk for them and also leads to
inefficiency in the market. So, a low level of volatility is preferred because that makes
investors able to trade more easily without confronting large fluctuations in prices. Along
with the conservative attitudes of investors, high levels of volatility also generate uncertainty
in the market and disturb the prices of stock such that they are not always truly
representative of the intrinsic value of firms[1]. Similarly, investors also keep their eyes on
volatility levels with respect to their future returns, influencing their investment behavior[2]
(Hameed and Ashraf, 2006;Mittal and Goyal, 2012).
Aside from the importance of being able to estimate volatility, there were various other
motivations for undertaking the current study. The literature shows that there have been
many more studies that have attempted to model conditional volatility in developed markets
when compared with emerging markets (Ahmed and Suliman, 2011;Mittal and Goyal,
2012). Emerging stock markets such as the KSE in Pakistan have not received much
attention in this respect[3]. In addition, to the best of our knowledge, previous studies of the
Pakistani market have not considered long periods of time. The current study has taken a
longer period of time than its predecessors – it ranges from November 2, 1991 to
December 31, 2013 – to build a more comprehensive picture of the volatile nature of the
KSE[4]. Another motivation for the current work has been to include weekly and monthly
returns in its deliberations, as past studies have focused on daily returns only. The current
study has also considered a range of models with a variety of specifications which were
less well-researched by previous studies concerning Pakistan. In addition, a series of
global financial crises (such as the Asian financial crisis of 1997, the dot-com crash of 2001
and the global financial crisis of 2008-2009) have motivated the researchers to analyze
volatility on the KSE. These motivations have led to a number of research questions. First,
what type of volatility behavior and what characteristics of the KSE 100 index return series
are observed as part of its volatility? Second, does the KSE offer risk premiums to its
investors or not? Third, is it good or bad news that leads to volatility, i.e. does good news
(positive returns) or bad news (negative returns) lead to greater volatility, which would verify
the existence of a leverage effect? Finally, which model, from among those used, better
explains the volatility behavior of the Pakistani Stock Market?
Thus, the current paper aims to shed light on what the different characteristics that the
daily, weekly and monthly return series from the KSE 100 index represent, and it intends to
comprehend the nature of the volatility on the KSE via the application of different volatility
models (Shah et al., 2012). One of the objectives of this research is to examine, investigate
or check for the presence or absence of risk premiums on the KSE to understand whether
risk premiums are offered to investors. The current paper also aims to report whether
positive shocks (news) or negative shocks (news) have a greater influence on volatility.
Moreover, the paper has also attempted to determine which models outperform and reflect
market estimates better.
For the purpose of estimating volatility and to understand its connected aspects, a variety
of models have been developed over time to model the conditional volatility of financial time
series data. Among these, conditional heteroskedasticity models are very popular (Ahmed
and Suliman, 2011). The current study has focused on the application of such models and
includes symmetric[5] models [the autoregressive conditional heteroskedasticity (ARCH)
model, the generalized autoregressive conditional heteroskedasticity (GARCH) model, the
GARCH in mean (GARCH-M) model] and asymmetric[6] models (such as exponential
GARCH [E-GARCH], threshold GARCH [T-GARCH] and power GARCH [P-GARCH (1, 1)])
for estimating the conditional volatility of the KSE. Aside from these complex models, a
simple EWMA model was also used for the current study. The rest of this paper is structured
as follows. Following the introduction, Section 2 gives a brief overview of the KSE. Section
3 explains the models used. Section 4 elaborates upon the studies conducted by previous
researchers in this area with respect to different markets across the world, including
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