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Modeling volatility on the Karachi Stock Exchange, Pakistan

Modeling volatility on the Karachi Stock Exchange, Pakistan PurposeThe current paper aims to fill a gap in the literature by analyzing the nature of volatility on the KSE 100 index of the Karachi Stock Exchange (KSE) and it also develops 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, as 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 has been divided into pre-crisis (1991-2007) and post-crisis (2008-2013) periods.Design/methodology/approachThis study has employed an advanced set of volatility models such as autoregressive conditional heteroscedasticity [ARCH (1)], generalized autoregressive conditional heteroscedasticity [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.FindingsThe results reveal that daily, weekly and monthly return series show non-normal distribution, stationarity and volatility clustering. However, the heteroscedasticity 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 a 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/valuePreviously, 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 employed 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Asia Business Studies Emerald Publishing

Modeling volatility on the Karachi Stock Exchange, Pakistan

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
1558-7894
DOI
10.1108/JABS-05-2015-0060
Publisher site
See Article on Publisher Site

Abstract

PurposeThe current paper aims to fill a gap in the literature by analyzing the nature of volatility on the KSE 100 index of the Karachi Stock Exchange (KSE) and it also develops 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, as 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 has been divided into pre-crisis (1991-2007) and post-crisis (2008-2013) periods.Design/methodology/approachThis study has employed an advanced set of volatility models such as autoregressive conditional heteroscedasticity [ARCH (1)], generalized autoregressive conditional heteroscedasticity [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.FindingsThe results reveal that daily, weekly and monthly return series show non-normal distribution, stationarity and volatility clustering. However, the heteroscedasticity 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 a 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/valuePreviously, 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 employed 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.

Journal

Journal of Asia Business StudiesEmerald Publishing

Published: Aug 1, 2016

References