Modeling volatility on the Karachi Stock Exchange, 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.

Time Series Analysis of Stock Market Volatility in Pakistan

“…Waqar [23] confirmed that both EGARCH and TARCH models were best to capture the leverage (asymmetric) effect of KSE. Furthermore, Akhter and Khan [24] confirmed that daily, weekly and monthly KSE-100 Index return series showed non-normal distribution, stationarity and volatility clustering. They also found that EWMA model was appropriate to measure the volatility level in the monthly series, P-GARCH (1, 1) model in case of daily returns, while the GARCH (1, 1) model for weekly data.…”

Impact of market anomalies on stock exchange: a comparative study of KSE and PSX

“…Javaria and Hassan [34] revealed the nonexistence of herd behaviour in the daily and monthly stock returns of Karachi Stock Exchange. Akhtar and Khan [3] analysed the volatility of KSE-100 index. By using ARCH and GARCH models, the study suggested that weekly, daily and monthly stock returns show volatility, stationarity and nonnormal distribution of KSE returns.…”

Performance of Shariah-Compliant Equity Portfolio Using Model-Based Return and Risk Estimation

“…The application of the ARIMA model is supported by Mondal, Shit, and Goswami (2014) that studied the ARIMA model efficiency for various Indian stock sectors and found that the precision of the ARIMA model in the equity prices prediction is exceeding 85%, means that ARIMA model provides sound predictability. Furthermore, the GARCH model is widely used by a large number of researchers for estimating volatility in considering various characteristics of the data (Akhtar & Khan, 2016;Fabozzi, Tunaru, & Wu, 2004). Abdalla (2012) used the GARCH model to analyze the variability in Saudi equity returns and it provides good evidence of the time persistence differing volatility.…”