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This study traces the degree of integration and volatility spillover effect between the Pakistani and leading foreign stock markets by analyzing the Meteor shower hypothesis. Daily data are used from nine worldly equity markets (KSE 100, NIKKEI 225, HIS, S&P 500, NASDAQ 100, DOW JONES, GADXI, FTSE 350 and DFMGI) for the period of 2005 to 2014. First, we used the whole data set and after that we split data set into two subsets, First subset of data contains the era of global financial crisis of 2008 from 2005 to 2009 and Second subset is after global financial crisis time period from 2010 to 2014 (The global crisis prevailed till end of 2009). By following the Hamao et al. (1990) technique the univariate GARCH type models are employed to explore the dynamic linkages between Pakistani and leading foreign stock markets. The results from whole data set illustrate that there is mixed co‐movements between leading foreign stock markets and Pakistani stock market. The results from both subsets provide an evidence that there is a unidirectional mean and volatility spillover effect from S&P 500, NASDAQ 100, DJI and DFMGI to KSE 100. Also we found bidirectional spillover effect between DFMGI and KSE 100 from both subsets of data. We concluded that there is only one indirect linkage through which may the information transmitted to KSE 100. This linkage is developed due to the co‐movement among KSE 100, DFMGI and NASDAQ 100 in crisis period. This integration between these markets may provide a sign of indirect linkage. It also exhibits the volatility in Pakistan stock market returns is instigated through direct effects as well as indirect effects. Our study brings important conclusions for financial institutions, portfolio managers, market players and academician to diagnose the nature and level of linkages between the financial markets.

In conventional Econometrics, the unit root and cointegration analysis are the only ways to circumvent the spurious regression which may arise from missing variable (lag values) rather than the nonstationarity process in time series data. We propose the Ghouse equation solution of autoregressive distributed lag mechanism which does not require additional work in unit root testing and bound testing. This advantage makes the proposed methodology more efficient compared to the existing cointegration procedures. The earlier tests weaken their position in comparison to it, as they had numerous linked testing procedures which further increase the size of the test and/or reduce the test power. The simplification of the Ghouse equation does not attain any such type of error, which makes it a more powerful test as compared to widely cited exiting testing methods in econometrics and statistics literature.

This paper attempts to detect the unavoidable impacts of COVID-19 on geopolitical and financial events related to Islamic banking and the finance sector in Pakistan. It considers only those major events that triggered imbalances in the equity prices of selected Islamic banks. Employed here is the GARCH model, used to predict the volatility series using daily data from January 2007 to July 2020. The Impulse Indicator Saturation (IIS) helps to identify the structural breaks due to COVID-19, as well as the effects of political and financial events on the returns and volatility series of Islamic banks. The results indicate that all the events due to COVID-19 are significant. While 19 out of 21 political and financial events impacted the returns and volatility series, there were only 2 political events out of 18 that showed no significant effect on the returns and the volatility series. The state’s and Islamic banks’ policymakers can use these results to build an effective and sustainable financial policy regarding Islamic finance and the banking sector.

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