Time Varying Volatility Modeling of Pakistani and leading foreign stock markets

Ghouse, Ghulam and Khan, Saud Ahmed and Arshad, Muhammad (2015): Time Varying Volatility Modeling of Pakistani and leading foreign stock markets.

Abstract

This study estimates the volatility of Pakistani and leading foreign stock markets. Daily data are used from nine international equity markets (KSE 100, NIKKEI 225, HIS, S&P 500, NASDAQ 100, DOW JONES, GADXI, FTSE 350 and DFMGI) for the period of Jan, 2005 to Nov, 2014. The whole data set is used for modeling of time varying volatility of stock markets. Univariate GARCH type models i.e. GARCH and GJR are employed for volatility modeling of Pakistani and leading foreign stock markets. The residual analysis also employed to check the validity of models. 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.

• Item Type: MPRA Paper
• Language: English
• Keywords: Volatility, Equity Market, GARCH and GJR
• Subjects: G - Financial Economics > G0 - General; G - Financial Economics > G0 - General > G01 - Financial Crises; G - Financial Economics > G1 - General Financial Markets; G - Financial Economics > G1 - General Financial Markets > G15 - International Financial Markets

References

Conclusion

This study has offered a framework to model the time varying volatility of equity markets by employing the risk models. On the basis of given data sets we employed symmetric GARCH and asymmetric GARCH models to estimate conditional mean equations follow ARMA process and conditional variance equations for risk (dispersion). For the validity of models the residual diagnostic test also employed. KSE 100 and NIKKEI 225 series have asymmetric effect while other series take symmetric effects. The persistence of shock is measure to specify the period of persistence of ARCH and GARCH effect in return series. The leverage effects are also quantified to check the effects of different news on volatility.

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