Modeling and Forecasting Volatility in Pakistan Stock Exchange

Authors

  • Muhammad Shahzeb Ali Department of statistics, international Islamic university, Islamabad, Pakistan, 46000
  • Atiya Javed Department of statistics, Quaid-I-Azam university, Islamabad, Pakistan, 46000

Keywords:

Expanding window, COVID-19, Financial timeseries, Volatility

Abstract

Analysis of time series is used to develop simple models which can forecast, interpret, and analyze the result concerning its field of application. The main purpose of this study was to check and model the volatility in Pakistan Stock Exchange (PSX) for the near future. The stock exchange data are highly volatile and there are many factors which affect the daily market returns. A developing country like Pakistan is interfered by the conditions of International Monetary Fund (IMF), dollar fluctuations and thus, it becomes very difficult to forecast these returns due to continuous effect of above and many other factors (which are unknowns). Generally, in the presence of these factors, the naive or simple models do not perform efficiently because of their ability to model averages and thus volatility models are used which model variation in the data. We used dataset from 2nd June 2006 to 23rd August 2020 using rolling window technique and find GARCH (3,2) as best model to forecast PSX returns.

References

. Ahsanuddin, M., Fraz, T. R., Fatima, S., et al. “Studying the volatility of pakistan stock exchange and shanghai stock exchange markets in the light of cpec: An application of garch and egarch modelling.” International Journal of Sciences, vol. 8(03), pp 125-132, 2019.

. HUSSAIN, A., HUSSAIN, A. B., and ALI, S. “The impact of interest rate volatility on stock returns volatility: Empirical evidence from pakistan stock exchange.” International journal of theoretical and applied finance, vol. 31, pp 47-57, 2017.

. Irfan Malik, M. and Rashid, A.“Do outliers matter in return and volatility linkages? A case of sectoral stock of psx and brent oil.” European Online Journal of Natural and Social Sciences, vol. 8(2 (s)), pp 44, 2019.

. Nasr, N., Farhadi Sartangi, M., and Madahi, Z. “A fuzzy random walk technique to forecasting volatility of iran stock exchange index.” Advances in Mathematical Finance and Applications, vol.4(1). pp 15-30, 2019.

. Irshad, H.“Relationship among political instability, stock market returns and stock market volatility.” Studies in business and economics, vol. 12(2). Pp 70-99, 2017.

. Baillie, R. T. and Myers, R. J.“Bivariate garch estimation of the optimal commodity futures hedge.” Journal of Applied Econometrics, vol. 6(2). Pp 109-124, 1991.

. Lux, T. and Marchesi, M.“Volatility clustering in financial markets: a microsimulation of interacting agents.” International journal of theoretical and applied finance, vol. 3(04). Pp 675-702, 2000.

. Yu, J.“Forecasting volatility in the new zealand stock market.” Applied Financial Economics, vol. 12(3), pp 193-202, 2002.

. Bollerslev, T.“Generalized autoregressive conditional heteroskedasticity.” Journal of econometrics, vol. 31(3), pp 307-327, 1986.

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Published

2020-09-20

How to Cite

Ali, M. S. ., & Javed, A. . (2020). Modeling and Forecasting Volatility in Pakistan Stock Exchange. International Journal of Sciences: Basic and Applied Research (IJSBAR), 54(1), 234–241. Retrieved from https://gssrr.org/index.php/JournalOfBasicAndApplied/article/view/11744

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