Studying the Volatility of Pakistan Stock Exchange and Shanghai Stock Exchange Markets in the Light of CPEC: An Application of GARCH and EGARCH Modelling

Studying the Volatility of Pakistan Stock Exchange and Shanghai Stock Exchange Markets in the Light of CPEC: An Application of GARCH and EGARCH Modelling

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Author(s): Muhammad Ahsanuddin, Tayyab Raza Fraz, Samreen Fatima

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DOI: 10.18483/ijSci.2016 50 195 125-132 Volume 8 - Mar 2019


Globalization in the new millennium has brought a set of profound social, economic and political changes in the world. Pakistan and her neighboring country China have joined hands and has initiated China-Pakistan Economic Corridor (CPEC) which is an ambitious project that focuses on improving connectivity and cooperation among both the neighboring giants. The growing nexus between China and Pakistan through CPEC is going to strengthen their markets as well stock markets. Ultimately both the economies are bound to benefit consequently resulting in increased GDP of both the countries. In this piece of research, we have studied the impact of CPEC on Pakistan Stock Market (PSX) and SSE by employing GARCH and EGARCH model from January 2011 to March 2019 on daily basis which shows recent purchases of up to 45% of Pakistani shares owned by China. Empirical analysis shows that in PSX risk parameter is little bit higher in post-CPEC comparatively pre-CPEC period. Volatility of SSE has high in both pre and post-CPEC period as compared to PSX. It reveals that PSX market after the advent of CPEC is showing stability which is a sign of encouragement for businessmen, traders and investors to invest. Market stability has increased manifold and the findings are supported by Morgan Stanley Capital International (MSCI). Recently Bloomberg has ranked Pakistan amongst the first 5 best performing stocks around the world. The research findings show PSX has a high potential for growth and is expected to become a lucrative market for businessmen, traders and investors.




  1. Alberg, Dima, Shalit, Haim, & Yosef, Rami. (2008). Estimating stock market volatility using asymmetric GARCH models. Applied Financial Economics, 18(15), 1201-1208.
  2. Alexander, Carol. (2009). Market Risk Analysis, Value at Risk Models (Vol. 4): John Wiley & Sons.
  3. Alexander, Carol, & Lazar, Emese. (2004). The equity index skew, market crashes and asymmetric normal mixture GARCH. ISMA Centre discussion papers in Finance, 14.
  4. Babikir, Ali, Gupta, Rangan, Mwabutwa, Chance, & Owusu-Sekyere, Emmanuel. (2012). Structural breaks and GARCH models of stock return volatility: The case of South Africa. Economic Modelling, 29(6), 2435-2443.
  5. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), 307-327.
  6. Chou, Ray Yeutien. (1988). Volatility persistence and stock valuations: Some empirical evidence using GARCH. Journal of Applied Econometrics, 3(4), 279-294.
  7. Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 987-1007.
  8. Islam, Mohd Aminul, & Mahkota, BI. (2013). Estimating volatility of stock index returns by using symmetric GARCH models. Middle-East Journal of Scientific Research, 18(7), 991-999.
  9. Koima, JK, Mwita, PN, & Nassiuma, DK. (2015). Volatility estimation of stock prices using Garch method.
  10. Kovačić, Zlatko. (2007). Forecasting volatility: Evidence from the Macedonian stock exchange.
  11. Lim, Ching Mun, & Sek, Siok Kun. (2013). Comparing the performances of GARCH-type models in capturing the stock market volatility in Malaysia. Procedia Economics and Finance, 5, 478-487.
  12. Malkiel, Burton G. (1979). The capital formation problem in the United States. The Journal of Finance, 34(2), 291-306.
  13. Matei, Marius. (2009). Assessing volatility forecasting models: why GARCH models take the lead. Romanian Journal of Economic Forecasting, 12(4), 42-65.
  14. Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica: Journal of the Econometric Society, 347-370.
  15. Pilbeam, Keith, & Langeland, Kjell Noralf. (2015). Forecasting exchange rate volatility: GARCH models versus implied volatility forecasts. International Economics and Economic Policy, 12(1), 127-142.
  16. Zhang, Ziqi. (2009). Analysis Skewness in GARCH model. School of Economics and Social Sciences, Høgskolan Dalarna.

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International Journal of Sciences is Open Access Journal.
This article is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License.
Author(s) retain the copyrights of this article, though, publication rights are with Alkhaer Publications.

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