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The volatility of bank stock prices and macroeconomic fundamentals in the Pakistani context: an application of GARCH and EGARCH models

Author

Listed:
  • Muhammad Mohsin

    (Hunan University of Humanities, Science and Technology (HUHST), China)

  • Li Naiwen

    (Liaoning Technical University, China)

  • Muhammad Zia-UR-Rehman

    (National Textile University, Pakistan)

  • Sobia Naseem

    (Shijiazhuang Tiedao University, China)

  • Sajjad Ahmad Baig

    (National Textile University, Pakistan)

Abstract

Research Background: The banking sector plays a crucial role in the world’s economic development. This research paper evaluates the volatility spillover, symmetric, and asymmetric effects between the macroeconomic fundamentals, i.e., market risks, interest rates, exchange rates, and bank stock returns, for the listed banks of Pakistan. Purpose of the article: The main purpose of this study is to examine the volatility of Pakistani banking stock returns due to the influence of market risk, interest rates, and exchange rates. Pakistan is selected for the study because the volatility of its banking stock returns is strongly influential in achieving sustainable economic development. Methods: By applying the OLS with the Heteroskedasticity and Autocorrelation Consistent (HAC) covariance matrix, the GARCH (1, 2), and the EGARCH (1, 1), analysis is conducted for the period from January 1, 2009 to December 31, 2019 using samples of 13 listed banks. Findings & Value added: The ARCH parameter is significant in the OLS with the HAC covariance matrix estimation, which is a clear indication of the existence of heteroskedasticity in the squared residuals and the inaccuracy of the OLS with the HAC covariance matrix. The results of the OLS with the HAC covariance matrix suggest using the GARCH model family to accurately measure the volatility of bank stock prices. The results of the mean equation in the GARCH (1, 2) and EGARCH (1, 1) indicate the positive significance of market risk and the low significance of interest and exchange rates, confirming that market returns strongly affect the sensitivity of bank stock returns compared to interest and exchange rates. It should be noted that the ARCH (?) and GARCH (ß) parameters of the variance equation fulfill the non-negative conditions of the GARCH model. Furthermore, the leverage parameter (?) is found to be positively significant for all banks, and volatility is found to be influenced by positive shocks compared to negative shocks. Conclusively, it can be stated that market returns determine the dynamics of the conditional returns of bank stocks. Nevertheless, the interest and exchange rate volatilities determine the conditional bank stock returns’ volatility.

Suggested Citation

  • Muhammad Mohsin & Li Naiwen & Muhammad Zia-UR-Rehman & Sobia Naseem & Sajjad Ahmad Baig, 2020. "The volatility of bank stock prices and macroeconomic fundamentals in the Pakistani context: an application of GARCH and EGARCH models," Oeconomia Copernicana, Institute of Economic Research, vol. 11(4), pages 609-636, December.
  • Handle: RePEc:pes:ieroec:v:11:y:2020:i:4:p:609-636
    DOI: 10.24136/oc.2020.025
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    Citations

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    Cited by:

    1. Budi Setiawan & Marwa Ben Abdallah & Maria Fekete-Farkas & Robert Jeyakumar Nathan & Zoltan Zeman, 2021. "GARCH (1,1) Models and Analysis of Stock Market Turmoil during COVID-19 Outbreak in an Emerging and Developed Economy," JRFM, MDPI, vol. 14(12), pages 1-19, December.
    2. Lu, Linna & Lei, Yalin & Yang, Yang & Zheng, Haoqi & Wang, Wen & Meng, Yan & Meng, Chunhong & Zha, Liqiang, 2023. "Assessing nickel sector index volatility based on quantile regression for Garch and Egarch models: Evidence from the Chinese stock market 2018–2022," Resources Policy, Elsevier, vol. 82(C).
    3. Muddassar Sarfraz & Muhammad Mohsin & Sobia Naseem & Amit Kumar, 2021. "Modeling the relationship between carbon emissions and environmental sustainability during COVID-19: a new evidence from asymmetric ARDL cointegration approach," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(11), pages 16208-16226, November.
    4. Larisa Ivascu & Muddassar Sarfraz & Muhammad Mohsin & Sobia Naseem & Ilknur Ozturk, 2021. "The Causes of Occupational Accidents and Injuries in Romanian Firms: An Application of the Johansen Cointegration and Granger Causality Test," IJERPH, MDPI, vol. 18(14), pages 1-17, July.
    5. Elena Villar-Rubio & María-Dolores Huete-Morales & Federico Galán-Valdivieso, 2023. "Using EGARCH models to predict volatility in unconsolidated financial markets: the case of European carbon allowances," Journal of Environmental Studies and Sciences, Springer;Association of Environmental Studies and Sciences, vol. 13(3), pages 500-509, September.
    6. Muhammad MOHSIN & Sobia NASEEM & Larisa IVAȘCU & Lucian-Ionel CIOCA & Muddassar SARFRAZ & Nicolae Cristian STĂNICĂ, 2021. "Gauging the Effect of Investor Sentiment on Cryptocurrency Market: An Analysis of Bitcoin Currency," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 87-102, December.

    More about this item

    Keywords

    bank stock return; OLS-HAC; GARCH; EGARCH;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • O11 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Macroeconomic Analyses of Economic Development

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