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Value-at-Risk Analysis for Measuring Stochastic Volatility of Stock Returns: Using GARCH-Based Dynamic Conditional Correlation Model

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  • Fahim Afzal
  • Pan Haiying
  • Farman Afzal
  • Asif Mahmood
  • Amir Ikram

Abstract

To assess the time-varying dynamics in value-at-risk (VaR) estimation, this study has employed an integrated approach of dynamic conditional correlation (DCC) and generalized autoregressive conditional heteroscedasticity (GARCH) models on daily stock return of the emerging markets. A daily log-returns of three leading indices such as KSE100, KSE30, and KSE-ALL from Pakistan Stock Exchange and SSE180, SSE50 and SSE-Composite from Shanghai Stock Exchange during the period of 2009–2019 are used in DCC-GARCH modeling. Joint DCC parametric results of stock indices show that even in the highly volatile stock markets, the bivariate time-varying DCC model provides better performance than traditional VaR models. Thus, the parametric results in the DCC-GRACH model indicate the effectiveness of the model in the dynamic stock markets. This study is helpful to the stockbrokers and investors to understand the actual behavior of stocks in dynamic markets. Subsequently, the results can also provide better insights into forecasting VaR while considering the combined correlational effect of all stocks.

Suggested Citation

  • Fahim Afzal & Pan Haiying & Farman Afzal & Asif Mahmood & Amir Ikram, 2021. "Value-at-Risk Analysis for Measuring Stochastic Volatility of Stock Returns: Using GARCH-Based Dynamic Conditional Correlation Model," SAGE Open, , vol. 11(1), pages 21582440211, March.
    • Handle: RePEc:sae:sagope:v:11:y:2021:i:1:p:21582440211005758
    DOI: 10.1177/21582440211005758
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    3. Minglian Lin & Indranil SenGupta & William Wilson, 2023. "Estimation of VaR with jump process: application in corn and soybean markets," Papers 2311.00832, arXiv.org, revised Jun 2024.
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    6. Salim Hamza Ringim & Abdulkareem Alhassan & Hasan Güngör & Festus Victor Bekun, 2022. "Economic Policy Uncertainty and Energy Prices: Empirical Evidence from Multivariate DCC-GARCH Models," Energies, MDPI, vol. 15(10), pages 1-18, May.

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