Stock price volatility during the COVID-19 pandemic: The GARCH model

  • Received August 19, 2021;
    Accepted September 20, 2021;
    Published October 4, 2021
  • Author(s)
  • DOI
  • Article Info
    Volume 18 2021, Issue #4, pp. 12-20
  • Cited by
    2 articles

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This work is licensed under a Creative Commons Attribution 4.0 International License

This study examined the response of stock prices on the Indonesia Stock Exchange (IDX) to COVID-19 using an event study approach and the GARCH model. The research sample is the closing price of the Composite Stock Price Index (JCI) and companies that are members of LQ-45 in the 40-day period before the COVID-19 incident, 1 day during the COVID-19 incident (March 2, 2020) and 10 days after, January 6, 2020 – March 16, 2020. Empirical findings prove that abnormal returns react negatively to COVID-19, JCI volatility fluctuates widely during the COVID-19 event, and the GARCH(1,2) model can be used to assess volatility and predict stock abnormal returns in IDX in market conditions infected with COVID-19. The practical implication of the study’s findings for investors is that the COVID-19 event caused stock price volatility, which affects abnormal returns. Therefore, to face the conditions of uncertainty and increased volatility in the future, several lines of risk management are needed in managing a stock portfolio. In addition, it also opens up opportunities for speculators to profit in an inefficient market environment. This study is based on the empirical literature currently being developed to investigate the phenomenon of stock price volatility behavior during COVID-19 on the IDX. The GARCH model used proves that during the COVID-19 pandemic, stock price volatility increases and leads to a decrease in abnormal returns. The empirical findings also validate the efficient market hypothesis theory related to the study of events and the theory of financial behavior related to uncertainty.

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    • Figure 1. Framework
    • Figure 2. Observation period
    • Table 1. Calculation of the daily AAR of shares during the observation period
    • Table 2. Hypothesis testing results using the ttest
    • Table 3 Abnormal return results
    • Table 4 Volatility results
    • Table 5. Result of the determination coefficient test (R2 Test) of the GARCH (1,2) model
    • Table 6. T-statistical test results
    • Conceptualization
      Endri Endri, Widya Aipama
    • Formal Analysis
      Endri Endri, Renil Septiano
    • Methodology
      Endri Endri, Widya Aipama
    • Validation
      Endri Endri
    • Writing – review & editing
      Endri Endri
    • Visualization
      Widya Aipama, A. Razak
    • Writing – original draft
      Widya Aipama
    • Data curation
      A. Razak
    • Investigation
      A. Razak, Laynita Sari, Renil Septiano
    • Resources
      A. Razak, Laynita Sari
    • Funding acquisition
      Laynita Sari
    • Supervision
      Laynita Sari
    • Project administration
      Renil Septiano
    • Software
      Renil Septiano