Testing volatility spillovers using GARCH models in the Japanese stock market during COVID-19

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This paper investigates volatility spillovers in the stock market in Japan during the COVID-19 pandemic by using GARCH family models. The empirical analysis is focused on the dynamics of the NIKKEI 225 stock market index during the sample period from July 30, 1998, to January 24, 2022. In other words, the sample period covers both the period of the global financial crisis (GFC) and the COVID-19 pandemic. The econometrics includes GARCH (1,1), GJR (1,1), and EGARCH (1,1) models. By applying GARCH family models, this empirical study also examines the long-term behavior of the Japanese stock market.
The Japanese stock market is much more stable and efficient than emerging or frontier markets characterized by higher volatility and lower liquidity. The paper establishes that NIKKEI 225 index dynamics is different in intensity in the case of the two most recent extreme events analyzed, namely the global financial crisis (GFC)of 2007–2008 and the COVID-19 pandemic. The findings confirmed the presence of the leverage effect during the sample period. Moreover, the empirical results identified the presence of high volatility in the sample returns of the selected stock market. Nevertheless, the econometric framework showed that the negative implications of the GFC were much more severe and caused more significant contractions compared to the COVID-19 pandemic for the Japanese stock market. This study contributes to the existing literature by providing additional empirical evidence on the long-term behavior of the stock market in Japan, especially in the context of extreme events.

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    • Figure 1. The trend of the NIKKEI 225 index using daily closing prices during the sample period
    • Figure 2. The log-returns of the NIKKEI 225 index using daily closing prices during the sample period
    • Figure 3. Evidence on autocorrelation and partial autocorrelation tested for NIKKEI 225 index during the sample period
    • Figure 4. Relative frequency distribution for NIKKEI 225 index during the sample period
    • Table 1. Frequency distribution for NIKKEI 225 index
    • Table 2. Summary statistics
    • Table 3. Statistical property of GARCH (1, 1), GJR (1, 1), and EGARCH (1, 1) models
    • Conceptualization
      Cristi Spulbar, Jatin Trivedi
    • Data curation
      Cristi Spulbar, Ramona Birau, Elena Loredana Minea
    • Formal Analysis
      Cristi Spulbar, Ramona Birau, Jatin Trivedi, Iqbal Thonse Hawaldar
    • Validation
      Cristi Spulbar, Ramona Birau, Jatin Trivedi, Iqbal Thonse Hawaldar
    • Writing – original draft
      Cristi Spulbar, Jatin Trivedi, Elena Loredana Minea
    • Writing – review & editing
      Cristi Spulbar, Ramona Birau, Iqbal Thonse Hawaldar
    • Supervision
      Ramona Birau
    • Visualization
      Ramona Birau, Jatin Trivedi, Iqbal Thonse Hawaldar , Elena Loredana Minea
    • Software
      Jatin Trivedi, Iqbal Thonse Hawaldar , Elena Loredana Minea
    • Methodology
      Iqbal Thonse Hawaldar , Elena Loredana Minea
    • Investigation
      Elena Loredana Minea
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Investment Management and Financial Innovations
Investment Management and Financial Innovations