Financial contagion analysis on asset return of S&P 500 Index, Shanghai Index, and Hang Seng Index with Jakarta Composite Index for the period 2017 – 2023

Purpose: Using the co-volatility contagion test investigation method developed by Fry-McKibbin (2018), this study analyzes financial contagion, which is how risk and instability spread from one market to another, especially during periods of economic crises. Method: The subject of this research is the asset return of four global stock markets represented by the S&P 500 Index (SPX, United States), the Shanghai Composite Index (SCI, China), the Hang Seng Index (HSI, Hong Kong), and the Jakarta Composite Index (JCI/JCI, Indonesia) from January 2017 to December 2023. Results: Utilizing the contagion co-volatility test developed by Fry-McKibbin (2018), this study successfully finds contagion from the SPX, SCI, and HSI indices to the JCI index with varying co-volatility values. The difference in co-volatility values between the SPX and JCI indices is significantly positive; it decreases during a crisis, meaning that both indices tend to move in the same direction and independently during periods of global uncertainty. Meanwhile, the difference in the co-volatility values between the SCI and JCI indices is significantly negative, indicating that the index tends to move in opposite directions during a crisis or independently. However, the magnitude is small during non-crisis and crisis periods. For the HSI and JCI, the difference in the co-volatility values is also significantly negative and decreases. This shows that the Hong Kong and Indonesian markets are becoming less connected with each market moving in opposite directions, reflecting different sensitivities to global uncertainty risk. These differences in market characteristics can provide valuable insights for market participants, investors, and policymakers, enhancing their understanding of risk preferences and volatility transmission between stock markets, thereby supporting informed investment decision making based on historical data analysis.