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Macroeconomic fundamentals, jump dynamics and expected volatility

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  • Zhiyuan Pan
  • Ruijun Bu
  • Li Liu
  • Yudong Wang

Abstract

In this paper, we develop a new volatility model capturing the effects of macroeconomic variables and jump dynamics on the stock volatility. The proposed GARCH-Jump-MIDAS model is applied to the S&P 500 index. Our in-sample results indicate that macroeconomic activities have important impacts on aggregate market volatility. Out-of-sample evidence suggests that our model with macroeconomic variables significantly outperform a wide range of competitors including the original GARCH(1,1), GARCH-MIDAS and GJR-A-MIDAS models. The volatility timing results also show that the information from jumps and macroeconomic activity is helpful for improving the portfolio performance.

Suggested Citation

  • Zhiyuan Pan & Ruijun Bu & Li Liu & Yudong Wang, 2020. "Macroeconomic fundamentals, jump dynamics and expected volatility," Quantitative Finance, Taylor & Francis Journals, vol. 20(8), pages 1345-1371, August.
    • Handle: RePEc:taf:quantf:v:20:y:2020:i:8:p:1345-1371
    DOI: 10.1080/14697688.2020.1736317
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    Citations

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    Cited by:

    1. Yun-Shi Dai & Peng-Fei Dai & Wei-Xing Zhou, 2024. "The impact of geopolitical risk on the international agricultural market: Empirical analysis based on the GJR-GARCH-MIDAS model," Papers 2404.01641, arXiv.org.
    2. Tong, Yuan & Wan, Ning & Dai, Xingyu & Bi, Xiaoyi & Wang, Qunwei, 2022. "China's energy stock market jumps: To what extent does the COVID-19 pandemic play a part?," Energy Economics, Elsevier, vol. 109(C).
    3. Pan, Zhiyuan & Xiao, Dongli & Dong, Qingma & Liu, Li, 2022. "Structural breaks, macroeconomic fundamentals and cross hedge ratio," Finance Research Letters, Elsevier, vol. 47(PA).
    4. Liu, Feng & Xu, Jie & Ai, Chunrong, 2023. "Heterogeneous impacts of oil prices on China's stock market: Based on a new decomposition method," Energy, Elsevier, vol. 268(C).
    5. Liu, Feng & Shao, Shuai & Li, Xin & Pan, Na & Qi, Yu, 2023. "Economic policy uncertainty, jump dynamics, and oil price volatility," Energy Economics, Elsevier, vol. 120(C).
    6. Dai, Xingyu & Li, Matthew C. & Xiao, Ling & Wang, Qunwei, 2022. "COVID-19 and China commodity price jump behavior: An information spillover and wavelet coherency analysis," Resources Policy, Elsevier, vol. 79(C).
    7. Mei, Xueting & Wang, Xinyu, 2024. "Forecasting stock volatility using time-distance weighting fundamental’s shocks," Finance Research Letters, Elsevier, vol. 65(C).
    8. Danyan Wen & Mengxi He & Yaojie Zhang & Yudong Wang, 2022. "Forecasting realized volatility of Chinese stock market: A simple but efficient truncated approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 230-251, March.
    9. Shen, Lihua & Lu, Xinjie & Luu Duc Huynh, Toan & Liang, Chao, 2023. "Air quality index and the Chinese stock market volatility: Evidence from both market and sector indices," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 224-239.
    10. Zeng, Qing & Tang, Yusui & Yang, Hua & Zhang, Xi, 2024. "Stock market volatility and economic policy uncertainty: New insight into a dynamic threshold mixed-frequency model," Finance Research Letters, Elsevier, vol. 59(C).

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