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Forecasting stock volatility using time-distance weighting fundamental’s shocks

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  • Mei, Xueting
  • Wang, Xinyu

Abstract

The effect of low-frequency fundamentals on high-frequency volatility is assumed to be constant over a low-frequency period in the GARCH-MIDAS model, but this is unrealistic, especially for datasets with large differences in sampling frequencies. We propose a new GARCH-MIDAS model, which innovatively employs a time-distance weighted (TDW) function that can generate time-varying shocks to high-frequency volatility from fundamentals within the same low-frequency period. The empirical results for the Chinese stock market show that the GARCH-MIDAS-TDW model outperforms the GARCH-MIDAS model in both in-sample fitting and out-of-sample forecasting performance, and this finding is also robust to the S&P 500 stock index.

Suggested Citation

  • Mei, Xueting & Wang, Xinyu, 2024. "Forecasting stock volatility using time-distance weighting fundamental’s shocks," Finance Research Letters, Elsevier, vol. 65(C).
    • Handle: RePEc:eee:finlet:v:65:y:2024:i:c:s1544612324006627
    DOI: 10.1016/j.frl.2024.105632
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    References listed on IDEAS

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