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Determinants of Price Discovery in Option Markets: An Interpretable Machine Learning Perspective

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  • Jufang Liang
  • Dan Yang
  • Qian Han

Abstract

This paper empirically demonstrates that the SSE 50 ETF option market has the informational advantage compared to the underlying market, and evaluates the relative importance of option characteristics in price discovery using interpretable machine learning methods. Estimating the Information Leadership Share using 1‐s resolution price data as a measure of price discovery indicates that price discovery occurs in the SSE 50 ETF option market more, less in the underlying market. The feature importance analysis reveals that trading cost is the primary factor contributing to the informational advantage of option markets, followed by leverage, market maker risk, and speculation, while liquidity and open interest have less impact. Extensive robustness tests are also conducted to assess the stability of the feature importance.

Suggested Citation

  • Jufang Liang & Dan Yang & Qian Han, 2026. "Determinants of Price Discovery in Option Markets: An Interpretable Machine Learning Perspective," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 46(2), pages 237-261, February.
    • Handle: RePEc:wly:jfutmk:v:46:y:2026:i:2:p:237-261
    DOI: 10.1002/fut.70052
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    References listed on IDEAS

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