TITLE:
Dynamic Correlation between Implied Volatility Spreads and Movement of CSI 300 ETF: Regime Identification
AUTHORS:
Han Yang, Yintao Hu, Zhijing Wang, Naixue Xiong, Rui Liang, Jerome Yen
KEYWORDS:
Implied Volatility Spread, Regime Switching, Multi-Modal Data, A-Share Market
JOURNAL NAME:
Open Journal of Social Sciences,
Vol.14 No.3,
March
31,
2026
ABSTRACT: Implied volatility (IV) is crucial for gauging market risk and potential price movements, as exemplified by volatility indices such as VIX for the S&P 500. However, China’s A-share markets exhibit unique volatility dynamics due to their distinctive investor composition, T + 1 settlement rules, and limited short-selling instruments. This study constructs an IV Spread indicator—the difference between put and call implied volatilities—to capture the dynamic relationship between IV movements and CSI 300 ETF price changes. Using the Black-Scholes model, we calculate IV and synthesize cross-maturity spread components. We then employ dynamic correlation analysis to examine the relationship between IV Spread changes and underlying asset prices. To identify market sentiment pivot points and extreme market conditions, we apply a two-stage change-point detection method: first merging segments via the Bottom-Up algorithm, then selecting optimal segments using Lavielle’s Maximum Curvature Criterion. This approach identifies 18 stable macro-states with significant transitions between them. Our findings demonstrate that IV Spread and its derivatives effectively identify regime switching and extreme market conditions. Furthermore, we incorporate unstructured data to validate market sentiment shifts, examining events surrounding major correlation breakpoints and price pivot points. This multi-modal approach provides insights for future research on asset movement prediction.