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This paper investigates the structural dynamics of stock market volatilitythrough the Financial Chaos Index, a tensor- and eigenvalue-based measuredesigned to capture realized volatility via mutual fluctuations among assetprices. Motivated by empirical evidence of regime-dependent volatility behaviorand perceptual time dilation during financial crises, we develop aregime-switching framework based on the Modified Lognormal Power-Lawdistribution. Analysis of the FCIX from January 1990 to December 2023identifies three distinct market regimes, low-chaos, intermediate-chaos, andhigh-chaos, each characterized by differing levels of systemic stress,statistical dispersion and persistence characteristics. Building upon thesegmented regime structure, we further examine the informational forces thatshape forward-looking market expectations. Using sentiment-based predictorsderived from the Equity Market Volatility tracker, we employ an elastic netregression model to forecast implied volatility, as proxied by the VIX index.Our findings indicate that shifts in macroeconomic, financial, policy, andgeopolitical uncertainty exhibit strong predictive power for volatilitydynamics across regimes. Together, these results offer a unified empiricalperspective on how systemic uncertainty governs both the realized evolution offinancial markets and the anticipatory behavior embedded in implied volatilitymeasures.

Modeling Regime Structure and Informational Drivers of Stock Market Volatility via the Financial Chaos Index