Modeling Regime Structure and Informational Drivers of Stock Market Volatility via the Financial Chaos Index
Paper analyzes stock market volatility using the Financial Chaos Index and regime-switching models, with sentiment predictors forecasting implied volatility.
What it examines
The paper introduces the Financial Chaos Index (FCIX), a novel tensor-based volatility measure that uses regime-switching and a modified lognormal power-law model to detect structural shifts in market volatility. It aims to link sentiment signals with systemic risk in financial markets.
What it concludes
The study finds clear volatility regimes and strong predictive links between sentiment indicators and implied risk. Its methods can improve risk monitoring, stress testing, and policymaking. Future work should develop real-time detection algorithms and extend the approach to higher-frequency market data.
Evidence objects
Researchers introduce an innovative Financial Chaos Index (FCIX) using tensor and eigenvalue methods to reveal that market volatility shifts sharply between low-chaos, intermediate-chaos, and high-chaos regimes during crises with impact.
key_findings bullet 1 · key_findings · validation V0
Study findings uncover a surprising dual behavior: normal market activities follow a lognormal process while extreme events adhere to a power-law pattern, emphasizing an unexpected shift in market statistical characteristics.
key_findings bullet 2 · key_findings · validation V0
Authors integrate a regime-switching model with a modified lognormal power-law framework, linking macroeconomic and political sentiment via $\text{elastic net regression}$ to forecast VIX volatility, noting data resolution and real-time challenges.
key_findings bullet 3 · key_findings · validation V0
This paper introduces the Financial Chaos Index (FCIX) via innovative tensor and eigenvalue analysis integrated with $$\text{regime-switching frameworks}$$ and a Modified Lognormal Power-Law distribution to capture nonstationary volatility. Its sentiment predictors with elastic net regression yield fresh insights. The original methodology is compelling, offering significant contributions to derivative modeling research.
key_findings bullet 4 · key_findings · validation V0
Raw abstract and provenance
Abstract: This paper investigates the structural dynamics of stock market volatility through the Financial Chaos Index, a tensor- and eigenvalue-based measure designed to capture realized volatility via mutual fluctuations among asset prices. Motivated by empirical evidence of regime-dependent volatility behavior and perceptual time dilation during financial crises, we develop a regime-switching framework b… ▽ More This paper investigates the structural dynamics of stock market volatility through the Financial Chaos Index, a tensor- and eigenvalue-based measure designed to capture realized volatility via mutual fluctuations among asset prices. Motivated by empirical evidence of regime-dependent volatility behavior and perceptual time dilation during financial crises, we develop a regime-switching framework based on the Modified Lognormal Power-Law distribution. Analysis of the FCIX from January 1990 to December 2023 identifies three distinct market regimes, low-chaos, intermediate-chaos, and high-chaos, each characterized by differing levels of systemic stress, statistical dispersion and persistence characteristics. Building upon the segmented regime structure, we further examine the informational forces that shape forward-looking market expectations. Using sentiment-based predictors derived from the Equity Market Volatility tracker, we employ an elastic net regression model to forecast implied volatility, as proxied by the VIX index. Our findings indicate that shifts in macroeconomic, financial, policy, and geopolitical uncertainty exhibit strong predictive power for volatility dynamics across regimes. Together, these results offer a unified empirical perspective on how systemic uncertainty governs both the realized evolution of financial markets and the anticipatory behavior embedded in implied volatility measures. △ Less
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