Beyond the Traditional VIX: A Novel Approach to Identifying Uncertainty Shocks in Financial Markets

ArXiv ID: 2411.02804 “View on arXiv”

Authors: Unknown

Abstract

We introduce a new identification strategy for uncertainty shocks to explain macroeconomic volatility in financial markets. The Chicago Board Options Exchange Volatility Index (VIX) measures market expectations of future volatility, but traditional methods based on second-moment shocks and time-varying volatility of the VIX often fail to capture the non-Gaussian, heavy-tailed nature of asset returns. To address this, we construct a revised VIX by fitting a double-subordinated Normal Inverse Gaussian Levy process to S&P 500 option prices, providing a more comprehensive measure of volatility that reflects the extreme movements and heavy tails observed in financial data. Using an axiomatic approach, we introduce a general family of risk-reward ratios, computed with our revised VIX and fitted over a fractional time series to more accurately identify uncertainty shocks in financial markets.

Keywords: Volatility Index (VIX), Levy Processes, Normal Inverse Gaussian, Uncertainty Shocks, Fractional Time Series

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 4.0/10
• Quadrant: Lab Rats
• Why: The paper heavily employs advanced stochastic calculus, Lévy processes, and subordination theory to construct a novel volatility measure, indicating high mathematical complexity. However, it relies on theoretical modeling and parameter estimation without presenting empirical backtests, performance metrics, or implementation details for live trading, resulting in lower empirical rigor.

  flowchart TD
    A["Research Goal:<br>Identify Uncertainty Shocks<br>to Explain Macro Volatility"] --> B{"Data Input:<br>S&P 500 Option Prices"}
    B --> C["Methodology:<br>Construct Revised VIX"]
    C --> D["Model Fitting:<br>Double-Subordinated<br>Normal Inverse Gaussian Levy Process"]
    D --> E["Computation:<br>Fractional Time Series &<br>Axiomatic Risk-Reward Ratios"]
    E --> F["Outcome:<br>Novel Identification Strategy for<br>Uncertainty Shocks"]