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

Ayush Jha (), Abootaleb Shirvani, Svetlozar T. Rachev and Frank J. Fabozzi
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Ayush Jha: Department of Economics, Texas Tech University, Lubbock, TX 79409, USA
Abootaleb Shirvani: Department of Mathematical Sciences, Kean University, Union, NJ 07083, USA
Svetlozar T. Rachev: Department of Mathematics and Statistics, Texas Tech University, Lubbock, TX 79409, USA
Frank J. Fabozzi: Carey Business School, Johns Hopkins University, Baltimore, MD 21202, USA

JRFM, 2024, vol. 18, issue 1, 1-18

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 the market expectations of future volatility, but traditional methods based on second-moment shocks and the time-varying volatility of the VIX often do not effectively to capture the non-Gaussian, heavy-tailed nature of asset returns. To address this, we constructed a revised VIX by fitting a double-subordinated Normal Inverse Gaussian Lévy process to S&P 500 log returns, to provide a more comprehensive measure of volatility that captures the extreme movements and heavy tails observed in financial data. Using an axiomatic framework, we developed a family of risk–reward ratios that, when computed with our revised VIX and fitted to a long-memory time series model, provide a more precise identification of uncertainty shocks in financial markets.

Keywords: asset pricing; volatility; long memory; uncertainty shocks; financial market modeling (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2024
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