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

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.

Authors

Institutions

Publication Details

Journal
Journal of risk and financial management
Published
2024-12-29
DOI
https://doi.org/10.3390/jrfm18010011
Citations
1
Primary Topic
Market Dynamics and Volatility
Type
article
Field-Weighted Citation Impact
1.13
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Svetlozar T. Rachev, Frank J. Fabozzi, Abootaleb Shirvani, Ayush Jha
1 citations
Journal of risk and financial management
Market Dynamics and Volatility
1.13
article

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

Svetlozar T. Rachev, Frank J. Fabozzi, Abootaleb Shirvani, Ayush Jha
article en
1 citations

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.

Journal of risk and financial managementVol. 18(1)
Texas Tech University (US), Johns Hopkins University (US), Kean University (US)
Openalex Percentile: Top 10%
Market Dynamics and Volatility
1.13
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.