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

TL;DR

This paper proposes a new method for detecting uncertainty shocks in financial markets by constructing a revised VIX index using a heavy-tailed Levy process, capturing extreme market movements more effectively.

Contribution

It introduces a novel approach to measure market uncertainty by fitting a double-subordinated Normal Inverse Gaussian process to option data, improving shock identification.

Findings

Revised VIX captures heavy tails and extreme movements better.

New risk-reward ratios improve uncertainty shock detection.

Method outperforms traditional VIX-based approaches.

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.