This issue compares the traditional Volatility Index (VIX) with the Turbulence Index (TI), emphasizing why the latter offers a structurally richer signal for portfolio risk management. While the VIX gauges expected volatility, the Turbulence Index measures statistical unusualness—accounting not only for magnitude shifts but also for breakdowns in asset correlations. The distinction is non-trivial. In crises, correlation behavior changes before volatility spikes. This newsletter outlines the academic foundations, empirical validation, and practical advantages of TI over VIX for modern portfolio construction.
The Conceptual Gap: Why VIX Isn’t Enough
The VIX, widely interpreted as the “fear gauge,” reflects the market’s consensus on future volatility. Yet its scope is univariate: it focuses exclusively on the magnitude of expected price fluctuations, neglecting how assets behave in relation to one another. By contrast, the Turbulence Index integrates both volatility and correlation dynamics into a single, scale-invariant statistic—based on the Mahalanobis distance. This multivariate approach enables TI to capture market anomalies far earlier than VIX. It quantifies not just how much assets move, but how unusually they move together.
Anatomy of Turbulence: Correlation Surprise and Magnitude Surprise
TI decomposes into two additive components:
- Magnitude Surprise captures abnormal returns for individual assets.
- Correlation Surprise, introduced by Kinlaw and Turkington (2013), isolates changes in asset relationships that deviate from historical norms.
The latter is particularly potent. While VIX responds to realized or implied volatility, TI’s correlation component detects the early unraveling of diversification benefits—often a precursor to market-wide stress. For example, during the 2007–2008 crisis and the COVID-19 onset, the TI rose sharply before VIX levels peaked.
Methodological Rigor: How TI Is Calculated
The Turbulence Index uses a rolling historical mean vector and covariance matrix of asset returns. For each time t, it computes the Mahalanobis distance:
where r_t is the current return vector, \mu_t is the historical mean, \Sigma_t the covariance matrix, and n the number of assets. Importantly, expanding windows and real-time constraints are used to avoid look-ahead bias, ensuring the metric mimics actual portfolio conditions.
In comparison, GARCH models—often used in volatility forecasting—adjust too quickly to stress, diluting the shock signal. GARCH-based TI versions show notably weaker and shorter-lived spikes than those using static historical benchmarks.
Empirical Evidence: Predictive Superiority
TI spikes have historically aligned with major market crises—from Black Monday to the Global Financial Crisis to the COVID crash. But more importantly, they have preceded volatility-based warnings. Moreover, backtests show that asset strategies guided by TI signals (e.g. dynamic shifts between equities and bonds) outperform naïve equity allocations, with lower drawdowns and improved Sharpe ratios.
A particularly striking finding: the S&P 500’s average weekly returns differ significantly between “quiet” and “turbulent” periods as classified by TI, whereas many bond indices do not. This creates scope for intelligent reallocation when TI flags regime changes.
Practical Use: What This Means for Portfolio Managers
For discretionary managers, the Turbulence Index serves as an early-warning system. It tells you not just how risky the market is, but why. Unlike VIX, which offers a consensus snapshot, TI provides a structural decomposition of risk—empowering tactical de-risking before volatility materializes. It enhances stress testing, improves scenario design, and—most critically—supports regime-aware rebalancing frameworks.
For further information, please contact us at. info@simplifypartners.com.
Wishing you the best,
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Federico Polese
This newsletter is the intellectual property of Simplify Partners SA.
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