Volatility Term Structure Index
Educational Overview
Multi-factor volatility regime model measuring market stress through five proprietary components. Term structure analysis combined with TSMOM filters for systematic volatility-based positioning.
Volatility Regime Strategy
The Volatility Term Structure Index (VTSI) measures market stress and complacency through a multi-factor framework producing values from 0 to 100. Higher values indicate calm conditions supporting risk-on positioning while lower values signal elevated stress requiring defensive measures.
The indicator operates on the principle that volatility derivatives markets contain valuable forward-looking information. The relationship between short-term and long-term implied volatility reveals market expectations about risk evolution. When markets expect calm to persist, the term structure typically exhibits contango. When stress emerges, this relationship inverts to backwardation.
The strategy has demonstrated consistent performance over 32 years since 1994, achieving a 66.2% win rate while generating 355.34% cumulative returns. VTSI includes advanced TSMOM filtering based on institutional time-series momentum methodology.
Historical Performance
Cumulative returns from 1994 to 2026 (backtested)
Past performance does not guarantee future results. For educational purposes only.
Performance by Year
Detailed breakdown showing volatility regime identification across multiple market cycles
Positive Years
Profitable trading years
Best Year
Highest annual return
Worst Year
Lowest annual return
Annual Returns Overview
Year-by-Year Performance
Returns calculated from cumulative performance data. All figures are net of costs and represent backtested results.
Why VTSI Works
Six key advantages that make VTSI effective for volatility regime analysis and systematic positioning
Strong Win Rate
Winning Trades
Over 32 years of backtested data, VTSI achieved a 66.2% win rate across 74 trades, demonstrating consistent signal quality through various volatility regimes.
Cumulative Returns
Since 1994
Systematic volatility regime positioning generated substantial returns over 32 years, identifying favorable calm conditions while avoiding stress periods.
Five-Component Framework
Volatility Components
VTSI analyzes term structure, VIX levels, VVIX stability, cross-asset volatility, and MOVE index for comprehensive volatility regime assessment.
Controlled Drawdowns
Worst Single Trade
VTSI's worst single position loss was limited to -7.7%, demonstrating controlled downside through multiple quality filters including VIX spike detection.
TSMOM Filter
Time-Series Momentum
Based on Moskowitz, Ooi, and Pedersen (2012) methodology used by AQR and Man AHL. Combines risk-adjusted returns with volatility for trend confirmation.
Long Track Record
Years of Data
VTSI has been validated across 32 years of market history including multiple volatility regimes and crisis periods from 1994 to 2026.
Core Framework
Three pillars of volatility regime assessment combining term structure analysis, multi-factor components, and quality filters
Scientific Foundation
Multi-horizon term structure analysis across VIX, VIX3M, VIX6M, and VIX futures for comprehensive volatility assessment.
- Term structure contango/backwardation
- VIX percentile ranking
- VVIX stability measurement
- Cross-asset volatility
Professional Implementation
Multiple filter layers including consensus requirements, trend confirmation, and VIX spike detection.
- Consensus filter (3/5 components)
- TSMOM trend filter
- VIX extreme level filter
- VIX spike detection
Practical Applications
Threshold-based signals with hysteresis band preventing rapid switching during volatile conditions.
- Entry threshold: 70
- Exit threshold: 35
- Stress threshold: 20
- Anti-repaint protection
Methodology & Applications
Understanding how VTSI transforms complex volatility data into actionable regime intelligence
Technical Approach
Term Structure Analysis
VTSI monitors the critical VIX term structure relationship. Contango indicates calm conditions while backwardation historically precedes significant equity drawdowns.
Regime Detection
Advanced statistical processing identifies distinct volatility regimes including crisis periods, complacency environments, and transitional states with mixed signals.
Quality Control
Comprehensive filtering mechanisms ensure signal reliability through TSMOM confirmation, VIX spike detection, and cross-component consistency checks.
Practical Usage
Portfolio Management
Strategic asset allocation decisions, tactical positioning adjustments, and dynamic rebalancing timing based on comprehensive volatility regime assessment.
Risk Overlay
Use VTSI as a risk overlay for existing strategies, reducing exposure during elevated stress conditions and increasing participation during calm regimes.
Systematic Execution
VTSI provides clear entry and exit signals at specific thresholds with hysteresis bands, removing emotional decision-making and ensuring disciplined execution.
Empirical Results
Performance metrics showcasing VTSI's effectiveness as a volatility regime strategy
Win Rate
Percentage of profitable trades
Total Return
Cumulative performance since 1994
Total Trades
Signals across 32 years
Years
Validated across multiple cycles
Risk-Adjusted Performance Analysis
Historical Statistics
Risk Management
Important: Performance metrics based on historical S&P 500 analysis from 1994-2026. VTSI identifies volatility regimes through term structure and multi-factor analysis. Results reflect systematic application under controlled backtesting conditions. Past performance does not guarantee future results.
Research Background
VTSI builds upon decades of peer-reviewed research in volatility modeling and time-series momentum
The theoretical foundation rests on seminal works analyzing VIX term structure as a leading indicator of market conditions. Research by Konstantinidi & Skiadopoulos (2011) demonstrates the predictive power of volatility term structure for future realized volatility and equity returns.
The TSMOM component is based on Moskowitz, Ooi, and Pedersen (2012) research published in the Journal of Financial Economics, documenting significant time-series momentum across asset classes. This methodology is employed by leading quantitative funds including AQR and Man AHL.
VTSI synthesizes these research streams into a practical framework that maintains academic rigor while providing actionable signals for professional portfolio management and systematic volatility regime positioning.
Ready for Volatility Regime Analysis?
Get VTSI Professional with five-component framework, TSMOM filtering, and term structure analysis. Built for investors seeking to align positions with underlying volatility conditions.