Sostieni Lee Shikoh

In options markets, convexity describes how an option’s price accelerates relative to its underlying as volatility or time changes. Conventional volatility-analysis systems rely on static implied-volatility (IV) surfaces, capturing convexity at a single point in time but failing to measure how it evolves dynamically as markets absorb information from earnings announcements, macroeconomic releases, or concentrated institutional flows.

This static, snapshot architecture creates temporal myopia - it cannot quantify the rate or persistence of changes in the curvature of option pricing such as skew shifts or volatility-term-structure inversions that frequently precede repricing in the underlying asset.

Empirical evidence supports this deficiency. Internal ConvexityIQ backtests using high-frequency U.S. equity-option data (2018 – 2025, thousands of contracts) indicate that most large equity-price dislocations (> 2 σ daily moves) are preceded by measurable convexity anomalies, yet static IV models detect fewer than one-third of these events.

These results were derived by comparing persistence-weighted convexity signals against conventional IV-snapshot thresholds, confirming that dynamic convexity signals frequently emerge hours - and sometimes days - before the underlying reprices.

Even advanced stochastic-volatility and machine-learning models share this limitation because they lack a persistence dimension - a calibrated decay constant (λ, which measures signal decay rate) or signal half-life (α½, the time over which a convexity signal retains predictive value).

Existing persistence-aware approaches remain regime-specific or computationally constrained, requiring frequent manual recalibration that limits scalability. This deficiency produces a systematic blind spot:

·      Missed Alpha Opportunities: Informed option activity that telegraphs equity-price shifts goes unnoticed in roughly 60 % of major moves.

·      Inefficient Capital Allocation: Traders misjudge whether convexity resides in the options structure or has migrated into the underlying, leading to mistimed trades.

·      Increased Risk Exposure: Mis-estimating convexity persistence degrades hedging accuracy and magnifies tail-risk.

·      Competitive Disadvantage: Leading quantitative and high-frequency firms are increasingly deploying dynamic, persistence-aware analytics, widening the gap over static-model counterparts.

While no model can fully capture how information moves through markets over time, ignoring persistence leaves traders blind to the temporal dynamics of convexity repricing — the leading indicator of future volatility regime shifts. Addressing this requires a framework that quantifies how convexity evolves, decays, and propagates through time across equity and index options - with extensibility to FX and fixed-income derivatives - transforming transient anomalies into persistence-weighted dislocations that enhance alpha capture, hedging precision, and systemic-risk control.