Why do OTM puts trade richer than OTM calls?

Equity returns and volatility are negatively correlated (the leverage effect): downside moves come with vol spikes, so the market pays up for downside protection. Persistent put-buying demand by institutional hedgers reinforces the bid for OTM puts.

What does steepening put skew tell traders?

Steepening skew (puts becoming relatively richer) signals rising tail-risk pricing, often ahead of macro events or in response to falling spot. It can precede a vol spike but is not a reliable directional signal on its own.

Does skew predict future returns?

Risk-reversal levels have modest predictive content for short-horizon returns and stronger content for realized variance asymmetry, but the relationship is regime-dependent. Skew is more reliable as a positioning gauge than as a directional forecast.

What Is Volatility Skew?

Q: What is volatility skew?

Volatility skew is the pattern of different implied volatilities across option strikes for the same expiration. In equity markets, OTM puts typically trade at higher IV than equidistant OTM calls because crash risk is priced in.

Q: How is volatility skew measured?

The most common measure is 25-delta risk reversal: the IV of the 25-delta call minus the IV of the 25-delta put. A more negative number means steeper put skew. Some desks report 90/110 skew or the slope of IV vs log-moneyness.

Last reviewed: May 17, 2026 by Options Analysis Suite Research.

Volatility skew is the pattern where options at different strikes have different implied volatilities, with downside puts typically priced at higher IV than equivalent upside calls in equity markets. It is the market's expression that downside moves are priced as more likely (or larger) than upside moves of the same magnitude.

Why Are Puts More Expensive Than Calls?

Look at the options chain on a normal trading day for SPY, AAPL, or any major equity index. Out-of-the-money puts cost more than out-of-the-money calls of the same delta or moneyness, even after adjusting for spot. This is not a pricing error. It is the volatility skew, and it tells you the market believes downside risk is asymmetric.

Three structural reasons drive the equity-skew pattern. First, the leverage effect: as a stock falls, its debt-to-equity ratio rises, equity becomes more volatile, and that correlation between falling spot and rising vol fattens the left tail of the return distribution. Second, demand for portfolio insurance: institutional investors holding long equity positions consistently bid OTM puts as hedges, which raises put-side IV. Third, jump risk: equity returns have negatively-skewed jump distributions empirically (large down-moves are more frequent than large up-moves of the same magnitude), and option markets price this into the skew.

The pattern is so consistent in equity markets that any model assuming flat IV across strikes will systematically misprice tail-protective options. Skew is not a mispricing to arbitrage away: it is the steady-state equilibrium that reflects asymmetric risk preferences and asymmetric realized return distributions.

How Is Volatility Skew Measured?

On a representative SPY surface for a 30-day expiration:

50-delta call (ATM): IV = 14.5%
25-delta call (OTM call): IV = 13.8%
10-delta call (deep OTM call): IV = 13.2%
25-delta put (OTM put): IV = 16.1%
10-delta put (deep OTM put): IV = 18.4%

The 10-delta put trades at 39% higher IV than the 10-delta call. The skew slope (∂IV/∂moneyness) is the standard metric: typical SPX 1-month skew runs 4-7% per 10% moneyness in calm regimes and steepens to 10%+ during drawdowns. The pattern inverts only in commodity markets (where supply shocks produce upside skew for crude oil, natural gas) or in rare meme-stock episodes where calls trade at steeper IV than puts.

How Do Pricing Models Capture Skew?

Each pricing model captures skew through a different mechanism. Knowing which model captures skew through which parameter is the bridge from observed skew to a calibrated model output.

Black-Scholes: assumes flat constant volatility and cannot produce skew. Any skew you observe in BS-implied vols is an artifact of the model failing to fit the true distribution. The presence of persistent skew is itself the empirical refutation of the constant-vol assumption.
Heston (stochastic volatility): captures skew through rho, the correlation between spot returns and instantaneous variance. A negative rho (typical for equities, often -0.5 to -0.8) produces a downward-sloping skew because falling spot leads to rising vol, which fattens the left tail. Skew steepness depends jointly on rho and nu (vol-of-vol).
SABR: directly parameterizes skew via rho (correlation between forward and stochastic vol process). The Hagan formula gives a closed-form approximation of the smile from SABR parameters, making it the industry-standard fit per-expiration for interest-rate and equity-index options.
Local volatility (Dupire): captures skew exactly by construction. The Dupire equation produces a deterministic vol function σ(S, t) that matches every traded option price, including the skew. Trade-off: LV fits any static surface perfectly but produces unrealistic forward-smile dynamics that flatten too quickly compared to what stochastic vol predicts.
Jump diffusion (Merton, Kou, Bates): captures skew through asymmetric jump distributions. A negative-mean jump produces left-skew. Jumps capture short-tenor skew that diffusion-only models miss because skew at 1-7 DTE is dominated by jump risk, not stochastic vol diffusion.
Variance Gamma: the skewness parameter (theta in VG notation) directly controls left-right asymmetry in the Lévy density. VG captures skew through pure jump-process structure rather than stochastic vol.

How Do Traders Use Volatility Skew?

Skew tells you whether selling premium on the call side is symmetric with selling on the put side. It is not. Selling 25-delta puts on SPX collects roughly 15-20% more premium than selling equivalent calls, because the market is pricing greater perceived downside risk. Skew also drives the cost of protective puts: when skew steepens (typically into market drawdowns), portfolio insurance gets expensive precisely when you most want to buy it.

For traders, skew is operationally relevant in three places: (1) sizing premium-collection strategies (puts collect more for the same delta because of skew), (2) timing protective hedge purchases (buy when skew is flat, defer when steep), and (3) reading regime: skew flattening into a rally signals retail FOMO into call-side speculation; skew steepening into a sell-off signals institutional hedge demand intensifying.

When Does Skew Steepen?

Skew is not static. Three regime-dependent behaviors matter:

Sticky strike vs sticky delta. When spot moves, does skew translate (sticky strike) or rotate around the new ATM (sticky delta)? Local volatility implies sticky strike; stochastic volatility implies sticky delta. Empirically, regime-dependent: equity index skew is closer to sticky delta in calm regimes and closer to sticky strike during crashes.
Steepening into drawdowns. Skew measured as the difference in IV between 25-delta puts and 25-delta calls typically widens 2-3x during volatility spikes. The October 2008, March 2020, and August 2024 episodes all featured skew steepening that preceded peak realized vol.
Flattening at long tenors. Skew is steepest at near-dated expirations and flattens toward zero at long tenors. The intuition: jump risk dominates skew at short horizons; diffusion-driven skew dominates at long horizons. Term-structure-of-skew is its own analytical surface.

Related Concepts

Volatility Smile · Vol of Vol · Tail Risk · Term Structure · Risk-Neutral Density · Pricing Model Landscape

References & Further Reading

Gatheral, J. (2006). The Volatility Surface: A Practitioner's Guide. Wiley. Practitioner reference for volatility-surface, skew, stochastic-volatility, and local-volatility modeling.
Derman, E. (1999). "Regimes of Volatility." Risk, 12(4), 55-59. The original sticky-strike vs sticky-delta framing.
Cont, R. and Tankov, P. (2003). Financial Modelling with Jump Processes. Chapman & Hall. Reference text on jump-process pricing and skew implications of jump models.
Hagan, P. S., Kumar, D., Lesniewski, A. S., and Woodward, D. E. (2002). "Managing Smile Risk." Wilmott, 1, 84-108. The SABR paper.
Gatheral, J., Jaisson, T. and Rosenbaum, M. (2018). "Volatility is rough." Quantitative Finance, 18(6), 933-949. Realized vol has Hurst exponent ~0.1, far below the 0.5 of diffusion - structural reason smile persists at short tenor.
Guyon, J. and Lekeufack, J. (2022/2023). "Volatility is (Mostly) Path-Dependent." Quantitative Finance, 23(9), 1221-1258 (SSRN 4174589). Up to 90% of equity-index IV variance is explained endogenously by past index returns; skew dynamics fall out of this path-dependent structure.

View the live SPY volatility surface and skew →

This page is part of the Pricing Model Landscape and the canonical reference set on options market structure. Browse all documentation.

As of the latest snapshot, SPY has an ATM implied volatility of 13.8%, IV rank 18% (percentile 43%); 20-day realized vol 15.1%. 25-delta skew is +4.4%, meaning OTM puts trade richer than OTM calls. The IV here is the input that pricing-model walkthroughs (Black-Scholes, Heston, SABR, local-vol) take as their starting point: each model decomposes the same observed quote into different latent dynamics (constant vol, stochastic vol, surface-fitted vol, etc.) which is why two models can agree on price but disagree on Greeks and on how vol will evolve.