What Are Sticky Strikes?
In this article we will focus on Sticky Strikes and the Skew. Sticky strikes refer to specific strike prices where large amounts of options—often from institutional players, market makers, or hedge funds—are concentrated.
These strikes accumulate significant gamma, the rate of change of an option’s delta relative to the underlying price. When gamma is high, hedging flows can become self-reinforcing, effectively “pulling” or “holding” the underlying price near these strikes. Two key reasons account for why certain strikes become sticky:
They act as liquidity hubs. If many traders have open positions at a particular strike, market makers will continuously hedge as the price approaches or moves away from it.
They serve as psychological anchors. Traders and investors often gravitate toward round-number strikes, reinforcing liquidity and fueling momentum that keeps the price near these levels.
In practice, you might observe that when the market approaches one of these heavily traded strikes, the price either stalls, bounces, or experiences sudden volatility as hedging flows increase. This stickiness can be useful in formulating short-term strategies or anticipating support and resistance.
The Black-Scholes Model
The Black-Scholes model (BSM) revolutionized options trading by providing a formula to calculate the theoretical fair value of European-style options. Its main parameters include the underlying asset’s spot price, time to expiration, strike price, risk-free rate, and volatility. In many respects, BSM remains a cornerstone of the derivatives market, offering:
- A standardized framework for pricing. Before BSM, traders relied on fragmented methods that varied from market to market.
- A means to compare different options on a similar basis. Implied volatility, derived from BSM, allows traders to assess relative cost and risk across strikes and maturities.
- A stepping stone for more advanced models. While BSM assumes constant volatility and lognormal price distributions, modern models (often called Q-Models) incorporate features like stochastic volatility, jumps, or mean reversion to address real-world market behavior.
Still, the events of 1987—in which markets witnessed drastic crashes—uncovered a critical limitation of the normal or lognormal distribution assumption. Extreme price moves occur more frequently than BSM would suggest, leading traders to question the model’s volatility assumptions.
Skew according to BSM
BSM assumes that returns are normally distributed
How Skew Emerges
Skew describes the observed pattern in implied volatilities across different strike prices. Under the pure BSM framework, you might expect each strike to have a similar implied volatility, forming a flat implied volatility curve. However, in reality, deep in-the-money (ITM) or far out-of-the-money (OTM) options often command higher implied volatilities. This results in a “smile” or “smirk” shape on an implied volatility chart.
BSM vs Reality of returns
Why does skew exist?
Fear of extreme events: Market crashes and sudden rallies can happen unexpectedly, prompting traders to buy OTM puts or calls for protection or speculative opportunities.
Supply and demand imbalances: When more traders purchase OTM puts (for instance, in bearish markets), the implied volatility for those strikes increases due to heightened demand.
Fat tails: Price returns do not follow a neat bell-shaped curve; instead, we see heavier distribution tails, meaning large price moves occur with higher frequency.
In bullish markets, calls may become pricier if market participants anticipate sharp upside moves. In bearish environments, puts can trade at elevated implied volatilities as traders scramble for downside protection. The cumulative effect is an implied volatility skew that departs from the flat assumption in classical Black-Scholes.
Sticky Strikes and Gamma
Gamma serves as the driving force behind sticky strikes. Gamma measures how quickly delta (the option’s sensitivity to the underlying price) changes. As expiration nears or as large positions pile up at a specific strike, gamma intensifies. For market makers, who often run delta-hedged books, this means:
If the underlying moves slightly above a heavily concentrated call strike, market makers need to buy shares (or the underlying asset) to hedge, thereby pushing the underlying price back toward that strike.
If the underlying drifts slightly below that same strike, they might sell shares to maintain a neutral position, nudging the price back up.
This repeated hedging activity creates a price magnet effect at high-gamma strikes. When large volumes of open interest converge around certain points—say, round numbers like 4000 on an equity index—these levels often become focal points where price action slows or reverses.
Market participants often observe that these sticky strikes can act as short-term support or resistance. If you track where the largest net gamma exposure lies, you can better forecast near-term market behavior. When the underlying strays too far from that sticky point, hedging flows might lessen, and the price can move more freely.
Practical Implications for Traders
Understanding how sticky strikes and skew interplay can offer several strategic benefits:
Identifying short-term trading opportunities. If you suspect that a particular strike has large open interest, you could exploit range-bound trades (such as selling iron butterflies or iron condors) near that level, aiming to collect premium as the market grinds around the sticky strike.
Managing risk more effectively. If a trader holds an option near a sticky strike, they should be prepared for muted price movement unless a major catalyst breaks the underlying away from that “magnet.”
Optimizing hedging strategies. Knowing that implied volatility skews can inflate premiums on certain calls or puts, savvy traders might sell options at those overpriced strikes and buy them where implied vol is relatively cheaper—assuming they remain mindful of how gamma exposure can shift quickly.
For longer-term investors, sticky strikes may appear less pivotal, but these levels can still coincide with psychological barriers. A heavily watched strike price could cause short-term fluctuations and influence overall market sentiment.
Using Q-Models to Track Sticky Strikes
Q-Models expand upon the basic premise of Black-Scholes by introducing more realistic elements, such as dynamic implied volatility surfaces, jumps in price, or changes in skew over time. In practice, these models:
Highlight where gamma exposure accumulates. Q-Models can overlay data on open interest, implied volatility, and gamma to identify the most influential strikes.
Provide a real-time view of liquidity flows. As delta-hedging occurs, liquidity can shift rapidly in the market. Tracking these flows can help you anticipate sudden price movements.
Incorporate fat tails. By recognizing that extreme events happen more frequently than standard BSM would predict, Q-Models offer a better framework for managing tail risk.
Our NetGex (Gold Option Futures NetGex)
Our 25 delta risk reversal that looks at OTM options (SPX 25 Delta Risk Reversal)
Historical Context: Fat Tails and Market Realities
After the 1987 crash, traders realized that markets are capable of more abrupt, extreme moves than the classic lognormal distribution would suggest. This observation extends beyond that single historical event: consider the dot-com bubble in the early 2000s, the 2008 financial crisis, and even the swift market responses to unexpected geopolitical or pandemic-related news.
Fat tails imply that out-of-the-money options can surge in value quickly, particularly during periods of market stress. In response, implied volatilities at these strikes rise, forming the skew that we see in real-world data. By acknowledging these characteristics, traders can:
Implement more conservative risk management. Knowing that tail events are more likely, you might scale position sizes appropriately or keep extra liquidity on hand.
Hedge with OTM options. While they may seem expensive during calm periods, OTM options can provide disproportionate protection in the event of a severe market swing.
Monitor shifting skews. As soon as panic hits, put skew can intensify, while call skew may flare up in especially bullish or manic conditions.
When we combine the concept of fat tails with sticky strikes, it becomes clear that the market’s overall shape isn’t just about a single implied volatility metric but about a complex web of demand, supply, hedging needs, and the perceived likelihood of extreme outcomes.
Conclusion
Sticky strikes and the skew are fundamental to a nuanced understanding of modern options trading. While the Black-Scholes model remains an essential building block, it cannot fully capture the complexities of real-world price distributions or the dynamics of market participants rushing to hedge large positions. Skew arises because traders demand greater compensation for the possibility of extreme moves, and sticky strikes develop where gamma exposures concentrate, creating a magnet-like effect on the underlying’s price.
For active traders, recognizing and monitoring sticky strikes can inform both directional and non-directional strategies, particularly around support and resistance levels. At the same time, analyzing skew can uncover where market sentiment leans—whether fear on the downside or speculation on the upside.
These concepts come alive even more vividly in Q-Models, which account for dynamic volatility surfaces and real-world liquidity flows. Ultimately, an informed grasp of sticky strikes and the skew equips you with a more powerful toolkit for navigating the complexities of options markets.
For a deeper dive into skew, volatility modeling, and how these elements tie into broader liquidity flows, explore additional resources and educational materials from MenthorQ. An appreciation of sticky strikes and skew isn’t merely academic—it directly influences how you hedge, structure, and time your trades. The interplay of gamma, implied volatility, and human psychology at these critical price levels underscores the importance of going beyond textbook theories and embracing the dynamic realities of the market.
To better understand how sticky strikes and skew show up in live markets and influence trade structure, chat with QUIN for deeper context