How Dealer Hedging Creates Self-Fulfilling Support and Resistance

Summary. Large open interest at a strike can act as support or resistance because option dealers rebalance their delta hedges as spot approaches it. This article works through the hedge arithmetic for a single strike, shows why the effect strengthens near expiry, and explains why the same open interest produces resistance in one case and acceleration in another. The key variable is not the size of the position but the sign of the dealer's gamma.

1. The dealer's objective

Option market makers earn the bid-ask spread and aim to keep little directional exposure. After each trade they hedge the option's delta with the underlying, typically index futures or the ETF for index options [1]. Because delta changes as spot moves, the hedge must be adjusted continuously. The size of each adjustment is set by gamma [2].

For a position of $Q$ contracts with multiplier $m$, the hedge trade after a spot move $\Delta S$ is approximately

where $s=+1$ if the dealer is long the options and $s=-1$ if short. A long-option dealer sells into strength and buys into weakness. A short-option dealer does the opposite.

2. Why the effect concentrates near the strike

Black-Scholes gamma is a bell-shaped function of spot centered close to the strike, and its height grows as expiry approaches [3]:

Peak gamma scales with $1/\sqrt{T}$. For a 6,000 strike at 16% implied volatility, peak gamma is about 0.0014 with 21 trading days to expiry, 0.0030 with five days and 0.0066 with one day. One day before expiry, the hedging intensity at the strike is roughly $\sqrt{21}\approx 4.6$ times what it was a month earlier. Figure 1 shows the narrowing and steepening of the profile.

3. A worked example

Suppose customers have sold 10,000 SPX calls at the 6,000 strike, so dealers are long them, and two trading days remain. At spot 6,000, gamma is about 0.0047 per index point. Each one point rise adds

which is about $28 million of notional, or roughly 93 E-mini S&P 500 futures at $50 per index point [4]. To stay neutral, dealers sell that amount for every point the index rises toward the strike and buy it back for every point it falls. A 10 point rally into the strike therefore generates on the order of 900 futures of selling from this one strike alone, all of it concentrated in a narrow price band.

Figure 2 compares this case with the opposite one, in which customers bought the calls and dealers are short them. The hedge flow has the same magnitude and the opposite sign.

4. Resistance or acceleration: the sign decides

The popular description of a "call wall" assumes the first case: dealers long calls that customers sold through overwriting programs. That assumption follows the SqueezeMetrics convention and is consistent with evidence that public customers are large net writers of index calls [5, 6]. Under it, call-heavy strikes above spot produce selling into rallies, which is why they behave like resistance.

When customers buy calls aggressively, the sign flips. Dealers become short gamma at that strike and must buy as spot rises toward it, which can accelerate the move. The same applies to puts: dealers short customer-bought puts are short gamma there, so their hedge sells into declines near the put strike. A put-heavy strike acts as support mainly when the market as a whole is in a long-gamma regime and when put holders monetize their protection as spot reaches the strike, which allows dealers to unwind short hedges.

Empirical work supports the mechanism in both directions. Ni, Pearson, Poteshman and White show that hedge rebalancing by option market makers lowers stock volatility when they are net long gamma and raises it when they are net short [7]. Barbon and Buraschi compare flash crashes in individual stocks by the sign of the previous day's gamma imbalance and plot the average drawdown for each group (Figure 8 of the 2020 working paper) [8]. Anderegg, Ulmann and Sornette find the same feedback in currency markets, where dealer negative gamma is associated with higher spot volatility [9].

5. When a wall fails

• Open interest changes. Positions roll to new strikes and expire. Open interest is updated once per trading day [10], so the map can be stale by the afternoon.
• The position sign is wrong. If the strike was built by customer call buying, the "wall" is a source of acceleration rather than resistance.
• Exogenous flow is larger than hedge flow. Hedging demand of tens of millions of dollars per point is large, but macro news, index rebalancing or a block program can be larger.
• Time decay. After expiry the gamma at that strike is gone. Walls built from same-day options do not exist the next morning.

6. Walls versus chart levels

Prior highs, round numbers and moving averages work, when they work, because many traders watch them. Hedging-driven levels have a mechanical source: the flow occurs whether or not anyone is watching. When both coincide, the level has two independent reasons to hold. When they conflict, the hedging level has the clearer causal story, but only if the sign assumption in Section 4 is right.

7. Practical use

1. Identify the strikes with the largest positive and negative dealer GEX and note how many days remain on the options that make them up.
2. Check the regime. Above the gamma flip, walls hold more often; below it, the put wall is a weaker floor.
3. Watch the tape at the level. Repeated rejections with shrinking ranges suggest hedge flow is absorbing the move. Rising implied volatility as price presses into a call wall suggests new upside demand, which is the opposite of the flow that built the wall.
4. Re-mark the levels after expirations and after large same-day flow.

DataFirst ranks walls by strength and flags strikes where gamma, vanna and charm exposure line up on the same level.

For educational and informational purposes only. Not financial advice. Options and futures trading involve substantial risk of loss. All prices and positions in this article are hypothetical.

References

1. Hull, J. C. (2022). Options, Futures, and Other Derivatives (11th ed.). Pearson.
2. Taleb, N. N. (1997). Dynamic Hedging: Managing Vanilla and Exotic Options. Wiley.
3. Black, F., & Scholes, M. (1973). The pricing of options and corporate liabilities. Journal of Political Economy, 81(3), 637–654. doi.org/10.1086/260062
4. CME Group. E-mini S&P 500 futures contract specifications. www.cmegroup.com/markets/equities/sp/e-mini-sandp500.contractSpecs.html
5. SqueezeMetrics. (2016, revised 2017). Gamma Exposure (GEX). White paper. squeezemetrics.com/monitor/download/pdf/white_paper.pdf
6. Lakonishok, J., Lee, I., Pearson, N. D., & Poteshman, A. M. (2007). Option market activity. Review of Financial Studies, 20(3), 813–857. doi.org/10.1093/rfs/hhl025
7. Ni, S. X., Pearson, N. D., Poteshman, A. M., & White, J. (2021). Does option trading have a pervasive impact on underlying stock prices? Review of Financial Studies, 34(4), 1952–1986. doi.org/10.1093/rfs/hhaa082
8. Barbon, A., & Buraschi, A. (2020). Gamma fragility. Working Papers on Finance No. 2020/05, Swiss Institute of Banking and Finance, University of St. Gallen. www.alexandria.unisg.ch/bitstreams/25fec636-90a2-4735-a3a4-dfc0b68d3feb/download
9. Anderegg, B., Ulmann, F., & Sornette, D. (2022). The impact of option hedging on the spot market volatility. Journal of International Money and Finance, 124, 102627. doi.org/10.1016/j.jimonfin.2022.102627
10. The Options Clearing Corporation. Volume and open interest reports. www.theocc.com/market-data/market-data-reports/volume-and-open-interest