Reading the Gamma Flip: Long-Gamma vs Short-Gamma Regimes

Summary. The gamma flip is the spot level at which aggregate dealer gamma exposure changes sign. Above it, delta hedging by option dealers leans against price moves; below it, the same hedging follows them. This article defines the flip precisely, shows how it is computed from an option chain, explains why the sign of dealer gamma matters for realized volatility, and lists the assumptions that make the level useful or misleading.

1. Dealer gamma and the direction of hedging

An option dealer who quotes both sides of the market ends each trade with a position it did not choose. Dealers neutralize the directional part of that position by trading the underlying so that their net delta stays close to zero [1, 2]. Gamma, the rate at which delta changes with spot, determines how that hedge must be adjusted as the market moves.

For a book with net gamma $\Gamma$, a spot move of $\Delta S$ changes the book's delta by approximately $\Gamma \Delta S$. To return to neutral, the dealer trades

The sign of $\Gamma$ is the whole story. When dealers are long gamma ($\Gamma >0$), the hedge sells after a rise and buys after a fall, which supplies liquidity against the move. When dealers are short gamma ($\Gamma <0$), the hedge buys after a rise and sells after a fall, which demands liquidity in the direction of the move. Barbon and Buraschi call the second state "gamma fragility": in a large panel of index and equity options they link negative aggregate dealer gamma to intraday momentum and to more frequent and larger flash crashes, with the strongest effects in less liquid underlyings [3].

2. Gamma exposure (GEX)

Dealer positions are not public, so practitioners estimate them from open interest with a sign convention. The convention introduced in the SqueezeMetrics white paper assumes customers are net sellers of index calls (overwriting) and net buyers of index puts (protection), which leaves dealers long calls and short puts [4]. Evidence on customer option activity is broadly consistent with heavy call writing and put buying in index products [5, 6], but the convention is an approximation and can be wrong for individual strikes.

Under that convention, dealer gamma exposure at a hypothetical spot level $S$ is

where the sum runs over every listed option $i$, $\text{OI}$ is open interest, $m=100$ is the contract multiplier and $\Gamma (\cdot )$ is the Black-Scholes gamma [7]:

The factor $S^2\times 0.01$ converts gamma into dollars of delta that must be traded for a 1% move in spot. A GEX of +$5 billion means dealers would sell roughly $5 billion of index exposure if the index rose 1%, and buy the same amount if it fell 1%.

3. Defining the flip

The gamma flip (also called zero gamma) is the spot level $S^{\ast }$ that solves

It is found by re-evaluating every option's gamma at a grid of hypothetical spot levels while holding open interest, implied volatility and time fixed, and locating the zero crossing. This is not the same as adding up per-strike GEX from the lowest strike to the highest and finding where the running total turns positive. The cumulative-over-strikes construction mixes strikes evaluated at today's spot and has no direct interpretation as a hedging threshold.

Figure 1 shows the calculation for a hypothetical SPX chain. With spot at 6,000, net GEX is positive. The curve crosses zero near 5,967. If the index fell below that level, the same open interest would leave dealers net short gamma.

The per-strike view in Figure 2 explains the shape. Call open interest above spot contributes positive gamma, put open interest below spot contributes negative gamma, and the contribution of each strike is largest when spot is close to it. As spot falls toward the put-heavy strikes, put gamma dominates and the total turns negative.

4. What the flip changes: evidence on volatility

The claim that dealer gamma affects realized volatility is testable, and several studies support it:

• Ni, Pearson, Poteshman and White find that stock return volatility falls when hedgers are net long gamma and rises when they are net short, consistent with a non-informational effect of hedge rebalancing [10].
• Baltussen, Da, Lammers and Martens show that the return over the last 30 minutes of the trading day is predicted by the return earlier in the day across more than 60 futures markets, and link the effect to short-gamma hedging by option dealers and leveraged ETFs. Conditioning on the sign of S&P 500 net gamma, the predictive coefficient is 6.63 (t = 4.78) on days after negative net gamma and 0.82 (t = 1.03, not significant) after positive net gamma (their Table 7) [9].
• Barbon and Buraschi find that positive dealer gamma imbalances go with intraday reversal and negative imbalances with intraday momentum, especially when the underlying is illiquid [3].
• The SqueezeMetrics paper reports that next-day SPX moves are smaller when GEX is in its highest quartile than when it is in its second-highest quartile (one-day standard deviations of about 0.55% and 0.85%) [4].

The effect is a tilt in the distribution of returns, not a guarantee. News, macro flow and the size of non-dealer trading can dominate hedging flow on any given day.

5. Trading implications by regime

A reclaim of the flip from below is often the most informative event in a session, because the direction of hedging flow reverses at that point. The reverse cross, from long to short gamma, is where volatility tends to expand.

6. Limits of the estimate

• Sign convention. The calculation assumes who holds which side. If customers buy calls aggressively, as in a call-buying wave, dealers can be short calls and the true flip can sit far from the estimate.
• Open interest is a daily snapshot. The Options Clearing Corporation publishes open interest once per day [11]. Same-day trading, including 0DTE flow, changes positions before the next update.
• Model inputs. Gamma depends on implied volatility and time. The flip moves when volatility or time to expiry change even if positions do not.
• Net versus level. Positive net GEX at the current spot and the distance to the flip are different quantities. A market can be long gamma at spot with the flip only a few points below, which makes the regime fragile.
• Aggregation. Single expiries and thin strikes produce noisy zero crossings. Aggregating across expiries gives a more stable level.

7. Practical checklist

1. Before the open, note the flip, call wall and put wall, and whether spot is above or below the flip.
2. Measure the distance from spot to the flip in points and as a share of the expected daily move. A flip within one expected move is a live risk.
3. Re-check the levels during the session, because same-day option flow can move them.
4. Treat the regime as context for position sizing and trade selection, not as an entry signal on its own.

DataFirst computes the flip across expiries and updates it through the session alongside the walls and the volatility trigger.

For educational and informational purposes only. Not financial advice. Options trading involves substantial risk of loss. Figures use a hypothetical option chain and the Black-Scholes model; they illustrate mechanics and are not market data.

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. Barbon, A., & Buraschi, A. (2021). Gamma fragility. Working paper, University of St. Gallen. SSRN 3725454. papers.ssrn.com/sol3/papers.cfm?abstract_id=3725454
4. SqueezeMetrics. (2016, revised 2017). Gamma Exposure (GEX). White paper. squeezemetrics.com/monitor/download/pdf/white_paper.pdf
5. 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
6. Gârleanu, N., Pedersen, L. H., & Poteshman, A. M. (2009). Demand-based option pricing. Review of Financial Studies, 22(10), 4259–4299. doi.org/10.1093/rfs/hhp005
7. 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
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. Baltussen, G., Da, Z., Lammers, S., & Martens, M. (2021). Hedging demand and market intraday momentum. Journal of Financial Economics, 142(1), 377–403. doi.org/10.1016/j.jfineco.2021.04.029
10. 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
11. The Options Clearing Corporation. Volume and open interest reports. www.theocc.com/market-data/market-data-reports/volume-and-open-interest