Trading volatility: gamma scalping, events and the weekend

VOL · Chapter 212 min readAsked at Optiver, SIG, IMC, Akuna

Assumes Implied against realised, and the surface.

After this lesson you should be able to

  • Describe gamma scalping and say where the profit comes from.
  • Strip an event premium out of an implied volatility.
  • Explain how desks handle weekend and holiday decay.

Buying an option and hedging it is a bet that the underlying moves more than the option price assumed. Making that bet well means knowing where the realised moves will come from — a steady grind, a single earnings gap, or nothing at all over a long weekend — because the option price treats all three the same and the P&L does not.

Proposition 2.1

Gamma scalping

Long an option and delta-hedged, you are forced to buy low and sell high: the position gains delta as the stock rises, so rehedging means selling, and loses delta as it falls, so rehedging means buying. Each round trip banks a small profit, and theta is the rent you pay for the privilege. The profit per scalp grows with the square of the move, which is why a few large days dominate.

Holds when

  • Scalping more often captures smaller moves and pays more in spreads.
  • The total scalping profit over the life of the option is the realised variance, not the sum of the moves.
  • Short gamma is the same mechanism in reverse: you are forced to sell low and buy high.

Example 2.2

Stripping an event

A 30-day option spanning earnings implies 45%45\%; a 30-day option on a comparable name with no earnings implies 30%30\%. What one-day move is being priced for the event?

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Worked solution

  1. Formula
    σevent2=(σwith2−σwithout2)T\sigma_{\text{event}}^2 = \left(\sigma_{\text{with}}^2 - \sigma_{\text{without}}^2\right)T
  2. Substitute
    T=30252=0.1190T = \tfrac{30}{252} = 0.1190
  3. Solve
    (0.452−0.302)=0.2025−0.09=0.1125(0.45^2 - 0.30^2) = 0.2025 - 0.09 = 0.1125
  4. σevent2=0.1125×0.1190=0.01339\sigma_{\text{event}}^2 = 0.1125 \times 0.1190 = 0.01339
  5. σevent=0.01339\sigma_{\text{event}} = \sqrt{0.01339}
  6. Answer
    ≈11.6% on the day\approx 11.6\% \text{ on the day}

Sanity check. Variances add, volatilities do not — the calculation lives entirely in variance and converts back once. An 11.6%11.6\% earnings move is on the large side but not unusual, which is the check that the arithmetic is sane.

Proposition 2.3

Trading the event

Once you can strip out the event premium you can take a view on it: if the market is pricing an 11.6%11.6\% move and the name has historically moved 6%6\% on earnings, the straddle is expensive and selling it — hedged — is the trade. The risk is entirely in the tail, because the times you are wrong are the times the move is enormous.

Holds when

  • Compare the implied event move against the distribution of past earnings moves, not their average.
  • Implied volatility collapses immediately after the announcement, so a long position must be right about the size of the move rather than about its direction.
  • The post-event volatility crush is the reason a long straddle into earnings can lose even when the stock gaps.

Why a weekend is not two days. Option pricing uses calendar time for discounting and, in practice, something closer to trading time for volatility — because the underlying does not move when the market is shut. A naive model decays an option by three days over a weekend while the stock has had no opportunity to move at all, so a long-gamma position pays theta for nothing. Desks handle this by running a time weighting that gives weekends and holidays a fraction of a day’s variance, which is also why implied volatility often ticks up on a Friday afternoon and back down on Monday.

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Trading volatility: gamma scalping, events and the weekend · QuantMax