The Wings You Don't Trade

Market noise, insurance logic, and why perfect curve fits are a trap

“The prudent see danger and take refuge, but the simple keep going and pay the penalty.”
— Proverbs 27:12

When beginners look at an option board, their first impulse is to draw a line that touches every price on the screen. Textbooks encourage this. They build elaborate mathematical apparatuses designed to force a smooth, unbroken curve through every strike, all the way out to the extreme edges. In actual trading, trying to fit every price is a beginner’s trap. You do not need a model that explains the extreme wings, because you simply choose not to trade them.

Think of how life insurance works. When an applicant presents an extraordinarily high mortality risk, an insurance company does not construct a sophisticated mathematical model to price their policy—they just refuse to underwrite them. Out in the deep out-of-the-money option strikes, where liquidity disappears and screen quotes get noisy, we do the exact same thing. We do not trade those strikes, so we do not waste energy trying to force our model to explain them.

As time passes or the underlying market moves, a strike that used to sit out in those untradable deep wings naturally drifts closer to at-the-money. As it enters the active part of the market, liquidity returns, the quotes clean up, and the fitting for that strike becomes completely normal. It becomes tradable right when it needs to be. Trying to force a curve through it weeks earlier, while it still sits in the noise, only distorts the shape of the curve where you are putting on real risk today.

There is a deeper reason not to want a perfect model fit: a model that passes through every quoted price nicely implies there is no mispricing left in the market. If your curve touches every point on the screen, you have turned your model into a passive recorder of quotes rather than a trading tool. What you actually want is a model that captures the structural reality of most strikes while letting the genuine oddities stand out as outliers. Those outlier points—quotes that sit visibly above or below the fitted curve—are statistically expected to mean-revert over time. That mispricing is where the opportunity lives.

That brings up how those opportunities are captured in practice. People talk a lot about speed and low-latency infrastructure, and while speed is important for managing risk, it is not what we rely on when trading a volatility curve. We are trading those outlier mispricings, and an option sitting off the curve does not disappear in microseconds. These dislocations often persist for hours, or even days, as inventory shifts or market participants slowly adjust their positions. You do not need sub-microsecond infrastructure to capture a volatility outlier that takes half an afternoon to mean-revert.

Finally, this judgment stays strictly within a single expiration. We do not trade cross-expiry volatility spreads. The relationship between different expiries is notoriously difficult to model with any real reliability, and the assumptions required to link them together introduce far more model risk than edge. We keep our judgment simple: focus on one expiry at a time, trade the outliers on that single curve, and leave the wings alone.