THE OPTION ON THE PLOT
Convexity: When Volatility Starts Paying You
A function is convex when every chord lies on or above its curve, equivalently when its slope does not decrease; capped loss with open upside is one example.
THE HOCKEY STICK
Convexity first. The hockey stick is one example.
- General rule: the curve lies below the chord between any two points
- Equivalently, its slope does not decrease
- A loss-capped, open-upside call payoff is one convex shape
Convexity is a property of a function, not a synonym for options. The call-style hockey stick shown here is one practical real-option example.
THE TELEPHONE ENGINEER
The average of the outcomes beats the outcome of the average
This is Jensen's inequality, the mathematical spine of convexity — and the reason cheap, loss-capped, open-ended bets can be a rational answer to a tail you cannot predict.
- Johan Jensen worked for Copenhagen's telephone company and led its technical department
- A self-taught research mathematician, he never held an academic appointment
- His 1906 paper proved the inequality for convex functions
THE PRICE OF A STORM
With the floor intact, volatility can raise option value
- Black & Scholes and Merton, 1973: option-pricing models
- Vega is the derived sensitivity of option value to volatility
- For standard calls and puts, vega is positive all else equal
Vega is calculated from the pricing model; it is not a named term carried inside the original formula. For standard calls and puts it is positive, all else equal, as explained in the spine.
THE THIRD PILE
The resilient resists shocks and stays the same; the antifragile gets better.
Nassim Nicholas Taleb, Antifragile, 2012
Taleb turned the shape into a way to live: sort everything by what a shock does to it. Convexity is the operational test — does volatility help? If yes, the thing is antifragile.
SAFE MASS, WILD SLICE
Load the ends, leave the middle empty
- Fragile: a shock destroys it
- Robust: a shock leaves it unchanged
- Antifragile: a shock feeds it
Taleb's tool is the barbell: the bulk parked in safety, a thin slice you can lose outright spread across many open-ended bets. That thin slice needs a real floor — an affordable loss — or the shape collapses.
A FEW BARS TOWER
Rare for you. Highly skewed in the corpus.
These samples show strongly concentrated innovation returns. That empirical skew is relevant to corpus comparison, but it does not establish a universal tail law or identify a future winner.
- Scherer & Harhoff, 2000 — eight datasets of innovation returns
- The top 10% of cases carried 48 to 93% of total value within the samples
- A few outcomes dominated; the returns were not evenly distributed
When the odds are rough, inspect the shape
Use probabilities where you have them; in a Knightian tail, do not let a fragile estimate do all the work. Ask: is the loss floor real, is the ceiling open, is the option cheap enough, and is there time to work?
WHAT KILLS THE CURVE
A ceiling can break this call-shaped convexity
- A cap, quota, or veto can flatten the rising arm over the range shown
- The resulting payoff is not convex across that full range
- Premium and time decay can still make a convex payoff a poor trade
This warning is scoped to the loss-capped, open-upside shape used here: a ceiling introduces a flattening kink and removes global convexity across the illustrated range. See When Uncertainty Pays You.