Some lessons stay with you, even when the asset class doesn't.
My undergraduate thesis at ZHAW School of Management and Law was on volatility arbitrage. Specifically, on algorithm-based strategies that use futures contracts to capture risk premia when implied volatility diverges meaningfully from realized volatility.
The core idea was straightforward in principle and technically demanding in practice. When markets are under stress, fear tends to overprice risk. Implied volatility, the market's expectation of future turbulence, runs ahead of what subsequently materializes. If you can identify that gap systematically, define entry and exit rules with discipline, and structure the position to survive the inevitable periods when the gap widens before it closes, you can build a repeatable edge out of other people's panic.
What fascinated me was not the mechanics. It was the underlying logic: that mispricing driven by emotion is structural and predictable, that asymmetry is a design choice rather than a lucky outcome, and that the difference between a signal and a strategy is the process that sits between them.
What It Taught Me About Risk
Volatility arbitrage does not reward being right about direction. It rewards being right about structure.
You can have a correct view on where volatility is headed and still lose money if the position is sized wrong, if the exit rules are too loose, or if the instrument you chose introduces basis risk you had not accounted for. The forecast is almost secondary. What determines the outcome is whether the framework around the forecast is sound.
I did not fully appreciate how portable that lesson was until I started working in private markets.
The Translation
Private market investing operates on a different time horizon and with far less liquidity than options markets. The instruments are different, the feedback loops are slower, and the data is harder to come by. On the surface, the two worlds have little in common.
But the underlying discipline is the same.
At Paligan, we do not try to predict which company will become an outlier. Prediction of that kind is largely beyond reach, and investors who believe otherwise tend to be pattern-matching rather than analysing. What we do instead is underwrite the structure: the entry terms, the conversion mechanics, the cap table dynamics, the downside containment. We try to build positions where the asymmetry is in our favour before the outcome is known, in the same way a volatility strategy tries to capture a structural mispricing before the gap closes.
When a market becomes fear-driven, implied volatility tends to overshoot. When a sector falls out of fashion in venture, valuations can undershoot. The mechanism is different. The opportunity created by emotional pricing is structurally similar.
Signal Without Process Is Noise
The finding from my thesis that has stayed with me most directly is this: a signal without a process is just noise.
In quantitative strategies, directional accuracy is not sufficient. The position needs to be built correctly, sized appropriately, and managed according to rules defined before the trade is entered. Improvising under pressure is how good signals produce bad outcomes.
The same is true in private investing. A strong initial impression of a company, a compelling founder, a market that feels right, these are signals. Whether they translate into a good investment depends entirely on the process applied to them. How deeply the business model was examined. Whether the terms were structured to reflect the actual risk. Whether the conviction was earned through analysis or formed through enthusiasm.
We do not forecast. We structure. The present is what we can actually work with, and getting the present right is what gives the future its best chance.
Closing
I wrote that thesis as an academic exercise. I did not expect it to become a reference point for how I think about investing.
What it gave me was a precise vocabulary for something I now apply differently but consistently: that edge in investing, as in trading, does not come from knowing more than everyone else about what will happen. It comes from building a framework that produces better decisions under uncertainty, and having the discipline to stay inside it when the environment pushes back.
The asset class changed. The logic did not.
