Daily Inspiration Quote by John C. Hull

Hull’s sentence is a polite academic way of saying: beware the false comfort of a single risk number. Value at Risk (VaR) sells itself as clean, boardroom-friendly measurement, but the moment you step into nonlinear derivatives, that cleanliness is mostly cosmetic. Options don’t behave like linear instruments; their risk doesn’t scale nicely with small price moves. Hull name-checks gamma and vega to flag the real problem: VaR frameworks that assume stable, near-linear relationships (and often near-normal returns) can badly misread positions whose sensitivities bend and breathe.

The intent is corrective, not cynical. Hull is warning practitioners that measurement isn’t just hard; it’s structurally mismatched when the instrument’s payoff curvature (gamma) and volatility dependence (vega) dominate. Translation: if your model treats risk as a smooth slope, you’ll miss the cliff.

Subtext: “measurement” here isn’t a neutral technical hurdle. It’s a critique of institutional incentives. VaR became popular because it standardizes reporting and creates the appearance of control. But nonlinear books can hide risk in second-order effects until markets jump, volatility spikes, or correlations snap. Gamma risk turns modest moves into accelerating losses; vega risk makes the portfolio hostage to volatility regimes that VaR often under-samples.

Contextually, this is Hull speaking from the long shadow of risk-model failures (from LTCM to 2008) and from inside the pedagogical project of derivatives education: the numbers are only as honest as the sensitivities you respect. VaR isn’t useless; it’s incomplete, and nonlinear derivatives punish anyone who forgets that.