One of the core assumptions behind most of quantitative finance is that stock returns are independent. What happened yesterday tells you nothing about today. The stock went up ten days in a row? Irrelevant. Tomorrow is a fresh coin toss. But is this really true? Chapter 65 of Wilmott’s book looks at the evidence for serial autocorrelation in stock returns and asks what happens to our models when returns are not independent.
Here is a frustrating reality of stochastic volatility models. You pick a model because it is tractable (Heston, anyone?). You get nice semi-closed-form solutions. But what if the model does not actually describe reality well? You have traded accuracy for mathematical convenience, and in finance, that trade can cost you real money.
Chapter 6 is called “Debiasing.” After five chapters of telling us how broken our brains are, Richards finally starts talking about what we can actually do about it. The short answer: there is no magic fix. But there are tools that help.