Anchoring Heuristic in Option Pricing

Abstract

A pragmatic approach to approximating Bayesian inference is relying on an informative starting point and then attempting to adjust it properly. Evidence suggests that underlying stock volatility is such a starting point, which is scaled-up to estimate call option volatility. Theoretically, I adjust state-of-the-art option pricing models for reliance on this informative starting point. Empirically, I show that Heston stochastic volatility model adjusted for the informative starting point matches the same data better, does so at more plausible values, while generating a steep short term skew. Furthermore, two novel predictions, arising from reliance on the informative starting point, are empirically tested and found to be strongly supported.