A Bayesian semiparametric approach to pricing the S&P 500 index options

Several studies incorporating estimated volatilities into option pricing formulas have appeared in the literature. However, the models described in these studies tend to perform quite poorly in out-of-sample tests. In particular, significant departures from the observed prices can be seen for the deep out-of-the-money short-term call options where mispricing seems to be somewhat excessive. This paper develops a new family of semiparametric Bayesian models. A particular member from this family that includes a nonparametric component is used to model option prices with the aim of improving the out-of-sample predictions. The principal advantage of injecting a nonparametric component into the model is that wide ranges of kurtosis in the observed asset prices are allowed, leading to lower pricing errors in out-of-sample predictions; that is, significant departures from normality in the underlying distribution of the asset prices when modeled lead to reliable forecasts. A detailed comparative empirical analysis with recent approaches to this problem is made for European out-of-the-money call options for which maturity does not exceed 40 days; it is for this subset of options that the pricing errors from other approaches are significant. The results indicate that the semiparametric Bayesian approach does better in terms of out-of-sample valuation errors compared with other approaches to the problem. Also, consistent with evidence reported in recent literature, for the group of short-term options exhibiting similar moneyness, pricing errors tend to decrease with the time to maturity.