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.
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