Testing the Stability of Implied Probability Density Functions

Implied probability density functions (PDFs) estimated from cross-sections of observed option prices are gaining increasing attention amongst academics and practitioners. However, to date little attention has been paid to the robustness of these estimates or to the confidence users can place in the summary statistics, for example skewness or the 99th percentile, derived from fitted PDFs. This paper begins to address these questions by examining the absolute and relative robustness of two of the most common methods for estimating implied PDFs---the double-lognormal approximating function and the smoothed implied volatility smile methods. The changes resulting from randomly perturbing quoted prices by no more than a half tick provide a lower bound on the confidence intervals of the summary statistics derived from the estimated PDFs. Tests are conducted using options contracts tied to Short Sterling futures and the FTSE 100 index---both trading on the London International Financial Futures Exchange. Our tests show that the smoothed implied volatility smile method dominates the double-lognormal as a technique for estimating implied PDFs when average goodness-of-fits are comparable for both methods