Generating Options-Implied Probability Densities to Understand Oil Market Events

We investigate the informational content of options-implied probability density functions (PDFs) for the future price of oil. Using a semiparametric variant of the methodology in Breeden and Litzenberger (1978), we investigate the fit and smoothness of distributions derived from alternative PDF estimation methods, and develop a set of robust summary statistics. Using PDFs estimated around episodes of high geopolitical tensions, oil supply disruptions, and macroeconomic data releases, we explore the extent to which oil price movements are expected or unexpected, and whether agents believe these movements to be persistent or temporary.