A0350
Title: Probabilistic predictions of option prices using multiple sources of data
Authors: Ole Maneesoonthorn - Monash University (Australia) [presenting]
Abstract: A new modular approximate Bayesian inferential framework is proposed that enables fast calculation of probabilistic predictions of future option prices. Multiple information sources are exploited, including daily spot returns, high-frequency spot data, and option prices. A benefit of this modular Bayesian approach is that it allows working with the theoretical option pricing model without needing to specify an arbitrary statistical model that links the theoretical prices to their observed counterparts. It is shown that the approach produces accurate probabilistic predictions of option prices in realistic scenarios, and despite not explicitly modeling pricing errors, the method is shown to be robust to their presence. Predictive accuracy based on the Heston stochastic volatility model, with predictions produced via rapid real-time updates, is illustrated empirically for short-maturity options.
