Stochastic volatility models for the Brent oil futures market: forecasting and extracting conditional moments

This paper builds and implements a multifactor stochastic volatility model for the latent (and observable) volatility from the front month future contracts at the Intercontinental Commodity Exchange (ICE), London, applying Bayesian Markov chain Monte Carlo simulation methodologies for estimation, inference and model adequacy assessment. Stochastic volatility is the main way time-varying volatility is modelled in financial markets. An appropriate scientific model description, specifying volatility as having its own stochastic process, broadens the applications into derivative pricing purposes, risk assessment and asset allocation and portfolio management. From an estimated optimal and appropriate stochastic volatility model, the paper reports risk and portfolio measures, extracts conditional one-step-ahead moments (smoothing), forecasts one-step-ahead conditional volatility (filtering), evaluates shocks from conditional variance functions, analyses multistep-ahead dynamics and calculates conditional persistence measures. (Exotic) option prices can be calculated using the re-projected conditional volatility. Observed market prices and implied volatilities establish market risk premiums. The analysis adds insight and enables forecasts to be made, building up the methodology for developing valid scientific commodity market models.