Scientific stochastic volatility models for the European energy market: forecasting and extracting conditional volatility

This paper builds and implements a multifactor stochastic volatility model for the latent (and unobservable) volatility of the baseload and peakload forward contracts at the European Energy Exchange AG, 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 modeled in financial markets. The main objective is therefore to structure a scientific model that specifies volatility as having its own stochastic process. Appropriate stochastic model descriptions broaden the applications into derivative pricing purposes, risk management, 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 (filtering) one-step-ahead conditional volatility, evaluates shocks from conditional variance functions, analyzes multi-step-ahead dynamics and calculates conditional persistence measures. The analysis adds insight and enables forecasts to be made, building up the methodology for developing valid scientific commodity market models.