Algorithmic estimation of risk factors in financial markets with stochastic drift

We assume a financial market governed by a diffusion process reverting to a stochastic mean which is itself governed by an unobservable ergodic diffusion, similar to those observed in electricity and other energy markets. We develop a moment method algorithm for the estimation of the parameters of both the observable process and the unobservable stochastic mean. Our approach is contrasted with other methods for parameter estimation of partially observed diffusions, and applications to the modelling of interest rates and commodity prices are discussed.