Modeling the Structure and Dynamics of Energy, Commodity and Alternative Asset Prices

The main goal of our project is to apply and extend some of the recently established techniques in financial econometrics and computational finance in order to develop new accurate univariate and multivariate models of energy and alternative assets, models of commodity futures term structure and volatility spillovers between commodity, equity, and credit markets. We will focus on specific issues such as estimation of volatility and valuation of energy options on non-liquid markets, or valuation of EU emission allowance derivatives. Among the main econometric techniques that will be used are the utilization of high-frequency data for more accurate estimations of the latent states (stochastic volatilities, jumps and covariance matrices) of econometric models, and the use of modern Bayesian methods to estimate complex financial models with large number of latent state variables (Particle MCMC and particle learning).