Ensemble prediction systems aim to account for uncertainties of initial conditions and model error. Ensemble forecasting is sometimes viewed as a method of obtaining (objective) probabilistic forecasts. How is one to judge the quality of an ensemble at forecasting a system? The probability that...

We propose a model for trading in emission allowances in the EU Emission Trading Scheme (ETS). Exploiting an arbitrage relationship we derive the spot prices of carbon allowances given a forward contract whose price is exogenous to the model. The modeling is done under the assumption of no...

This edited volume features cutting-edge topics from the leading researchers in the areas of latent variable modeling. Content highlights include coverage of approaches dealing with missing values, semi-parametric estimation, robust analysis, hierarchical data, factor scores, multi-group...

Dye [J Account Res 23 (1985) 123] showed that the optimal disclosure policy, when a manager is randomly endowed with perfect private information, is upper tailed, i.e., the manager only discloses firm value above an appropriate cutoff level. We interpret this strategically as an optimal exercise...

ARCH/GARCH representations of financial series usually attempt to model the serial correlation structure of squared returns. Although it is undoubtedly true that squared returns are correlated, there is increasing empirical evidence of stronger correlation in the absolute returns than in squared...

Moving from univariate to bivariate jointly dependent long memory time series introduces a phase parameter (γ), at the frequency of principal interest, zero; for short memory series γ = 0 automatically. The latter case has also been stressed under long memory, along with the "fractional...

This volume is based on lectures presented at the AMS Special Session on Algebraic Methods in Statistics and Probability--held March 27-29, 2009, at the University of Illinois at Urbana-Champaign--and on contributed articles solicited for this volume. A decade after the publication of...

This paper presents a Markov chain Monte Carlo algorithm for a class of multivariate diffusion models with unobserved paths. This class is of high practical interest as it includes most diffusion driven stochastic volatility models. The algorithm is based on a data augmentation scheme where the...

Parameter estimation in nonlinear models is a common task, and one for which there is no general solution at present. In the case of linear models, the distribution of forecast errors provides a reliable guide to parameter estimation, but in nonlinear models the facts that predictability may...

Cointegrated bivariate nonstationary time series are considered in a fractional context, without allowance for deterministic trends. Both the observable series and the cointegrating error can be fractional processes. The familiar situation in which the respective integration orders are 1 and 0...