Multiproduct Optimal Hedging by Time-Varying Correlations in a State Dependent model of Regime-Switching

Replaced with revised version of paper 07/29/10.Agribusiness, Demand and Price Analysis, Risk and Uncertainty,

Price Volatility, Nonlinearity, and Asymmetric Adjustments in Corn, Soybean, and Cattle Markets: Implications of Ethanol-Driven (Market) Shocks

Grain prices have risen sharply since 2005 and 2006 affecting livestock markets by increasing feed prices and leading to significant volatility shocks. The high price levels and magnitude of sustained high volatilities has raised concerns for many sectors of the economy, in particular those with direct relation to these markets. Policy makers are analyzing the interrelationships among these markets, and the effects of energy market shocks on agricultural markets. This study considers a threshold structure in a multivariate time-series model that evaluates these market linkages, capturing asymmetric correlations between grain and livestock prices, including volatility spillovers. We empirically study the impact of corn usage for ethanol production in the evolution of the above mentioned prices. Results are compared to previous scenarios where corn, soybean and livestock production and consumption did not face the corn demand for ethanol production. We find positive dynamic correlations between corn and soybean and feeder and fed cattle prices, consistent with the literature. And we find an inverse or negative relation between corn and feeder/calf prices for the period post mandated ethanol production, as anticipated by the literature for increased corn prices. Also, we find there are adjustment costs inhibiting price transmission between the crops and the live cattle market, in the form of modifying feeding rations. More relevantly, we identify plausible asymmetric effect on the correlations between the markets, especially when considering the period for the ethanol driven corn consumption versus previous periods of corn consumption. These asymmetric correlations are the result of spillover effects.price volatility, market linkages, thresholds, ethanol-driven shocks, asymmetric correlations, spillovers, Agribusiness, Agricultural and Food Policy, Agricultural Finance, Demand and Price Analysis, Farm Management, Financial Economics, Public Economics, Research Methods/ Statistical Methods,

Latent tree models

Latent tree models are graphical models defined on trees, in which only a subset of variables is observed. They were first discussed by Judea Pearl as tree-decomposable distributions to generalise star-decomposable distributions such as the latent class model. Latent tree models, or their submodels, are widely used in: phylogenetic analysis, network tomography, computer vision, causal modeling, and data clustering. They also contain other well-known classes of models like hidden Markov models, Brownian motion tree model, the Ising model on a tree, and many popular models used in phylogenetics. This article offers a concise introduction to the theory of latent tree models. We emphasise the role of tree metrics in the structural description of this model class, in designing learning algorithms, and in understanding fundamental limits of what and when can be learned

Sparse Linear Identifiable Multivariate Modeling

In this paper we consider sparse and identifiable linear latent variable (factor) and linear Bayesian network models for parsimonious analysis of multivariate data. We propose a computationally efficient method for joint parameter and model inference, and model comparison. It consists of a fully Bayesian hierarchy for sparse models using slab and spike priors (two-component delta-function and continuous mixtures), non-Gaussian latent factors and a stochastic search over the ordering of the variables. The framework, which we call SLIM (Sparse Linear Identifiable Multivariate modeling), is validated and bench-marked on artificial and real biological data sets. SLIM is closest in spirit to LiNGAM (Shimizu et al., 2006), but differs substantially in inference, Bayesian network structure learning and model comparison. Experimentally, SLIM performs equally well or better than LiNGAM with comparable computational complexity. We attribute this mainly to the stochastic search strategy used, and to parsimony (sparsity and identifiability), which is an explicit part of the model. We propose two extensions to the basic i.i.d. linear framework: non-linear dependence on observed variables, called SNIM (Sparse Non-linear Identifiable Multivariate modeling) and allowing for correlations between latent variables, called CSLIM (Correlated SLIM), for the temporal and/or spatial data. The source code and scripts are available from http://cogsys.imm.dtu.dk/slim/.Comment: 45 pages, 17 figure

Bayesian Geoadditive Seemingly Unrelated Regression

Parametric seemingly unrelated regression (SUR) models are a common tool for multivariate regression analysis when error variables are reasonably correlated, so that separate univariate analysis may result in inefficient estimates of covariate effects. A weakness of parametric models is that they require strong assumptions on the functional form of possibly nonlinear effects of metrical covariates. In this paper, we develop a Bayesian semiparametric SUR model, where the usual linear predictors are replaced by more flexible additive predictors allowing for simultaneous nonparametric estimation of such covariate effects and of spatial effects. The approach is based on appropriate smoothness priors which allow different forms and degrees of smoothness in a general framework. Inference is fully Bayesian and uses recent Markov chain Monte Carlo techniques