Factor stochastic volatility with time varying loadings and Markov switching regimes

We generalize the factor stochastic volatility (FSV) model of Pitt and Shephard [1999. Time varying covariances: a factor stochastic volatility approach (with discussion). In: Bernardo, J.M., Berger, J.O., Dawid, A.P., Smith, A.F.M. (Eds.), Bayesian Statistics, vol. 6, Oxford University Press, London, pp. 547–570.] and Aguilar and West [2000. Bayesian dynamic factor models and variance matrix discounting for portfolio allocation. J. Business Econom. Statist. 18, 338–357.] in two important directions. First, we make the FSV model more flexible and able to capture more general time-varying variance–covariance structures by letting the matrix of factor loadings to be time dependent. Secondly, we entertain FSV models with jumps in the common factors volatilities through So, Lam and Li's [1998. A stochastic volatility model with Markov switching. J. Business Econom. Statist. 16, 244–253.] Markov switching stochastic volatility model. Novel Markov Chain Monte Carlo algorithms are derived for both classes of models. We apply our methodology to two illustrative situations: daily exchange rate returns [Aguilar, O., West, M., 2000. Bayesian dynamic factor models and variance matrix discounting for portfolio allocation. J. Business Econom. Statist. 18, 338–357.] and Latin American stock returns [Lopes, H.F., Migon, H.S., 2002. Comovements and contagion in emergent markets: stock indexes volatilities. In: Gatsonis, C., Kass, R.E., Carriquiry, A.L., Gelman, A., Verdinelli, I. Pauler, D., Higdon, D. (Eds.), Case Studies in Bayesian Statistics, vol. 6, pp. 287–302].     

Connectivity and Resilience: A Multidimensional Analysis of Infrastructure Impacts in the Southwestern Amazon

Infrastructure is a worldwide policy priority for national development via regional integration into the global economy. However, economic, ecological and social research draws contrasting conclusions about the consequences of infrastructure. We present a synthetic approach to the study of infrastructure, focusing on a multidimensional treatment of indicators of connectivity and resilience. As our study case, we adopt a tri-national frontier in the southwestern Amazon being integrated by a highway, and use survey data for rural leaders to evaluate the relationship of community connectivity to market towns and social-ecological resilience. The findings show varying relationships among different dimensions of connectivity and resilience, which bear implications regarding indicator approaches to the study of infrastructure impacts.     

High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics

We describe studies in molecular profiling and biological pathway analysis that use sparse latent factor and regression models for microarray gene expression data. We discuss breast cancer applications and key aspects of the modeling and computational methodology. Our case studies aim to investigate and characterize heterogeneity of structure related to specific oncogenic pathways, as well as links between aggregate patterns in gene expression profiles and clinical biomarkers. Based on the metaphor of statistically derived "factors" as representing biological "subpathway" structure, we explore the decomposition of fitted sparse factor models into pathway subcomponents and investigate how these components overlay multiple aspects of known biological activity. Our methodology is based on sparsity modeling of multivariate regression, ANOVA, and latent factor models, as well as a class of models that combines all components. Hierarchical sparsity priors address questions of dimension reduction and multiple comparisons, as well as scalability of the methodology. The models include practically relevant non-Gaussian/nonparametric components for latent structure, underlying often quite complex non-Gaussianity in multivariate expression patterns. Model search and fitting are addressed through stochastic simulation and evolutionary stochastic search methods that are exemplified in the oncogenic pathway studies. Supplementary supporting material provides more details of the applications, as well as examples of the use of freely available software tools for implementing the methodology.     

Estimating the stability of census-based racial/ethnic classifications: the case of Brazil

This study presents a method of estimating the degree to which people change their racial/ethnic identity from one census enumeration to another. The technique is applied to the classification of skin colour in Brazil (white, black, brown, yellow). For the period 1950-80, the findings show a deficit of 38 per cent in the black category and a gain of 34 per cent in the brown category, suggesting that a large proportion of individuals who declared themselves black in 1950 reclassified themselves as brown in 1980. Estimates for 1980-90, adjusted for the effects of international migration, reveal a similar pattern, although the magnitude of colour reclassification may have declined somewhat during the 1980s. Procedures to determine the stability of racial/ethnic identity produce data useful to recent policy initiatives that rely on demographic censuses to measure changes in the status of minority groups in less developed countries.     

Risk analysis in the brazilian stock market: copula-APARCH modeling for value-at-risk

Risk management of stock portfolios is a fundamental problem for the financial analysis since it indicates the potential losses of an investment at any given time. The objective of this study is to use bivariate static conditional copulas to quantify the dependence structure and to estimate the risk measure Value-at-Risk (VaR). There were selected stocks that have been performing outstandingly on the Brazilian Stock Exchange to compose pairs trading portfolios (B3, Gerdau, Magazine Luiza, and Petrobras). Due to the flexibility that this methodology offers in the construction of multivariate distributions and risk aggregation in finance, we used the copula-APARCH approach with the Normal, T-student, and Joe-Clayton copula functions. In most scenarios, the results showed a pattern of dependence at the extremes. Moreover, the copula form seems not to be relevant for VaR estimation, since in most portfolios the appropriate copulas lead to significant VaR estimates. It has found that the best models fitted provided conservative risk measures, estimates at 5% and 1%, in a scenario more aggressive.     

Metaheuristic algorithms for the bandwidth reduction of large-scale matrices

Journal of Combinatorial Optimization - This paper considers the bandwidth reduction problem for large-scale sparse matrices in serial computations. A heuristic for bandwidth reduction reorders the...