Gaussian Process Conditional Copulas with Applications to Financial Time Series

The estimation of dependencies between multiple variables is a central problem in the analysis of financial time series. A common approach is to express these dependencies in terms of a copula function. Typically the copula function is assumed to be constant but this may be inaccurate when there are covariates that could have a large influence on the dependence structure of the data. To account for this, a Bayesian framework for the estimation of conditional copulas is proposed. In this framework the parameters of a copula are non-linearly related to some arbitrary conditioning variables. We evaluate the ability of our method to predict time-varying dependencies on several equities and currencies and observe consistent performance gains compared to static copula models and other time-varying copula methods

MODELLING SEGREGATION THROUGH CELLULAR AUTOMATA: A THEORETICAL ANSWER

This paper is a note in which we prove that Cellular Automata are suitable tools to model multi-agent interactive procedures. In particular, we apply the argument to validate results from simulation tools obtained for the classical model of segregation of Thomas Schelling (1971a).Cellular Automata, segregation, local information

Report on identifying a protocol to elicit flowering in Brachiaria humidicola with photoperiod management

Two Genotypes of Brachiaria humidicola (A and B) were planted on the grounds of CIAT headquarters in Palmira during 2018 – 2019, 10 lamps were placed in the lot to evaluate 6 different photoperiods (1 - 6) with Light in 2 different wavelength range (W.R.) α and β, for this, 17 samples were carried out on the variables height, vigor, chlorophyll content and number of inflorescences; a total of 93 field work were carried out to support the trial, finding that the photoperiod 5 in the W.R. β and 3 photoperiod in the W.R. α for the B genotype show significant differences (p <0.05, Tukey) with respect to the other treatments for height and number of inflorescences, performing the statistical analysis in the SAS software. As to the seed production, it was found that any light stimulus generates greater seed production, despite the conditions under which the crops were made and the method of harvest used. I order to refine the protocol and validate the results in bigger genotype sample another trial with the 2 most efficient treatments was proposed for 2020, focusing on number of inflorescences and seed production