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

Tissue-nonspecific alkaline phosphatase promotes axonal growth of hippocampal neurons

Axonal growth is essential for establishing neuronal circuits during brain development and for regenerative processes in the adult brain. Unfortunately, the extracellular signals controlling axonal growth are poorly understood. Here we report that a reduction in extracellular ATP levels by tissue-nonspecific alkaline phosphatase (TNAP) is essential for the development of neuritic processes by cultured hippocampal neurons. Selective blockade of TNAP activity with levamisole or specific TNAP knockdown with short hairpin RNA interference inhibited the growth and branching of principal axons, whereas addition of alkaline phosphatase (ALP) promoted axonal growth. Neither activation nor inhibition of adenosine receptors affected the axonal growth, excluding the contribution of extracellular adenosine as a potential hydrolysis product of extracellular ATP to the TNAP-mediated effects. TNAP was colocalized at axonal growth cones with ionotropic ATP receptors (P2X7 receptor), whose activation inhibited axonal growth. Additional analyses suggested a close functional interrelation of TNAP and P2X7 receptors whereby TNAP prevents P2X7 receptor activation by hydrolyzing ATP in the immediate environment of the receptor. Furthermore inhibition of P2X7 receptor reduced TNAP expression, whereas addition of ALP enhanced P2X7 receptor expression. Our results demonstrate that TNAP, regulating both ligand availability and protein expression of P2X7 receptor, is essential for axonal development