Portfolio optimization with mixture vector autoregressive models

Obtaining reliable estimates of conditional covariance matrices is an important task of heteroskedastic multivariate time series. In portfolio optimization and financial risk management, it is crucial to provide measures of uncertainty and risk as accurately as possible. We propose using mixture vector autoregressive (MVAR) models for portfolio optimization. Combining a mixture of distributions that depend on the recent history of the process, MVAR models can accommodate asymmetry, multimodality, heteroskedasticity and cross-correlation in multivariate time series data. For mixtures of Normal components, we exploit a property of the multivariate Normal distribution to obtain explicit formulas of conditional predictive distributions of returns on a portfolio of assets. After showing how the method works, we perform a comparison with other relevant multivariate time series models on real stock return data.Comment: 19 pages, 9 figures, 2 table

Pion and kaon condensation in a 3-flavor NJL model

We analyze the phase diagram of a three-flavor Nambu-Jona-Lasinio model at finite temperature $T$ and chemical potentials $\mu_u, \mu_d, \mu_s$. We study the competition of pion and kaon condensation and we propose a physical situation in which kaon condensation could be led only by light quark finite densities.Comment: 21 pages, 8 figures include

JRLV at SemEval-2022 Task 5: The Importance of Visual Elements for Misogyny Identification in Memes

Gender discrimination is a serious and widespread problem on social media and online in general. Besides offensive messages, memes are one of the main means of dissemination for such content. With these premises, the MAMI task was proposed at the SemEval-2022, which consists of identifying memes with misogynous characteristics. In this work, we propose a solution to this problem based on Mask R-CNN and VisualBERT that leverages the multimodal nature of the task. Our study focuses on observing how the two sources of data in memes (text and image) and their possible combinations impact performances. Our best result slightly exceeds the higher baseline, but the experiments allowed us to draw important considerations regarding the importance of correctly exploiting the visual information and the relevance of the elements present in the memes images

Quark coalescence based on a transport equation

We employ the Boltzmann equation for describing hadron production from a quark-gluon plasma (QGP) in ultrarelativistic heavy-ion collisions. We propose resonance formation in quark-antiquark scattering as the dominant meson-production channel, which, in particular, ensures that energy is conserved in the recombination process. This, in turn, facilitates a more controlled extension of hadronization to low transverse momenta ($p_T$), and to address the experimentally observed transition from a hydrodynamic regime to constituent quark-number scaling (CQNS). Based on input distributions for strange and charm quarks with azimuthal asymmetries, $v_2(p_T)$, characteristic for RHIC energies, we recover CQNS at sufficiently high $p_T$, while at low $p_T$ a scaling with transverse kinetic energy is found, reminiscent to experiment. The dependence of the transition regime on microscopic QGP properties, i.e. resonance widths and $Q$-values in the $q+\bar q \to M$ process, is elucidated.Comment: 7 pages, 6 figure

Bayesian analysis of mixture autoregressive models covering the complete parameter space

Mixture autoregressive (MAR) models provide a flexible way to model time series with predictive distributions which depend on the recent history of the process and are able to accommodate asymmetry and multimodality. Bayesian inference for such models offers the additional advantage of incorporating the uncertainty in the estimated models into the predictions. We introduce a new way of sampling from the posterior distribution of the parameters of MAR models which allows for covering the complete parameter space of the models, unlike previous approaches. We also propose a relabelling algorithm to deal a posteriori with label switching. We apply our new method to simulated and real datasets, discuss the accuracy and performance of our new method, as well as its advantages over previous studies. The idea of density forecasting using MCMC output is also introduced.Comment: 27 pages, 10 figures, 4 table

A NJL-based study of the QCD critical line

We employ a 3 flavor NJL model to stress some general remarks about the QCD critical line. The dependence of the critical curve on $\mu_q=(\mu_u+\mu_d)/2$ and $\mu_I=(\mu_u-\mu_d)/2$ is discussed. The quark masses are varied to confirm that, in agreement with universality arguments, the order of transition depends on the number of active flavors $N_f$. The slope of the critical curve vs. chemical potential is studied as a function of $N_f$. We compare our results with those recently obtained in lattice simulations to establish a comparison among different models.Comment: 17 pages, 5 figure