Archivio Istituzionale della Ricercahttps://iris.unive.itIl sistema di repository digitale IRIS acquisisce, archivia, indicizza, conserva e rende accessibili prodotti digitali della ricerca.Fri, 22 Jan 2021 05:59:28 GMT2021-01-22T05:59:28Z10101Contributed discussion to Using Stacking to Average Bayesian Predictive Distributionshttp://hdl.handle.net/10278/3704365Titolo: Contributed discussion to Using Stacking to Average Bayesian Predictive Distributions
Abstract: The discussion suggests to couple the model selection methodology proposed in the main paper with a Bayesian non parametric method, in order to extend its applicability to more general contexts. A simulation study illustrates the improvement of performance in small samples.
Mon, 01 Jan 2018 00:00:00 GMThttp://hdl.handle.net/10278/37043652018-01-01T00:00:00ZSparse graphs using exchangeable random measureshttp://hdl.handle.net/10278/3691785Titolo: Sparse graphs using exchangeable random measures
Sun, 01 Jan 2017 00:00:00 GMThttp://hdl.handle.net/10278/36917852017-01-01T00:00:00ZBayesian Tensor Binary Regressionhttp://hdl.handle.net/10278/3700828Titolo: Bayesian Tensor Binary Regression
Mon, 01 Jan 2018 00:00:00 GMThttp://hdl.handle.net/10278/37008282018-01-01T00:00:00ZBayesian Tensor Regression Modelshttp://hdl.handle.net/10278/3700829Titolo: Bayesian Tensor Regression Models
Mon, 01 Jan 2018 00:00:00 GMThttp://hdl.handle.net/10278/37008292018-01-01T00:00:00ZNonparametric forecasting of multivariate probability density functionshttp://hdl.handle.net/10278/3700804Titolo: Nonparametric forecasting of multivariate probability density functions
Abstract: The study of dependence between random variables is the core of theoretical and applied statistics. Static and dynamic copula models are useful for describing the dependence structure, which is fully encrypted in the copula probability density function. However, these models are not always able to describe the temporal change of the dependence patterns, which is a key characteristic of nancial data.
We propose a novel nonparametric framework for modelling a time series of copula probability density functions, which allows to forecast the entire function without the need of post-processing procedures to grant positiveness and unit integral. We exploit a suitable isometry that allows to transfer the analysis in a subset of the space of square integrable functions, where we build on nonparametric functional data analysis techniques to perform the analysis.
The framework does not assume the densities to belong to any parametric family and it can be successfully applied also to general multivariate probability density functions with bounded or unbounded support. Finally, a noteworthy eld of application pertains the study of time varying networks represented through vine copula models.
We apply the proposed methodology for estimating and forecasting the time varying dependence structure between the S&P500 and NASDAQ indices
Mon, 01 Jan 2018 00:00:00 GMThttp://hdl.handle.net/10278/37008042018-01-01T00:00:00ZBayesian Markov switching tensor regression for time-varying networkshttp://hdl.handle.net/10278/3700802Titolo: Bayesian Markov switching tensor regression for time-varying networks
Abstract: We propose a new Bayesian Markov switching regression model for multi-dimensional arrays (tensors) of binary time series. We assume a zero-inflated logit dynamics with time-varying parameters and apply it to multi-layer temporal networks. The original contribution is threefold. First, in order to avoid over-fitting we propose a parsimonious parametrization of the model, based on a low-rank decomposition of the tensor of regression coefficients. Second, the parameters of the tensor model are driven by a hidden Markov chain, thus allowing for structural changes. The regimes are identied through prior constraints on the mixing probability of the zero-inflated model. Finally, we model the jointly dynamics of the network and of a set of variables of interest. We follow a Bayesian approach to inference, exploiting the Polya-Gamma data augmentation scheme for logit models in order to provide an efficient Gibbs sampler for posterior approximation. We show the effectiveness of the sampler on simulated datasets of medium-big sizes, nally we apply the methodology to a real dataset of nancial networks.
Mon, 01 Jan 2018 00:00:00 GMThttp://hdl.handle.net/10278/37008022018-01-01T00:00:00ZBayesian dynamic tensor regressionhttp://hdl.handle.net/10278/3700801Titolo: Bayesian dynamic tensor regression
Abstract: Multidimensional arrays (i.e. tensors) of data are becoming increasingly available and call for suitable econometric tools. We propose a new dynamic linear regression model for tensor-valued response variables and covariates that encompasses some well known multivariate models such as SUR, VAR, VECM, panel VAR and matrix regression models as special cases. For dealing with the over-parametrization and over-fitting issues due to the curse of dimensionality, we exploit a suitable parametrization based on the parallel factor (PARAFAC) decomposition which enables to achieve both parameter parsimony and to incorporate sparsity effects. Our contribution is twofold: first, we provide an extension of multivariate econometric models to account for both tensor-variate response and covariates; second, we show the effectiveness of proposed methodology in defining an autoregressive process for time-varying real economic networks. Inference is carried out in the Bayesian framework combined with Monte Carlo Markov Chain (MCMC). We show the efficiency of the MCMC procedure on simulated datasets, with different size of the response and independent variables, proving computational efficiency even with high-dimensions of the parameter space. Finally, we apply the model for studying the temporal evolution of real economic networks.
Mon, 01 Jan 2018 00:00:00 GMThttp://hdl.handle.net/10278/37008012018-01-01T00:00:00ZBasics of optimization theory with applications in MATLAB and Rhttp://hdl.handle.net/10278/3700807Titolo: Basics of optimization theory with applications in MATLAB and R
Fri, 01 Jan 2016 00:00:00 GMThttp://hdl.handle.net/10278/37008072016-01-01T00:00:00ZBayesian Tensor Regression Modelshttp://hdl.handle.net/10278/3691747Titolo: Bayesian Tensor Regression Models
Sun, 01 Jan 2017 00:00:00 GMThttp://hdl.handle.net/10278/36917472017-01-01T00:00:00ZCOVID-19 spreading in financial networks: A semiparametric matrix regression modelhttp://hdl.handle.net/10278/3735307Titolo: COVID-19 spreading in financial networks: A semiparametric matrix regression model
Abstract: Network models represent a useful tool to describe the complex set of financial relationships among heterogeneous firms in the system. In this paper, we propose a new semiparametric model for temporal multilayer causal networks with both intra- and inter-layer connectivity. A Bayesian model with a hierarchical mixture prior distribution is assumed to capture heterogeneity in the response of the network edges to a set of risk factors including the European COVID-19 cases. We measure the financial connectedness arising from the interactions between two layers defined by stock returns and volatilities. In the empirical analysis, we study the topology of the network before and after the spreading of the COVID-19 disease.
Fri, 01 Jan 2021 00:00:00 GMThttp://hdl.handle.net/10278/37353072021-01-01T00:00:00Z