Zobrazeno 1 - 10
of 62
pro vyhledávání: '"Lopes, Hedibert Freitas"'
Autor:
Martins, Igor, Lopes, Hedibert Freitas
This paper expands on stochastic volatility models by proposing a data-driven method to select the macroeconomic events most likely to impact volatility. The paper identifies and quantifies the effects of macroeconomic events across multiple countrie
Externí odkaz:
http://arxiv.org/abs/2411.16244
This paper expands traditional stochastic volatility models by allowing for time-varying skewness without imposing it. While dynamic asymmetry may capture the likely direction of future asset returns, it comes at the risk of leading to overparameteri
Externí odkaz:
http://arxiv.org/abs/2312.00282
We discuss efficient Bayesian estimation of dynamic covariance matrices in multivariate time series through a factor stochastic volatility model. In particular, we propose two interweaving strategies (Yu and Meng, Journal of Computational and Graphic
Externí odkaz:
http://epub.wu.ac.at/4875/1/research_report_updated.pdf
There has been increased research interest in the subfield of sparse Bayesian factor analysis with shrinkage priors, which achieve additional sparsity beyond the natural parsimonity of factor models. In this spirit, we estimate the number of common f
Externí odkaz:
http://arxiv.org/abs/2301.06459
Despite the popularity of factor models with sparse loading matrices, little attention has been given to formally address identifiability of these models beyond standard rotation-based identification such as the positive lower triangular (PLT) constr
Externí odkaz:
http://arxiv.org/abs/2301.06354
The inclusion of the propensity score as a covariate in Bayesian regression trees for causal inference can reduce the bias in treatment effect estimations, which occurs due to the regularization-induced confounding phenomenon. This study advocate for
Externí odkaz:
http://arxiv.org/abs/1808.09507
Despite the popularity of sparse factor models, little attention has been given to formally address identifiability of these models beyond standard rotation-based identification such as the positive lower triangular constraint. To fill this gap, we p
Externí odkaz:
http://arxiv.org/abs/1804.04231
Heavy tailed distributions present a tough setting for inference. They are also common in industrial applications, particularly with Internet transaction datasets, and machine learners often analyze such data without considering the biases and risks
Externí odkaz:
http://arxiv.org/abs/1602.08066
Publikováno v:
Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part B
Publikováno v:
Journal of Computational and Graphical Statistics, 2017 Dec 01. 26(4), 905-917.
Externí odkaz:
https://www.jstor.org/stable/44862019
