This paper extends the existing fully parametric Bayesian literature on stochastic volatility to allow for more general return distributions. Instead of specifying a particular distribution for the return innovation, we use nonparametric Bayesian methods to flexibly model the skewness and...

This paper proposes a Bayesian nonparametric modeling approach for the return distribution in multivariate GARCH models. In contrast to the parametric literature, the return distribution can display general forms of asymmetry and thick tails. An infinite mixture of multivariate normals is given...

In this paper, we extend the parametric, asymmetric, stochastic volatility model (ASV), where returns are correlated with volatility, by flexibly modeling the bivariate distribution of the return and volatility innovations nonparametrically. Its novelty is in modeling the joint, conditional,...

In this paper, we use Bayesian nonparametric learning to estimate the skill of actively managed mutual funds and also to estimate the population distribution for this skill. A nonparametric hierarchical prior, where the hyperprior distribution is unknown and modeled with a Dirichlet process...

This paper considers Bayesian nonparametric estimation of conditional densities by countable mixtures of location-scale densities with covariate dependent mixing probabilities. The mixing probabilities are modeled in two ways. First, we consider finite covariate dependent mixture models, in...

We consider nonparametric estimation of a mixed discrete-continuous distribution under anisotropic smoothness conditions and possibly increasing number of support points for the discrete part of the distribution. For these settings, we derive lower bounds on the estimation rates in the total...

This paper presents a framework for empirical analysis of dynamic macroeconomic models using Bayesian Öltering, with a speciÖc focus on the state-space formulation of Dynamic Stochastic General Equilibrium (DSGE) models with multiple regimes. We outline the theoretical foundations of model...

What drives the recent inflation surge? To answer this question, one must decompose inflation fluctuations into the contribution of structural shocks. We document how whimsical such a historical shock decomposition can be in standard vector autoregressive (VAR) models. We show that the...

They do. Partly. We identify credit supply shocks via sign restrictions in a Bayesian VAR and separate them into positive and negative. Using local projections, we find that positive credit supply shocks leave notably different prints in private debt, mortgage debt, and debt-to-GDP, as opposed...

This paper uses a Bayesian Structural Vector Autoregressive (BSVAR) framework to estimate the pass-through of unexpected gas price supply shocks on HICP inflation in the euro area and its four largest economies. In comparison to oil price shocks, gas price shocks have approximately one-third...