This paper accounts for fat tails and serial dependence for implied volatility index network connections among equity and commodity markets using Bayesian vector heterogeneous autoregressions. I analyse the information content of such connections over short-, medium- and long-horizons for...

We develop novel multivariate time series models using Bayesian additive regression trees that posit nonlinear relationships among macroeconomic variables, their lags, and possibly the lags of the errors. The variance of the errors can be stable, driven by stochastic volatility (SV), or follow a...

We consider extreme value analysis in a semi-supervised setting, where we observe, next to the n data on the target variable, n +m data on one or more covariates. This is called the semi-supervised model with n labeled and m unlabeled data. By exploiting the tail dependence between the target...

We introduce the new F-Riesz distribution to model tail-heterogeneity in fat-tailed covariance matrix observations. In contrast to the typical matrix-valued distributions from the econometric literature, the F-Riesz distribution allows for different tail behavior across all variables in the...

The metalog distributions represent a convenient way to approach many practical application. Their distinctive feature is simple closed-form expressions for quantile functions. This paper contributes to further development of the metalog distributions by deriving the closed-form expressions for...

In this paper we present a new asymptotically normal test for out-of-sample evaluation in nested models. Our approach is a simple modification of a traditional encompassing test that is commonly known as Clark and West test (CW). The key point of our strategy is to introduce an independent...

We study a general and efficient nonparametric density estimation procedure for local bases, including B-splines, using a novel statistical Galerkin method, combined with basis duality theory. We provide an efficient cross-validation procedure to select the bandwidth, based on closed-form...

A Bayesian dynamic compositional model is introduced that can deal with combining a large set of predictive densities. It extends the mixture of experts and the smoothly mixing regression models by allowing for combination weight dependence across models and time. A compositional model with...

We propose methods for constructing regularized mixtures of density forecasts. We explore a variety of objectives and regularization penalties, and we use them in a substantive exploration of Eurozone inflation and real interest rate density forecasts. All individual inflation forecasters...

We study the tails of closing auction return distributions for a sample of liquidEuropean stocks. We use the stochastic call auction model of Derksen et al. (2020a),to derive a relation between tail exponents of limit order placement distributions andtail exponents of the resulting closing...