This paper presents the R-package MitISEM (mixture of t by importance sampling weighted expectation maximization) which provides an automatic and flexible two-stage method to approximate a non-elliptical target density kernel -- typically a posterior density kernel -- using an adaptive mixture...

A class of adaptive sampling methods is introduced for efficient posterior and predictive simulation. The proposed methods are robust in the sense that they can handle target distributions that exhibit non-elliptical shapes such as multimodality and skewness. The basic method makes use of...

We introduce the vector-valued t-Riesz distribution for time series models of electricity prices. The t-Riesz distribution extends the well-known Multivariate Student's t distribution by allowing for tail heterogeneity via a vector of degrees of freedom (DoF) parameters. The closed-form density...

This paper disentangles the added value of using high-frequency-based (realized) covariance measures on multivariate volatility forecasting into two pillars: the realized variances and realized correlations and quantifies the corresponding economic gains using a broad set of portfolio...

This paper compares the statistical and economic performance of state-of-the-art highfrequency based multivariate volatility models with a simpler, widely used alternative-the Exponentially Weighted Moving Average (EWMA) filter. Using over two decades of 100 U.S. stock returns (2002-2023), we...