The purpose of this study is to investigate the relationship between investor sentiment and leading equity market indices from the U.S., Europe, Asia, and globally between January 2020 and June 2022. The methodological approaches utilized are quantile regression and wavelet analysis. The results...

Outlier detection in high-dimensional datasets poses new challenges that have not been investigated in the literature. In this paper, we present an integrated methodology for the identification of outliers which is suitable for datasets with higher number of variables than observations. Our...

This document provides an overview of the StMAR Toolbox, a MATLAB toolbox specifically designed for simulation, estimation, diagnostic, and forecasting of the Student's t mixture autoregressive (StMAR) model proposed by Meitz, Preve & Saikkonen (2018). The StMAR model is a new type of mixture...

Using a Markov switching unobserved component model we decompose the term premium of the North American CDX investment grade index (CDX-IG) into a permanent and a stationary component. We explain the evolution of the two components in relating them to monetary policy and stock market variables....

Using a Markov switching unobserved component model we decompose the term premium of the North American CDX investment grade index (CDX-IG) into a permanent and a stationary component. We explain the evolution of the two components in relating them to monetary policy and stock market variables....

Using a Markov switching unobserved component model we decompose the term premium of the North American CDX investment grade index (CDX-IG) into a permanent and a stationary component. We explain the evolution of the two components in relating them to monetary policy and stock market variables....

This paper develops methods for the production and evaluation of censored density forecasts. Censored density forecasts quantify forecast risks in a middle region of the density covering a specified probability, and ignore the magnitude but not the frequency of outlying observations. We propose...

We introduce a copula-based dynamic model for multivariate processes of (non-negative) high-frequency trading variables revealing time-varying conditional variances and correlations. Modeling the variables' conditional mean processes using a multiplicative error model we map the resulting...