We introduce a novel parametrization of the correlation matrix. The reparametrization
facilitates modeling of correlation and covariance matrices by an unrestricted vector, where …

We develop a new score-driven model for the joint dynamics of fat-tailed realized covariance
matrix observations and daily returns. The score dynamics for the unobserved true …

The construction of score-driven filters for nonlinear time series models is described, and
they are shown to apply over a wide range of disciplines. Their theoretical and practical …

A new framework for the joint estimation and forecasting of dynamic value at risk (VaR) and
expected shortfall (ES) is proposed by our incorporating intraday information into a …

We propose a novel class of multivariate GARCH models that utilize realized measures of
volatilities and correlations. The central component is an unconstrained vector …

The use of social–emotional learning (SEL) practices in online literature teaching has not yet
been sufficiently researched. This study addresses this lacuna by identifying SEL practices …

This chapter provides a survey of various multivariate GARCH specifications that model the
temporal dependence in the second moment of multivariate return series processes. The …

This paper introduces the scalar DCC-HEAVY and DECO-HEAVY models for conditional
variances and correlations of daily returns based on measures of realized variances and …

The global minimum variance portfolio (GMVP) is the starting point of the Markowitz mean‐
variance efficient frontier. The estimation of the GMVP weights is therefore of much …

This paper develops a new class of dynamic models for forecasting extreme financial risk.
This class of models is driven by the score of the conditional distribution with respect to both …