An accessible guide to the multivariate time series tools used in numerous real-world
applications Multivariate Time Series Analysis: With R and Financial Applications is the …

We propose a new class of generalized thresholding operators that combine thresholding
with shrinkage, and study generalized thresholding of the sample covariance matrix in high …

We propose a new class of observation-driven time-varying parameter models for dynamic
volatilities and correlations to handle time series from heavy-tailed distributions. The model …

This paper addresses the question of the selection of multivariate generalized
autoregressive conditional heteroskedastic (GARCH) models in terms of variance matrix …

We introduce dynamic orthogonal components (DOC) for multivariate time series and
propose a procedure for estimating and testing the existence of DOCs for a given time …

Beyond their importance from the regulatory policy point of view, Value-at-Risk (VaR) and
Expected Shortfall (ES) play an important role in risk management, portfolio allocation …

The statistical properties of realized volatility estimators critically depend on the sampling
frequency of the underlying intra-day returns and on the chosen estimation formula. This …

This paper introduces a new class of multivariate volatility models which is easy to estimate
using covariance targeting, even with rich dynamics. We call them rotated ARCH (RARCH) …

Consistently determining the number of factors plays an important role in factor modelling for
volatility of multivariate time series. In this paper, the modelling is extended to handle the …

We extend the principal component analysis (PCA) to second-order stationary vector time
series in the sense that we seek for a contemporaneous linear transformation for ap-variate …