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 multivariate GARCH (MGARCH) model is popular for analyzing financial time series
data. However, statistical inferences for MGARCH models are quite challenging, owing to …

Abstract Based on a General Dynamic Factor Model with infinite-dimensional factor space
and MGARCH volatility models, we develop new estimation and forecasting procedures for …

Many financial decisions, such as portfolio allocation, risk management, option pricing and
hedge strategies, are based on forecasts of the conditional variances, covariances and …

This article aims to decompose a large dimensional vector autoregressive (VAR) model into
two components, the first one being generated by a small‐scale VAR and the second one …

General dynamic factor models have demonstrated their capacity to circumvent the curse of
dimensionality in the analysis of high-dimensional time series and have been successfully …

In this paper, we analyse the recent principal volatility components analysis procedure. The
procedure overcomes several difficulties in modelling and forecasting the conditional …

Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model and its
variations have been widely adopted in the study of financial volatilities, while the extension …

In order to meet the increasingly stringent global standards of banking management and
regulation, several methods have been proposed in the literature for forecasting tail risk …

Inference on the maximal rank of time-varying covariance matrices using high-frequency
data Page 1 The Annals of Statistics 2023, Vol. 51, No. 2, 791–815 https://doi.org/10.1214/23-AOS2273 …