Forecasting value-at-risk and expected shortfall in large portfolios: A general dynamic factor model approach
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 …
Expected Shortfall (ES) play an important role in risk management, portfolio allocation …
Forecasting conditional covariance matrices in high-dimensional time series: a general dynamic factor approach
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 …
and MGARCH volatility models, we develop new estimation and forecasting procedures for …
Covariance prediction in large portfolio allocation
Many financial decisions, such as portfolio allocation, risk management, option pricing and
hedge strategies, are based on forecasts of the conditional variances, covariances and …
hedge strategies, are based on forecasts of the conditional variances, covariances and …
Dimension Reduction for High‐Dimensional Vector Autoregressive Models
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 …
two components, the first one being generated by a small‐scale VAR and the second one …
Robustness and the general dynamic factor model with infinite-dimensional space: identification, estimation, and forecasting
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 …
dimensionality in the analysis of high-dimensional time series and have been successfully …
On the robustness of the principal volatility components
In this paper, we analyse the recent principal volatility components analysis procedure. The
procedure overcomes several difficulties in modelling and forecasting the conditional …
procedure overcomes several difficulties in modelling and forecasting the conditional …
Threshold network GARCH model
Y Pan, J Pan - Journal of Time Series Analysis, 2024 - Wiley Online Library
Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model and its
variations have been widely adopted in the study of financial volatilities, while the extension …
variations have been widely adopted in the study of financial volatilities, while the extension …
Adaptive combinations of tail-risk forecasts
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 …
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
M Reiss, L Winkelmann - The Annals of Statistics, 2023 - projecteuclid.org
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 …
data Page 1 The Annals of Statistics 2023, Vol. 51, No. 2, 791–815 https://doi.org/10.1214/23-AOS2273 …