Add to ORKGBased on a General Dynamic Factor Model with infinite-dimensional factor space, we develop a new estimation and forecasting procedures for conditional covariance matrices in high-dimensional time series. The performance of our approach is evaluated via Monte Carlo experiments, outperforming many alternative methods. The new procedure is used to construct minimum variance portfolios for a high-dimensional panel of assets. The results are shown to achieve better out-of-sample portfolio performance than alternative existing procedures.info:eu-repo/semantics/publishe
The Multiplicative MIDAS Realized DCC (MMReDCC) model simultaneously accounts for short and long ter...
markdownabstract__Abstract__ Modelling covariance structures is known to suffer from the curse of...
Building models for high dimensional portfolios is important in risk management and asset allocation...
Based on a General Dynamic Factor Model with infinite-dimensional factor space, we develop a new est...
<p>Estimation of high-dimensional covariance matrices is an interesting and important research topic...
Modelling and forecasting the covariance of financial return series has always been a challenge due ...
Modelling and forecasting high dimensional covariance matrices is a key challenge in data-richenviro...
We propose a dynamic factor state-space model for the prediction of high-dimensional realized covari...
This thesis considers two problems related to high-dimensional covariance matrices, namely, covarian...
Abstract. The identication of the optimal forecasting model for multivariate volatility prediction ...
In multivariate volatility prediction, identifying the optimal forecasting model is not always a fea...
In this article, we introduce a new method of forecasting large-dimensional covariance matrices by e...
This paper injects factor structure into the estimation of time-varying, large-dimensional covarianc...
Building models for high dimensional portfolios is important in risk management and asset allocation...
This thesis focuses on variance-covariance matrix modeling and forecasting. Majority of existing res...
The Multiplicative MIDAS Realized DCC (MMReDCC) model simultaneously accounts for short and long ter...
markdownabstract__Abstract__ Modelling covariance structures is known to suffer from the curse of...
Building models for high dimensional portfolios is important in risk management and asset allocation...
Based on a General Dynamic Factor Model with infinite-dimensional factor space, we develop a new est...
<p>Estimation of high-dimensional covariance matrices is an interesting and important research topic...
Modelling and forecasting the covariance of financial return series has always been a challenge due ...
Modelling and forecasting high dimensional covariance matrices is a key challenge in data-richenviro...
We propose a dynamic factor state-space model for the prediction of high-dimensional realized covari...
This thesis considers two problems related to high-dimensional covariance matrices, namely, covarian...
Abstract. The identication of the optimal forecasting model for multivariate volatility prediction ...
In multivariate volatility prediction, identifying the optimal forecasting model is not always a fea...
In this article, we introduce a new method of forecasting large-dimensional covariance matrices by e...
This paper injects factor structure into the estimation of time-varying, large-dimensional covarianc...
Building models for high dimensional portfolios is important in risk management and asset allocation...
This thesis focuses on variance-covariance matrix modeling and forecasting. Majority of existing res...
The Multiplicative MIDAS Realized DCC (MMReDCC) model simultaneously accounts for short and long ter...
markdownabstract__Abstract__ Modelling covariance structures is known to suffer from the curse of...
Building models for high dimensional portfolios is important in risk management and asset allocation...