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
markdownabstract__Abstract__ Modelling covariance structures is known to suffer from the curse of...
The Multiplicative MIDAS Realized DCC (MMReDCC) model simultaneously accounts for short and long ter...
This paper proposes a methodology for modelling time series of realized covariance matrices in order...
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 ...
We propose a dynamic factor state-space model for the prediction of high-dimensional realized covari...
Modelling and forecasting high dimensional covariance matrices is a key challenge in data-richenviro...
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...
Building models for high dimensional portfolios is important in risk management and asset allocation...
markdownabstract__Abstract__ Modelling covariance structures is known to suffer from the curse of...
The Multiplicative MIDAS Realized DCC (MMReDCC) model simultaneously accounts for short and long ter...
This paper proposes a methodology for modelling time series of realized covariance matrices in order...
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 ...
We propose a dynamic factor state-space model for the prediction of high-dimensional realized covari...
Modelling and forecasting high dimensional covariance matrices is a key challenge in data-richenviro...
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...
Building models for high dimensional portfolios is important in risk management and asset allocation...
markdownabstract__Abstract__ Modelling covariance structures is known to suffer from the curse of...
The Multiplicative MIDAS Realized DCC (MMReDCC) model simultaneously accounts for short and long ter...
This paper proposes a methodology for modelling time series of realized covariance matrices in order...