Add to ORKGBuilding models for high dimensional portfolios is important in risk management and asset allocation. Here we propose a novel way of estimating models of time-varying covariances that overcome some of the computational problems which have troubled existing methods when applied to 1,000s of assets. The theory of this new strategy is developed in some detail, allowing formal hypothesis testing to be carried out on these models. Simulations are used to explore the performance of this inference strategy while empirical examples are reported which show the strength of this method
Modelling and forecasting the covariance of financial return series has always been a challenge due ...
A new covariance matrix estimator is proposed under the assumption that at every time period all pai...
The accurate prediction of time-changing covariances is an important problem in the modeling of mult...
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...
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...
Economic modeling in a data-rich environment is often challenging. To allow for enough flexibility a...
This paper develops time series methods for forecasting correlations in high dimensional problems. T...
This paper injects factor structure into the estimation of time-varying, large-dimensional covarianc...
Modelling and forecasting high dimensional covariance matrices is a key challenge in data-richenviro...
ABSTRACT. This paper discusses a new dynamic model for dealing with large dimensional realized covar...
Many parameterizations have been introduced to model covariance dynamics. Yet estimat-ing even moder...
Dynamic covariance estimation for multivariate time series suffers from the curse of dimensionality....
Modelling and forecasting the covariance of financial return series has always been a challenge due ...
A new covariance matrix estimator is proposed under the assumption that at every time period all pai...
The accurate prediction of time-changing covariances is an important problem in the modeling of mult...
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...
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...
Economic modeling in a data-rich environment is often challenging. To allow for enough flexibility a...
This paper develops time series methods for forecasting correlations in high dimensional problems. T...
This paper injects factor structure into the estimation of time-varying, large-dimensional covarianc...
Modelling and forecasting high dimensional covariance matrices is a key challenge in data-richenviro...
ABSTRACT. This paper discusses a new dynamic model for dealing with large dimensional realized covar...
Many parameterizations have been introduced to model covariance dynamics. Yet estimat-ing even moder...
Dynamic covariance estimation for multivariate time series suffers from the curse of dimensionality....
Modelling and forecasting the covariance of financial return series has always been a challenge due ...
A new covariance matrix estimator is proposed under the assumption that at every time period all pai...
The accurate prediction of time-changing covariances is an important problem in the modeling of mult...