Add to ORKGThe performance of techniques for evaluating multivariate volatility forecasts are not yet as well understood as their univariate counterparts. This paper aims to evaluate the efficacy of a range of traditional statistical-based methods for multivariate forecast evaluation together with methods based on underlying considerations of economic theory. It is found that a statistical-based method based on likelihood theory and an economic loss function based on portfolio variance are the most effective means of identifying optimal forecasts of conditional covariance matrices
In multivariate volatility prediction, identifying the optimal forecasting model is not always a fea...
Recent research has suggested that forecast evaluation on the basis of standard statistical loss fun...
Volatility plays an important role in controlling and forecasting risks in various �nancial operatio...
The performance of techniques for evaluating univariate volatility forecasts are well understood. In...
Multivariate volatility forecasts are an important input in many financial applications, in particul...
Volatility has been one of the most active and successful areas of research in time series econometr...
We investigate the economic value of multivariate volatility forecasting ability using a testing fra...
The consistent ranking of multivariate volatility models by means of statistical loss function is a ...
A large number of parameterizations have been proposed to model conditional variance dynamics in a m...
Forecasting conditional covariance matrices of returns involves a variety of modeling options. First...
This paper considers the problem of evaluation and comparison of univariate and multivariate volatil...
The paper investigates the effect of model uncertainty on multivariate volatility prediction. Our a...
In multivariate volatility prediction, identifying the optimal forecasting model is not always a fea...
Recent research has suggested that forecast evaluation on the basis of standard statistical loss fun...
Volatility plays an important role in controlling and forecasting risks in various �nancial operatio...
The performance of techniques for evaluating univariate volatility forecasts are well understood. In...
Multivariate volatility forecasts are an important input in many financial applications, in particul...
Volatility has been one of the most active and successful areas of research in time series econometr...
We investigate the economic value of multivariate volatility forecasting ability using a testing fra...
The consistent ranking of multivariate volatility models by means of statistical loss function is a ...
A large number of parameterizations have been proposed to model conditional variance dynamics in a m...
Forecasting conditional covariance matrices of returns involves a variety of modeling options. First...
This paper considers the problem of evaluation and comparison of univariate and multivariate volatil...
The paper investigates the effect of model uncertainty on multivariate volatility prediction. Our a...
In multivariate volatility prediction, identifying the optimal forecasting model is not always a fea...
Recent research has suggested that forecast evaluation on the basis of standard statistical loss fun...
Volatility plays an important role in controlling and forecasting risks in various �nancial operatio...