Aim of this paper is to investigate the effect of model uncertainty on multivariate volatility prediction. This effect is expected to be particularly relevant in applications to vast dimensional datasets since it is well known that, in this case, the need for tractable model structures requires the imposition of severe and often untested constraints on the volatility dynamics. By means of an application to the optimization of a vast dimensional portfolio of stock returns, the paper compares the performances of different models and combination procedures. The main finding is that results are highly sensitive not only to the choice of the model but also to the specific combination procedure being used.
Comparison of different procedures for combining high-dimensional multivariate volatility forecasts
AMENDOLA, Alessandra;STORTI, Giuseppe
2012-01-01
Abstract
Aim of this paper is to investigate the effect of model uncertainty on multivariate volatility prediction. This effect is expected to be particularly relevant in applications to vast dimensional datasets since it is well known that, in this case, the need for tractable model structures requires the imposition of severe and often untested constraints on the volatility dynamics. By means of an application to the optimization of a vast dimensional portfolio of stock returns, the paper compares the performances of different models and combination procedures. The main finding is that results are highly sensitive not only to the choice of the model but also to the specific combination procedure being used.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
