and WTI in America are compared. OVX index is able to provide the optimal forecast for the volatility of Brent's future …

competing strategies such as forecast combinations and shrinkage methods. A mean-variance investor who targets a constant Sharpe …

effectively incorporate persistent homology (PH) into neural network models to increase their forecast accuracy in predicting …), NBEATSx-VP demonstrates a minimum 9% improvement in forecast precision compared to benchmark models. Considering Quasi …

This paper considers forecast averaging when the same model is used but estimation is carried out over different … estimation windows leads to a lower bias and to a lower root mean square forecast error for all but the smallest of breaks … estimation windows. It develops theoretical results for random walks when their drift and/or volatility are subject to one or …

In this paper, we apply machine learning to forecast the conditional variance of long-term stock returns measured in …

This paper considers forecast averaging when the same model is used but estimation is carried out over different … estimation windows leads to a lower bias and to a lower root mean square forecast error for all but the smallest of breaks … estimation windows. It develops theoretical results for random walks when their drift and/or volatility are subject to one or …

We forecast the realized and median realized volatility of agricultural commodities using variants of the Heterogeneous …

learning, dimensionality reduction, forecast combination and amalgamation approaches. Our results highlight the predictive …

returns for equity returns for all the countries across various forecast horizons and the length of out-of-sample periods …

care is required in order to achieve these forecast improvements. Rich multivariate models are needed to exploit the …, shrinkage and forecast combinations …