predictors of interest), selective search within the range of possible models, control of collinearity, out-of-sample forecasting … the usual linear regression model, and over some models suggested by PcGets. Thus, both methods are useful components of …

estimator for forecasting time series, with a special attention to GARCH and ACD models. The local large sample properties of …Nonlinear time series models can exhibit components such as long range trends and seasonalities that may be modeled in … suboptimal for forecasting purposes. The paper proposes the use of a class of shrinkage estimators that includes the Ridge …

scale factors and of the correlations of the innovation processes. We illustrate the feasibility of the procedure and the …

neural network models, the concavity of the likelihood in the weights of the usual linear models, and the ability to identify … candidate regressors by a saliency feature, chooses the models in subsample 1, uses subsample 2 for parameter estimation, and …

This paper assesses the performance of volatility forecasting using focused selection and combination strategies to … include relevant explanatory variables in the forecasting model. The focused selection/combination strategies consist of … BIC. The methodology is applied to a daily recursive 1--step ahead value--at--risk (VaR) forecasting exercise of 4 widely …

ETFs shows that the proposed methodology is able to significantly outperform common forecasting methods and delivers …