Factor Forests (DFF) for macroeconomic forecasting, which synthesize the recent machine learning, dynamic factor model and … proposed in Zeileis, Hothorn and Hornik (2008). DFTs and DFFs are non-linear and state-dependent forecasting models, which … powerful tree-based machine learning ensembles conditional on the state of the business cycle. The out-of-sample forecasting …

financial index price forecasting. …

-to-estimate and explain, performs best for forecasting. Our conservative out-of-sample forecast evaluation, using data …

financial forecasting. This paper deals with the application of SVR in volatility forecasting. Based on a recurrent SVR, a GARCH … 30, 2005. The experiment shows that, under both varying and fixed forecasting schemes, the SVR-based GARCH outperforms … examined to the free parameters. Keywords: recurrent support vector regression ; GARCH model ; volatility forecasting …

forecasting both non-overlapping and overlapping excess bond returns. In contrast, some machine learning models can help find some … statistical evidence for forecasting overlapping excess bond returns. Second, when using both pure real-time macro information and … yield curve information, we find that deep learning performs well for forecasting medium- and long-maturity overlapping …

financial forecasting. This paper deals with the application of SVR in volatility forecasting. Based on a recurrent SVR, a GARCH … 30, 2005. The experiment shows that, under both varying and fixed forecasting schemes, the SVR-based GARCH outperforms …

The topic of this chapter is forecasting with nonlinear models. First, a number of well-known nonlinear models are … linear model. There exist relatively large studies in which the forecasting performance of nonlinear models is compared with …

with random effects, while the in-sample and out-sample forecasting performance is higher in random effects estimation than …