Given observations on a stationary economie vector time series process we show that the best % periods ahead forecast (best in the sense of having minimal forecast error variance) of one of the variables can be consistently estimated by nonparametric regression on an ARMA memory index. Our approach is based on a combination of the ARMA memory index modeling approach of Bierens (1986a) with a mo...

Gold price forecast is of great importance. Many models were presented by researchers to forecast gold price. It seems that although different models could forecast gold price under different conditions, the new factors affecting gold price forecast have a significant importance and effect on the increase of forecast accuracy. In this paper, different factors were studied in comparison to the p...

This paper demonstrates that a significant link exists between the real economy and financial conditions proxied by technical indicators. We show that technical indicators distill the high frequency information of asset prices into useful signals of future economic activity. In-sample and out-of-sample results reveal that technical indicators forecast coincident indicators as well as the Confer...

Abstract Machine learning is routinely used to forecast solar radiation from inputs, which are forecasts of meteorological variables provided by numerical weather prediction (NWP) models, on a spatially distributed grid. However, the number features resulting these grids usually large, especially if several vertical levels included. Principal Components Analysis (PCA) one simplest and most wide...

A novel forecast combination and weighted quantile-based tail risk forecasting framework is proposed, aiming to reduce the impact of modeling uncertainty. The proposed approach based on a two-step estimation procedure. first step involves value-at-risk (VaR) forecasts at grid quantile levels. range parametric semiparametric models selected as model universe in weights are estimated by optimizin...