-switching models, and forecast combination to predict the dynamics in the S&P 500. First, we aggregate the weekly information of 115 …

We assess financial theory-based and machine learning-implied measurements of stock risk premia by comparing the … preferable to rely on a theory-based approach instead of engaging in the computerintensive hyper-parameter tuning of statistical … models. The theory-based approach also delivers a solid performance at the one year horizon, at which only one machine …

predictability, popular predictors from the literature fail to outperform the simple historical average benchmark forecast in out … model restrictions, forecast combination, diffusion indices, and regime shifts—improve forecasting performance by addressing …

In this paper, we estimate, model and forecast Realized Range Volatility, a new realized measure and estimator of the … distribution for the innovations. The analysis of the forecast performance during the different periods suggests that including the …

proposed forecast and a benchmark. Considering stock return forecasting as an example, we show that the resulting robust … monitoring forecast improves the average performance of the proposed forecast by 15% (in terms of mean-squared-error) and reduces …

forecast nonlinear ARMA model based simulated data and real data of financial returns. The forecasting ability of the recurrent …