best. Also, the volatility forecasts generated from multivariate time series models can be successfully converted into … higher portfolio returns using quantitative investment approaches if the right balance of volatility modelling and portfolio …

topological stock market changes as well as the incorporation of these topological changes into forecasting realized volatility … (RV) models to improve their forecast performance during turbulent periods. The results of the empirical experimentation … indicate that the employment of PH information allows nonlinear and neural network models to better forecast RV during a …

price volatility. To address this issue, we find a phenomenon, "momentum of jumps" (MoJ), that the predictive ability of the … jump component is persistent when forecasting the oil futures market volatility. Specifically, we propose a strategy that … according to their recent past forecasting performance. The volatility data are based on the intraday prices of West Texas …

. We build a comprehensive set of models and compare forecast performances across different selling levels and alcohol …

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 …

rolling windows and report considerable forecast error reductions. The adaptive averaging autoregressive model forecast ADA …-AR and the adaptive learning forecast, ADL, produced the smallest root-mean-square errors and lowest mean absolute errors …