New approaches to forecasting economic and financial time series

Project status in progress
Project time since 01.01.2020
Keywords Econometrics; time series analysis; forecasting; forecast combinations; forecast- error modeling; multifractal structures

We establish and analyze two new approaches to forecasting economic and financial time series. (i) In many real-world situations, the forecaster has available multiple (say M) individual forecasts of the same target variable. We establish a procedure for (potentially) reducing the forecast errors of the M individual forecast models, by interrelating the M forecast-error processes within a vector autoregressive framework. (ii) Many financial time series (like electricity prices) can be shown to include multifractal structures. We model the data-generating process (DGP) of the variable by incorporating (theoretical) multifractal elements, and analyze the forecasting capacities of these new DGPs.