. Third, the choice of the volatility forecasting mode affects the simulation results significantly. Fourth, we find a … significant impact of correlation versus no correlation but do not find a strong impact of sophisticated correlation forecasting …

forecasting techniques, e.g. correlation forecasts based on historical values and on a dynamic conditional correlation (DCC) model … varied. We find that the applied volatility forecasting models have a strong influence on the expected net present value … distribution and on the probability of default. In contrast, correlation forecasting models play a minor role. Time resolution and …

developed in MATLAB. We examine five major stock market index returns for a testing forecasting period of 10 days ahead. We …

This paper puts forward kernel ridge regression as an approach for forecasting with many predictors that are related … overfitting. We extend the kernel ridge regression methodology to enable its use for economic time-series forecasting, by …

This paper puts forward kernel ridge regression as an approach for forecasting with many predictors that are related … overfitting. We extend the kernel ridge regression methodology to enable its use for economic time-series forecasting, by …

filtering of time-varying volatility, and volatility forecasting. Specifically, we make use of the indirect inference method to …

returns and intradaily squared returns for forecasting horizons rangingfrom 1 to 10 days. For the daily squared returns we …