We study the forecasting of future realized volatility in the foreign exchange, stock, and bond markets from variables … an additional forecasting variable. A vector HAR (VecHAR) model for the resulting simultaneous system is introduced … exchange and stock markets. Out-of-sample forecasting experiments confirm that implied volatility is important in forecasting …

HAR components in the model improve the point forecasting accuracy while the introduction of asymmetric effects only leads …

proposed. A large-scale out-of-sample forecasting analysis comparing the different models is performed. It is found that models …

. The out-of-sample forecasting performance of the proposed model is evaluated against a number of standard models, using …

We evaluate the performance of several linear and nonlinear machine learning models in forecasting the realized …

Volatility forecasting is crucial for portfolio management, risk management, and pricing of derivative securities …

We survey the literature on stock return forecasting, highlighting the challenges faced by forecasters as well as …-of-sample tests. Recent studies, however, provide improved forecasting strategies that deliver statistically and economically … model restrictions, forecast combination, diffusion indices, and regime shifts—improve forecasting performance by addressing …

quantile regression. I apply different approaches to forecasting growthat-risk, including quantile regression, quantile random …