VARs. To address these issues, we propose VAR models with outlier-augmented stochastic volatility (SV) that combine … transitory and persistent changes in volatility. The resulting density forecasts are much less sensitive to outliers in the data … the pandemic period, as well as for earlier subsamples of relatively high volatility. In historical forecasting, outlier …

This paper evaluates the profitability of applying four different volatility forecasting models to the trading of … applied in this paper are: historical volatility, two ARCH models, and an autoregressive model for the volatility index. VDAX …. The ARCH models perform best in generating profits for market makers. Forecasts based on historical volatility also …

volatility. For forecasting, the choice among outlier-robust error structures is less important, however, when a large cross …

an accurate calibration of forecast confidence intervals, and is better suited at long horizons and in high-volatility …