assets. Building on this idea, we propose the use of a highly flexible and tractable model to forecast the volatility of an …

The GARCH(1,1) model and its extensions have become a standard econometric tool for modeling volatility dynamics of … on the basis of their one-step ahead forecasting performance. With regard to forecast unbiasedness and precision …

The GARCH(1,1) model and its extensions have become a standard econometric tool for modeling volatility dynamics of … on the basis of their one-step ahead forecasting performance. With regard to forecast unbiasedness and precision …

-at-Risk (VaR) forecast that combines the forecasts of different VaR models. The robust forecast is based on the median of the point … VaR forecasts of a set of conditional volatility models. This risk management strategy is GFC-robust in the sense that …

We investigate the question of whether macroeconomic variables contain information about future stock volatility beyond … that contained in past volatility. We show that forecasts of GDP growth from the Federal Reserve's Survey of Professional … Forecasters predict volatility in a cross-section of 49 industry portfolios. The expectation of higher growth rates is associated …

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 …

volatility, based on estimated portfolio factor loadings and responsive estimates of factor volatility. These predictions are of … of Defined Contribution pension plans. Because return volatility is variable and mean-reverting, the square root rule for … extrapolating short-term volatility predictions to medium-horizon (one year to ten years) risk predictions systematically overstates …

strategies. The high volatility of cryptocurrencies turns them a really risky investment and consequently, appropriate risk … between cryptocurrencies, we propose a methodology based on vine copulas and robust volatility models. Our procedure is …

. (2010c). The robust forecast is based on the median of the point VaR forecasts of a set of conditional volatility models … selecting a Value-at-Risk (VaR) forecast that combines the forecasts of different VaR models, was proposed in McAleer et al … for the entire period. This paper presents evidence to support the claim that the median point forecast of VaR is …

conditional volatilities and correlations, the distribution for the innovations and the method of forecast construction. We find … smaller. The differences from the model, distribution and forecast choices are also smaller compared to temporal aggregation. …