more than a single regime, have performed substantially better than standard methods in terms of volatility and Value … individual models, we evaluate the use of forecast combinations strategies. In our empirical application, procedures that are …

Several procedures to forecast daily risk measures in cryptocurrency markets have been recently implemented in the … models, we evaluate the use of several forecast combining strategies. Our results, based on a comprehensive backtesting …

. (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 …

volatility for estimating conditional variances and covariances; (2) alternative currencies; and (3) alternative maturities of … Chang et al. [17], we estimate four multivariate volatility models (namely CCC, VARMA-AGARCH, DCC and BEKK), and calculate …

predictability, popular predictors from the literature fail to outperform the simple historical average benchmark forecast in out … model restrictions, forecast combination, diffusion indices, and regime shifts—improve forecasting performance by addressing …

returns and volatility. We extend the Realized LGARCH model by allowing for a timevarying intercept, which responds to changes … estimation and forecast procedure. Using more than 10 years of high-frequency transactions for 55 U.S. stocks, we argue that the … combination of low-frequency exogenous economic indicators with high-frequency financial data improves our ability to forecast the …

New models to forecast the real price of oil on the basis of macroeconomic indicators and Google search data are …

For forecasting volatility of futures returns, the paper proposes an indirect method based on the relationship between … futures and the underlying asset for the returns and time-varying volatility. For volatility forecasting, the paper considers … the stochastic volatility model with asymmetry and long memory, using high frequency data for the underlying asset …