asset management are predicated on the importance of jumps, or discontinuous movements in asset returns. In light of this, a … number of recent papers have addressed volatility predictability, some from the perspective of the usefulness of jumps in … forecasting volatility. Key papers in this area include Andersen, Bollerslev, Diebold and Labys (2003), Corsi (2004), Andersen …

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 … that the new method based on stochastic volatility models with the asymmetry and long memory outperforms the forecasting …

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 … that the new method based on stochastic volatility models with the asymmetry and long memory outperforms the forecasting …

Forecasting volatility models typically rely on either daily or high frequency (HF) data and the choice between these … these two family forecasting-volatility models, comparing their performance (in terms of Value at Risk, VaR) under the … assumptions of jumps in prices and leverage effects for volatility. Findings suggest that daily-data models are preferred to HF …

Forecasting-volatility models typically rely on either daily or high frequency (HF) data and the choice between these … forecasting-volatility models, comparing their performance (in terms of Value at Risk, VaR) under the assumptions of jumping …

We document the forecasting gains achieved by incorporating measures of signed, finite and infinite jumps in … forecasting the volatility of equity prices, using high-frequency data from 2000 to 2016. We consider the SPY and 20 stocks that … threshold bipower variation measures. Incorporating signed finite and infinite jumps generates significantly better real …

, forecasting of the full density for long horizons is feasible, which we pursue. We document variability in conditional variances … over time, which stresses the importance of careful modeling and forecasting of volatility. We show that improved model fit …

using these models in an out-of-sample forecasting exercise compared with the forecasts obtained based on the usual linear …

forecasting, the authors propose a new factor multivariate stochastic volatility (fMSV) model for realized covariance measures … outperform existing dynamic conditional correlation models for forecasting future covariances. Among the new fMSV models, the … Cholesky MSV model with long memory and asymmetry shows stable and better forecasting performance for one-day, five-day and ten …