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...

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...

The classical realized variance (RV) estimator is biased due to microstructure effects and asset price jumps. Robust realized variance (RRV) estimators adjust for these biases, and make more efficient of use of the intraday data. This article examines the benefits of using RRV estimators instead...

We explore intraday transaction records from NASDAQ OMX Commodities Europe from January 2006 to October 2013. We analyze empirical results for a selection of existing realized measures of volatility and incorporate them in a Realized GARCH framework for the joint modeling of returns and realized...

The classical realized variance (RV) estimator is biased due to microstructure effects and asset price jumps. Robust realized variance (RRV) estimators adjust for these biases, and make more efficient of use of the intraday data. This article examines the benefits of using RRV estimators instead...

 We propose a new class of multivariate volatility models utilizing realized measures of asset volatility and covolatility extracted from high-frequency data. Dimension reduction for estimation of large covariance matrices is achieved by imposing a factor structure with time-varying conditional...