By Jensen's inequality, a model's forecasts of the variance and standard deviation of returns cannot both be unbiased. This paper explores the bias in GARCH type model forecasts of the standard deviation of returns, which we argue is the more appropriate volatility measure for most financial...

In this paper, we study the methods of combining different volatility forecasts using various GARCH models. Given that the major risk exposure for many investors in energy is the volatility of the electricity price, our motivation stems from the fact that there is no single best model for...

Rogers-Satchell (RS) measure is an efficient volatility measure. This paper proposes quantile RS (QRS) measure to ensure robustness and correct the downward bias of RS measure with an additive term. Moreover scaling factors are provided for different interquantile ranges to ensure unbiasedness....

In this paper, we use factor-augmented HAR-type models to predict the daily integrated volatility of asset returns. Our approach is based on a proposed two-step dimension reduction procedure designed to extract latent common volatility factors from a large dimensional and high-frequency returns...

This paper introduces a volatility model with a component structure allowing for a realized measure based on high-frequency data (e.g realized variance) to drive the short-run volatility dynamics. In a joint model of the daily return and the realized measure, the conditional variance of the...

Forecasting volatility models typically rely on either daily or high frequency (HF) data and the choice between these two categories is not obvious. In particular, the latter allows to treat volatility as observable but they suffer from many limitations. HF data feature microstructure problem,...

We examine the properties and forecast performance of multiplicative volatility specifications that belong to the class of GARCH-MIDAS models suggested in Engle et al. (2013). In those models volatility is decomposed into a short-term GARCH component and a long-term component that is driven by...

Accurate volatility forecasting is a key determinant for portfolio management, risk management and economic policy. The paper provides evidence that the sum of squared standardized forecast errors is a reliable measure for model evaluation when the predicted variable is the intra-day realized...

ARFIMAX models are applied in estimating the intra-day realized volatility of the CAC40 and DAX30 indices. Volatility clustering and asymmetry characterize the logarithmic realized volatility of both indices. ARFIMAX model with time-varying conditional heteroscedasticity is the best performing...

Volatility forecasting is crucial for portfolio management, risk management, and pricing of derivative securities. Still, little is known about the accuracy of volatility forecasts and the horizon of volatility predictability. This paper aims to fill these gaps in the literature. We begin this...