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 of many limitations. HF data feature microstructure problem,...

We introduce and investigate some properties of a class of nonlinear time series models based on the moving sample quantiles in the autoregressive data generating process. We derive a test fit to detect this type of nonlinearity. Using the daily realized volatility data of Standard & Poor's 500...

This paper examines the impact of intraday periodicity on forecasting realized volatility using a heterogeneous autoregressive model (HAR) framework. We show that periodicity inflates the variance of the realized volatility and biases jump estimators. This combined effect adversely affects...

In this study, we model realized volatility constructed from intraday high-frequency data. We explore the possibility of confusing long memory and structural breaks in the realized volatility of the following spot exchange rates: EUR/USD, EUR/JPY, EUR/CHF, EUR/GBP, and EUR/AUD. The results show...

Volatility has been used as an indirect means for predicting risk accompanied with an asset. Volatility explains the variations in returns. Forecasting volatility has been a stimulating problem in the financial systems. This study examined the different volatility estimators and determined the...

Volatility had been used as an indirect means for predicting risk accompanied with the asset. Volatility explains the variations in returns. Forecasting volatility had been a stimulating problem in the financial systems. The study examined the different volatility estimators and determined the...

This paper proposes novel approaches to the modeling of attenuation bias effects in volatility forecasting. Our strategy relies on suitable generalizations of the Realized GARCH model by Hansen et al. (2012) where the impact of lagged realized measures on the current conditional variance is...