In this paper, we estimate, model and forecast Realized Range Volatility, a new realized measure and estimator of the quadratic variation of financial prices. This estimator was early introduced in the literature and it is based on the high-low range observed at high frequency during the day. We...

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,...

An increase in the number of asset pricing models intensifies model uncertainties in assetpricing. While a pure "model selection" (singling out a best model) can result in a loss of usefulinformation, a full “model pooling” may increase the risk of including noisy information.We make a...

We propose a model that extends the RT-GARCH model by allowing conditional heteroskedasticity in the volatility process. We show we are able to filter and forecast both volatility and volatility of volatility simultaneously in this simple setting. The volatility forecast function follows a...

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,...

In asset pricing, most studies focus on finding new factors such as macroeconomic factors or firm characteristics to explain risk premium. Investigating whether these factors are useful in forecasting stock returns remains active research in the field of finance and computer science. This paper...

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 evaluate the performance of several linear and nonlinear machine learning models in forecasting the realized volatility (RV) of ten global stock market indices in the period from January 2000 to December 2021. We train models using a dataset which includes past values of the RV and additional...