such as the realized volatility and squared overnight returns, are confronted with those from ARFIMA realized volatility … intraday stock returns. Value-at-Risk (VaR) predictions obtained from daily GARCH models extended with additional information … individual VaR rejections and a block-bootstrap unconditional coverage test that is robust to estimation uncertainty and model …

to forecast the volatility of the Moroccan stock-market index MADEX. We use daily returns covering the period between 01 …Nowadays, modeling and forecasting the volatility of stock markets have become central to the practice of risk …, as well as leading to a better understanding of the Moroccan stock-exchange volatility dynamics, especially with the lack …

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 dataset with 267 constituents of the S&P 500 index. In the first step …

model in several ways, it allows for all the primary stylized facts of financial asset returns, including volatility … volatility, but without the estimation problems associated with the latter, and being applicable in the multivariate setting for … EM-algorithm is developed for estimation. Each element of the vector return at time t is endowed with a common univariate …

conjunction with a variety of volatility models for returns on the Standard & Poor's 100 stock index. We consider two so …-calIed realised volatility models in which the cumulative squared intraday returns are modelled directly. We adopt an unobserved …-formation inherent in the high frequency returns. In the absence of the intraday volatility information, we find that the SV model …

The sum of squared intraday returns provides an unbiased and almost error-free measure of ex-post volatility. In this … paper we develop a nonlinear Autoregressive Fractionally Integrated Moving Average (ARFIMA) model for realized volatility …, which accommodates level shifts, day-of-the-week effects, leverage effects and volatility level effects. Applying the model …

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 … forecasting-volatility models, comparing their performance (in terms of Value at Risk, VaR) under the assumptions of jumping …

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 … these two family forecasting-volatility models, comparing their performance (in terms of Value at Risk, VaR) under the …

In this paper, we apply machine learning to forecast the conditional variance of long-term stock returns measured in … of covariates as well as the smoothing parameters via cross-validation. We find that volatility forecastability is much … squared return prediction errors gives an adequate approximation of the unobserved realised conditional variance for both the …

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 second-order difference equation as opposed to first-order under GARCH(1 …