coverage or tone to provide the largest forecasting performance improvements in the prediction of the conditional variance of … weekly FTSE 100 stock returns. We find that versions of standard symmetric and asymmetric Generalized Autoregressive …

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 …

We compare more than 1000 different volatility models in terms of their fit to the historical ISE-100 Index data and … for modeling the ISE-100 return volatility. The t-distribution seems to characterize the distribution of the heavy tailed … returns better than the Gaussian distribution or the generalized error distribution. In terms of the forecasting performance …

volatility of the Standard and Poors 500 index among recent extensions of the heterogeneous autoregressive model. While we find …, improvements achieved by the inclusion of implied volatility turn out to be insignificant. …

accurately. Taking into consideration the main characteristics of the conditional volatility of asset returns, I estimate an …Predicting the one-step-ahead volatility is of great importance in measuring and managing investment risk more … excess kurtosis that the asset returns exhibit and ii) the fractional integration of the conditional variance. The model …

application to in- and out-of-sample one-step-ahead density forecasts of daily returns on the S&P 500, DAX and ATX stock market …

application to in- and out-of-sample one-step-ahead density forecasts of daily returns on the S&P 500, DAX and ATX stock market …

of volatility in finance for portfolio allocation, derivative pricing and risk management. The method has a two …This paper develops a method to improve the estimation of jump variation using high frequency data with the existence … of market microstructure noises. Accurate estimation of jump variation is in high demand, as it is an important component …

regularizing appropriate groups of coefficients. The second pass delivers risk premia estimates to predict equity excess returns …. Moreover, our results demonstrate that the proposed method reduces the prediction errors compared to a penalized approach …

This paper introduces a unified multivariate overnight GARCH-Ito model for volatility matrix estimation and prediction … the proposed estimation and prediction methods.The empirical analysis is carried out to compare the performance of the … proposed model has two different instantaneous volatility processes for the open-to-close and close-to-open periods, while each …