. Applying our model to high-frequency transaction data, we detect two distinct regimes in the intraday volatility process: a … dominant volatility regime that is observable throughout the trading day representing the risk-transferring trading activity of … investors, and a minor volatility regime that concentrates around market liquidity shocks which mainly capture impacts of firm …

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 …

This paper introduces novel volatility diffusion models to account for the stylized facts of high-frequency financial … data such as volatility clustering, intra-day U-shape, and leverage effect. For example, the daily integrated volatility of … the proposed volatility process has a realized GARCH structure with an asymmetric effect on log-returns. To further …

leverage effect and maintains a mathematical structure that facilitates volatility estimation. A class of bivariate models that … volatility information improves the day volatility estimation. The results indicate a forecasting improvement using bivariate …We propose a methodology to include night volatility estimates in the day volatility modeling problem with high …

the variance matrix. Monte Carlo evidence for parameter estimation based on different small sample sizes is provided. We …

the behaviour of returns and their volatility during both the calm as well as various crises/turmoil periods. Besides the …-GJR-GARCH) were estimated in order to examine the volatility switches of the Central European transition stock markets. The t …-distribution of error terms was used to capture the dynamics of analysed returns more precisely. The results proved high volatility …

This paper examines the volatility of banks equity weekly returns for six banks (coded B1 to B6) using GARCH models … in Student’s t-distribution are adjudged the best volatility models for B2 and B3 respectively. The study recommends that … in modelling stock market volatility, variants of GARCH models and alternative error distribution should be considered …

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 …

In the class of univariate conditional volatility models, the three most popular are the generalized autoregressive … mathematical regularity properties, including invertibility, to determine the likelihood function for estimation, and the … effects on conditional volatility of positive and negative effects of equal magnitude, and possibly also leverage, which is …