memory in stochastic volatility (SV) components in order to develop the General Long Memory SV (GLMSV) model. We examine the …

-frequency returns. Our measure quantifies volatility reduction, which could be achieved by including an additional asset in the …

This paper investigates the volatility of daily returns on the Romanian stock market between January 2020 and April … 2021. Volatility is analyzed by means of the representative index for Bucharest Stock Exchange (BSE), namely, the Bucharest … GARCH approach. In the survey, the GARCH model (1,1) was applied to explore the volatility of the BET and BSE traded shares …

asset price volatility. We show how the squared low-frequency returns can be expressed in terms of the temporal aggregation … properties of the spectral density function of realized volatility series, constructed from squared returns with different … new features of volatility in financial market indices. The theoretical findings are illustrated via the analysis of both …

We build a discrete-time non-linear model for volatility forecasting purposes. This model belongs to the class of … volatility regimes and using more accurate volatility measures allow outperforming other benchmark models, such as linear …

This paper investigates the benefits of jointly using several realized measures in predicting daily price volatility … significantly increases the accuracy of volatility forecasts, while in forecasting Value-at-Risk and Expected Shortfall at different …

The inhomogeneity of the cross-sectional distribution of realized assets’ volatility is explored and used to build a …-sectional distribution of realized volatility is captured by a finite Gaussian mixture model plus a uniform component that represents … abnormal variations in volatility. Based on the cross-sectional mixture model, at each time point, memberships of assets to …