The scientific problem of the dissertation is search of adequacy of the trading portfolio risk management methods and models to the current economic, technological, and informational circumstances of financial institutions. The main features of science novelty characteristic to this research are...

In this paper we study volatility functions. Our main assumption is that the volatility is deterministic or stochastic but driven by a Brownian motion independent of the stock. We propose a forecasting method and check the consistency with option pricing theory. To estimate the unknown...

We propose a new conditionally heteroskedastic factor model, the GICA-GARCH model, which combines independent component analysis (ICA) and multivariate GARCH (MGARCH) models. This model assumes that the data are generated by a set of underlying independent components (ICs) that capture the...

In this paper, a forecast of conditional volatility in Saudi, Kuwait and Abu-Dhabi markets is performed. To capture the skewness and excess kurtosis that characterise asset returns in Gulf Cooperation Council markets, the conditional volatility of asset returns was estimated using skewed...

In this paper, a forecast of conditional volatility in Saudi, Kuwait and Abu-Dhabi markets is performed. To capture the skewness and excess kurtosis that characterise asset returns in Gulf Cooperation Council markets, the conditional volatility of asset returns was estimated using skewed...

In this paper the issue of detecting and handling outliers in the GARCH(1,1) model is addressed. Simulation evidence shows that neglecting even a single outlier has a dramatic on parameter estimates. To detect and correct for outliers, we propose an adaptation of the iterative in Chen and Liu...

In this paper the issue of detecting and handling outliers in the GARCH(1,1) model is addressed. Simulation evidence shows that neglecting even a single outlier has a dramatic on parameter estimates. To detect and correct for outliers, we propose an adaptation of the iterative in Chen and Liu...