We examine the effect of oil price uncertainty on sovereign credit risks in Gulf Cooperation Council (GCC) countries. Unlike past studies, we employ a structural vector autoregression with multivariate GARCH-in-mean (VAR-GARCH-in-mean) approach after filtering out outliers observed series. The findings show that market has positive impact Credit Default Swap (CDS) spreads GCC Furthermore, find ...

In accordance with Basel Capital Accords, the Capital Requirements (CR) for market risk exposure of banks is a nonlinear function of Value-at-Risk (VaR). Importantly, the CR is calculated based on a bank’s actual portfolio, i.e. the portfolio represented by its current holdings. To tackle mean-VaR portfolio optimization within the actual portfolio framework (APF), we propose a novel mean-VaR op...

We introduce a new family of processes that include the long memory (power law) in the volatility correlation. This is achieved by measuring the historical volatilities on a set of increasing time horizons and by computing the resulting effective volatility by a sum with power law weights. The processes have 2 parameters (linear processes) or 4 parameters (affine processes). In the limit where ...

Modelling the asymmetric effect between return and volatility has long been an important issue in finance as well as in econometrics. While much literature interpret this asymmetric effect as a natural result from leverage and modelled as a threshold GARCH process, it has now accepted that it is more likely to due to the feedback effect from expected volatility. Considering this explanation, in...

In this paper, we introduce supergaussian generalized autoregressive conditional heteroscedasticity (GARCH) models for speech signals in the short-time Fourier transform (STFT) domain. We address the problem of speech enhancement, and show that estimating the variances of the STFT expansion coefficients based on GARCH models yields higher speech quality than by using the decision-directed metho...

This paper investigates the forecasting ability of four different GARCH models and the Kalman filter method. The four GARCH models applied are the bivariate GARCH, BEKK GARCH, GARCH-GJR and the GARCH-X model. The paper also compares the forecasting ability of the non-GARCH model the Kalman method. Forecast errors based on twenty UK company weekly stock return (based on timevary beta) forecasts ...