The estimation of multivariate GARCH models remains a challenging task, even in modern computer environments. This manuscript shows how Independent Component Analysis can be used to estimate the Generalized Orthogonal GARCH model in a fraction of the time otherwise required. The proposed method...

A new model class for univariate asset returns is proposed which involves the use of mixtures of stable Paretian distributions, and readily lends itself to use in a multivariate context for portfolio selection. The model nests numerous ones currently in use, and is shown to outperform all its...

The use of mixture distributions for modeling asset returns has a long history in finance. New methods of demonstrating support for the presence of mixtures in the multivariate case are provided. The use of a two-component multivariate normal mixture distribution, coupled with shrinkage via a...

In light of the growing use, acceptance of, and demand for, machine learning in many fields, notably data science, but also other fields such as finance -- and this in both industry and academics, some university departments might wish, or find themselves forced to, accord to the winds of change...

The paper proposes a framework for large-scale portfolio optimization which accounts for all the major stylized facts of multivariate financial returns, including volatility clustering, dynamics in the dependency structure, asymmetry, heavy tails, and nonellipticity. It introduces a so-called...

A non-Gaussian multivariate regime switching dynamic correlation model for fi nancial asset returns is proposed. It incorporates the multivariate generalized hyperbolic law for the conditional distribution of returns. All model parameters are estimated consistently using a new two-stage...

Covariance matrix forecasts for portfolio optimization have to balance sensitivity to new data points with stability in order to avoid excessive rebalancing. To achieve this, a new robust orthogonal GARCH model for a multivariate set of non-Gaussian asset returns is proposed. The conditional...

It is well-known in empirical nance that virtually all asset returns, whether monthly, daily, or intraday, are heavy-tailed and, particularly for stock returns, are mildly but often signi cantly negatively skewed. However, the tail indices, or maximally existing moments of the returns, can di er...

The use of GARCH models is widely used as an effective method for capturing the volatility clustering inherent in financial returns series. The residuals from such models are however often non-Gaussian, and two methods suggest themselves for dealing with this; outlier removal, or use of...

A new multivariate time series model with various attractive properties is motivated and studied. By extending the CCC model in several ways, it allows for all the primary stylized facts of financial asset returns, including volatility clustering, non-normality (excess kurtosis and asymmetry),...