The class of mixed normal conditional heteroskedastic (MixN-GARCH) models, which couples a mixed normal distributional structure with GARCH-type dynamics, has been shown to offer a plausible decomposition of the contributions to volatility, as well as excellent out-of-sample forecasting...

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 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...

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),...

A fast method is developed for value at risk and expected shortfall prediction for univariate asset return time series exhibiting leptokurtosis, asymmetry, and conditional heteroskedasticity. It is based on a GARCH-type process driven by noncentral t innovations. While the method involves use of...

We construct a momentum factor that identifies cross-sectional winners and losers based on a weighting scheme that incorporates all the price data, over the entire lookback period, as opposed to only the first and last price points of the window. The weighting scheme is derived from the...