Loukia Meligkotsidou

TL;DR: In order to enlarge the applicability of the model, inference for a multivariate Poisson model with larger structure is proposed, i.e. different covariance for each pair of variables, and extension to models with complete structure with many multi-way covariance terms is discussed.

TL;DR: In this article, the authors examined finite mixtures of multivariate Poisson distributions as an alternative class of models for multivariate count data, allowing for both overdispersion in the marginal distributions and negative correlation, while they are computationally tractable using standard ideas from finite mixture modelling.

TL;DR: In this article, the authors introduce the idea of modeling the conditional quantiles of hedge fund returns using a set of risk factors and explore potential economic impacts of their approach by analysing hedge fund strategies return series and by constructing style portfolios.

TL;DR: The authors proposed a Bayesian approach to model comparison of different risk factor models that can be used for model averaging and found differences in factor effects across quantiles of returns, which suggest that the standard conditional mean regression method may not be adequate for uncovering the risk-return characteristics of hedge funds.

TL;DR: The forward-backward algorithm as mentioned in this paper is an exact filtering algorithm which can efficiently calculate likelihoods, and which can be used to simulate from posterior distributions, and it has been used to calculate the distribution of a sum of gamma random variables, and to simulate their joint distribution given their sum.