Covariance Prediction in Large Portfolio Allocation

Tangency portfolio weights for singular covariance matrix in small and large dimensions: estimation and test theory

In this paper we derive the finite-sample distribution of the estimated weights of the tangency portfolio when both the population and the sample covariance matrices are singular. These results are used in the derivation of a statistical test on the weights of the tangency portfolio where the distribution of the test statistic is obtained under both the null and the alternative hypotheses. More...

Optimal Portfolio Selection with Singular Covariance Matrix

In this paper we use the Moore-Penrose inverse in the case of a close to singular and ill-conditioned, or singular variance-covariance matrix, in the classic Portfolio Selection Problem. In this way the possible singularity of the variance-covariance matrix is tackled in an efficient way so that the various application of the Problem to benefit from the numerical tractability of the Moore-Penro...

Outlier Removal for Prediction of Covariance Matrices -- with an Application to Portfolio Optimization

Portfolio Allocation for Bayesian Optimization

Bayesian optimization with Gaussian processes has become an increasingly popular tool in the machine learning community. It is efficient and can be used when very little is known about the objective function, making it popular in expensive black-box optimization scenarios. It uses Bayesian methods to sample the objective efficiently using an acquisition function which incorporates the posterior...

Equity Allocation and Portfolio Selection in Insurance

A discrete time probabilistic model, for optimal equity allocation and portfolio selection, is formulated so as to apply to (at least) reinsurance. In the context of a company with several portfolios (or subsidiaries), representing both liabilities and assets, it is proved that the model has solutions respecting constraints on ROE’s, ruin probabilities and market shares currently in practical u...