Sparse portfolio selection via Bayesian multiple testing

DOI10.1007/s13571-020-00240-zzbMath1476.62222arXiv1705.01407OpenAlexW3098952024MaRDI QIDQ2061782

Rituparna Sen, Sourish Das

Publication date: 21 December 2021

Published in: Sankhyā. Series B (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1705.01407

zbMATH Keywords

oracleCAPMhierarchical Bayesfactor modeldiscrete mixture prior

Mathematics Subject Classification ID

Applications of statistics to actuarial sciences and financial mathematics (62P05) Bayesian inference (62F15) Portfolio theory (91G10) Paired and multiple comparisons; multiple testing (62J15)

Uses Software

• GitHub

Cites Work

• Likelihood Ratio Tests for Model Selection and Non-Nested Hypotheses
• High-dimensionality effects in the Markowitz problem and other quadratic programs with linear constraints: risk underestimation
• Asymptotic Bayes-optimality under sparsity of some multiple testing procedures
• Asymptotic properties of Bayes risk for the horseshoe prior
• Uncertainty quantification for the horseshoe (with discussion)
• Regularizing portfolio risk analysis: a Bayesian approach
• Portfolio optimization with linear and fixed transaction costs
• The horseshoe estimator for sparse signals
• Bayesian Variable Selection in Linear Regression
• Distribution of a Sum of Weighted Chi-Square Variables
• Vast Portfolio Selection With Gross-Exposure Constraints
• Common risk factors in the returns on stocks and bonds
• Prior distributions for variance parameters in hierarchical models (Comment on article by Browne and Draper)

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