Bayesian Portfolio Regularization

风格化的事实上的投资组合模型的性能表明，理论上最优的投资组合策略的实证表现非常差的方法相比，基于简单的和理论上低劣的策略。 该研究项目的目标是开发一类新的贝叶斯正则化策略，以实现鲁棒和高性能的投资组合模型。贝叶斯方法直接应用于投资组合权重的估计，为大量不同的正则化策略以及模型选择和评估提供了一个统一的框架，除了理论贡献外，该项目还将为各种资产和市场提供新策略的质量方面的经验证据。将特别注意在高维设置的估计策略。

Stylized facts on the performance of portfolio models indicate that the empirical counterparts of theoretically optimal portfolio strategies show a very poor performance compared to approaches based on simpler and theoretically inferior strategies. The goal of the proposed research project is to develop a new class of Bayesian regularization strategies to achieve robust and high-performing portfolio models. The Bayesian approach applied directly to the estimation of the portfolio weights provides a unifying framework for a large range of different regularization strategies as well as for model selection and evaluation.Besides the theoretical contributions the project will also provide empirical evidence on the quality of the proposed new strategies for a variety of assets and markets. Special attention will be given to estimation strategies in high-dimensional settings.