Portfolio Choices with Many Big Models
提出一种贝叶斯平均异质向量自回归投资组合策略，利用多个大数据模型，在日、周、月数据上优于现有方法，适用于对模型误设稳健的投资决策。
This paper proposes a Bayesian-averaging heterogeneous vector autoregressive portfolio choice strategy with many big models that outperforms existing methods out-of-sample on numerous daily, weekly, and monthly datasets. The strategy assumes that excess returns are approximately determined by a time-varying regression with a large number of explanatory variables that are the sample means of past returns. Investors consider the possibility that every period there is a regime change by keeping track of many models, but doubt that any specification is able to perfectly predict the distribution of future returns, and compute portfolio choices that are robust to model misspecification. This paper was accepted by Tyler Shumway, finance.