arXiv 2604.044302026-04-06q-fin.PR

共同定价因子动物园

The Co-Pricing Factor Zoo

London School of Economics and Political Science（伦敦政治经济学院）；Warwick Business School, The University of Warwick（华威大学华威商学院）；Centre for Economic Policy Research（经济政策研究中心）

Alexander Dickerson, Christian Julliard, Philippe Mueller

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AI总结：

本文分析18万亿美元模型，发现仅需股权和非交易因子即可解释企业债券风险溢价，从而使大量债券因子文献变得冗余。通过贝叶斯模型平均随机折扣因子，能更优地解释风险溢价，且具有持续性和预测性。

摘要：

我们分析了18万亿美元模型，用于企业债券和股票收益的联合定价。令人惊讶的是，我们发现仅需股权和非交易因子即可解释企业债券风险溢价，一旦考虑了其国债期限结构风险。尽管只有少数因子可能是稳健的风险定价来源，但真正的潜在随机折扣因子在可观测因子空间中是密集的。因此，贝叶斯模型平均随机折扣因子在样本内和样本外都能更好地解释风险溢价，通过最优聚合数十个作为常见潜在风险噪声代理的因子，产生样本外夏普比率1.5至1.8。该SDF及其条件均值和波动率具有持续性，跟踪商业周期和经济不确定时期，并预测未来资产回报。

英文摘要：

We analyze 18 quadrillion models for the joint pricing of corporate bond and stock returns. Strikingly, we find that equity and nontradable factors alone suffice to explain corporate bond risk premia once their Treasury term structure risk is accounted for, rendering the extensive bond factor literature largely redundant for this purpose. While only a handful of factors, behavioral and nontradable, are likely robust sources of priced risk, the true latent stochastic discount factor is dense in the space of observable factors. Consequently, a Bayesian Model Averaging Stochastic Discount Factor explains risk premia better than all low-dimensional models, in- and out-of-sample, by optimally aggregating dozens of factors that serve as noisy proxies for common underlying risks, yielding an out-of-sample Sharpe ratio of 1.5 to 1.8. This SDF, as well as its conditional mean and volatility, are persistent, track the business cycle and times of heightened economic uncertainty, and predict future asset returns.

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