贝叶斯对回报可预测性的调和
Borys Koval1,2, Sylvia Frühwirth-Schnatter3, Leopold Sögner2,1
1Vienna Graduate School of Finance, WU Vienna University of Economics and Business, 1020 Vienna, Austria.
Studies in nonlinear dynamics and econometrics
|May 8, 2024
概括
这项研究引入了贝叶斯的回报可预测性方法,使用稳定的向量自回归 (VAR) 模型. 贝叶斯方法的表现优于传统的估计方法,最近的财务数据显示回报可预测性的证据很弱.
科学领域:
- 金融计量经济学 金融计量经济学
- 贝叶斯统计学 贝叶斯统计学
- 资产定价是指资产的定价.
背景情况:
- 回报的可预测性是金融经济学的一个关键问题.
- 像普通最小平方 (OLS) 和减少偏差估计器这样的现有方法都有局限性.
- 矢量自回归 (VAR) 模型通常用于分析财务时间序列.
研究的目的:
- 开发和评估一种新的贝叶斯方法来研究回报可预测性.
- 将拟议的贝叶斯方法与OLS和降低偏差估计器的性能进行比较.
- 用历史财务数据和各种预测变量来评估回报可预测性.
主要方法:
- 在双变的VAR模型中开发一个关键参数的新型收缩先验.
- 贝叶斯方法与OLS和Amihud和Hurvich (2004) 通过模拟的减少偏差估计器的比较.
- 该方法应用于历史的CRSP价值加权收益率和股息价格比率以及替代预测因素 (Welch & Goyal,2008).
主要成果:
- 模拟研究表明,贝叶斯方法在控制错误阳性和负值方面超过了减少偏差估计器.
- 使用1926-2004年数据的经验分析不支持回归可预测性;最近的数据 (1953-2021) 显示可预测性很弱.
- 替代预测变量也为回报可预测性提供了微弱的证据,而非样本预测证实了这一点.
结论:
- 建议的贝叶斯方法为估计回报可预测性提供了一个强大的替代方案.
- 对回报可预测性的证据对所使用的数据期和预测变量敏感.
- 这些发现表明,近期股票市场的可预测性有限,尽管存在.
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