Tests of risk premia in linear factor models
线性因子模型中风险溢价的检验AI 解读 ↓
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线性因子模型中风险溢价的检验
- 研究问题
- 在线性因子模型中，资产贝塔较小或资产数量较多时，传统两步风险溢价推断为何失真，如何构造更可靠的检验？
- 方法与思路
- 作者基于既有极大似然估计构造新统计量，与 Fama–MacBeth 和 GLS 两步统计量比较大小、功效及置信集合。
- 材料陈述的贡献
- 摘要称传统两步检验可能严重失真，新统计量给出明显不同的风险溢价置信集合，其中部分集合无界或覆盖整条实线。
- 阅读关注点
- 应核对小贝塔与资产数量的理论框架、新统计量的有效条件，以及无界置信集合如何影响对定价模型的解释。
- 阅读边界
- 仅据所给出版社摘要；完整实线的置信集合缺乏信息，不能理解为证实模型，也不能把应用结论推广到其他资产样本。
原文摘要
We show that statistical inference on the risk premia in linear factor models that is based on the Fama-MacBeth (FM) and generalized least squares (GLS) two-pass risk premia estimators is misleading when the [beta]'s are small and/or the number of assets is large. We propose novel statistics, that are based on the maximum likelihood estimator of Gibbons [Gibbons, M., 1982. Multivariate tests of financial models: A new approach. Journal of Financial Economics 10, 3-27], which remain trustworthy in these cases. The inadequacy of the FM and GLS two-pass t/Wald statistics is highlighted in a power and size comparison using quarterly portfolio returns from Lettau and Ludvigson [Lettau, M., Ludvigson, S., 2001. Resurrecting the (C)CAPM: A cross-sectional test when risk premia are time-varying. Journal of Political Economy 109, 1238-1287]. The power and size comparison shows that the FM and GLS two-pass t/Wald statistics can be severely size distorted. The 95% confidence sets for the risk premia in the above-cited work that result from the novel statistics differ substantially from those that result from the FM and GLS two-pass t-statistics. They show support for the human capital asset pricing model although the 95% confidence set for the risk premia on labor income growth is unbounded. The 95% confidence sets show no support for the (scaled) consumption asset pricing model, since the 95% confidence set of the risk premia on the scaled consumption growth consists of the whole real line, but do not reject it either.