论文标题
使用样本创新的三因素FAMA回归模型的新外观
A New Look to Three-Factor Fama-French Regression Model using Sample Innovations
论文作者
论文摘要
与市场收益相比，Fama-French模型广泛用于评估投资组合的性能。在Fama-French模型中，所有因素都是时间序列数据。横截面数据与时间序列数据略有不同。时间序列回归的一个独特问题是，与典型的横截面数据相比，时间序列回归中的R平方通常很高，尤其是与典型的R平方相比。 R平方的高价值可能会导致误解，即回归模型很好地拟合了观察到的数据，并且因变量很好地解释了因变量的差异。因此，为了进行回归分析，并克服串行依赖性和波动性聚类，我们使用标准计量经济学时间序列模型来得出样本创新。在这项研究中，我们以两种不同的方式对Fama-French模型进行了验证和验证：使用Fama-French模型中的因素和资产回报，并考虑Fama-French模型中的样本创新，而不是研究因素。比较了本研究中考虑的两种方法，我们建议应考虑使用重型尾部分布的FAMA-FRENCH模型，因为尾巴行为与Fama-French模型相关，包括财务数据，而QQ图并未证实将正态分布作为模型中噪声的理论分布的选择。
The Fama-French model is widely used in assessing the portfolio's performance compared to market returns. In Fama-French models, all factors are time-series data. The cross-sectional data are slightly different from the time series data. A distinct problem with time-series regressions is that R-squared in time series regressions is usually very high, especially compared with typical R-squared for cross-sectional data. The high value of R-squared may cause misinterpretation that the regression model fits the observed data well, and the variance in the dependent variable is explained well by the independent variables. Thus, to do regression analysis, and overcome with the serial dependence and volatility clustering, we use standard econometrics time series models to derive sample innovations. In this study, we revisit and validate the Fama-French models in two different ways: using the factors and asset returns in the Fama-French model and considering the sample innovations in the Fama-French model instead of studying the factors. Comparing the two methods considered in this study, we suggest the Fama-French model should be considered with heavy tail distributions as the tail behavior is relevant in Fama-French models, including financial data, and the QQ plot does not validate that the choice of the normal distribution as the theoretical distribution for the noise in the model.
