美国康纳尔代写作业:预测的任何限制
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美国康纳尔代写作业:预测的任何限制

2017-11-21 23:11

在Fama和Macbeth(1973)的研究中,我们可以将预先确定的解释变量添加到市场beta中资产回报的月wise cross section回归中。如果预期回报的所有差异都是由betas解释的,那么任何附加变量的系数都不应与0完全不同。因此,在横断面分析中,重要的是仔细选择附加变量。在这方面,我们可以以Fama和MacBeth(1973)的研究为例。在这个工作中,附加变量是平方。这些变量对解释平均资产回报没有影响。通过使用时间序列回归,我们也可以检验市场betas完全解释预期资产回报的假设。正如我们已经提到的,在时间序列回归分析中,常数项是资产的平均收益率和sharpel - lintner模型预测的超额收益之间的差额。我们不能在组合中组合资产,因为常数项与零的关系是完全不同的,这只适用于模型。例如,对于投资组合来说,高市盈率和低市盈率的常数项应该是零。因此,为了验证假设是否足够解释预期收益,我们可以估计投资组合的时间序列回归,然后检验联合假设,以防止对零。在这种方法中,我们必须选择投资组合的形式,以描述CAPM预测的任何限制。
美国康纳尔代写作业:预测的任何限制
In the study by Fama and Macbeth (1973), we can add pre-determined explanatory variables to the month wise cross section regressions of asset return on the market beta. Provided that all the differences in expected return are explained by the betas, the coefficients of any additional variable should not be dependably different from zero. So, in the cross-section analysis the important thing is to carefully choose the additional variable. In this regard we can take the example of the study by Fama and MacBeth (1973). In that work the additional variables are squared betas. These variables have no impact in explaining the average asset return. By using the time series regression we can also test the hypothesis that market betas completely explain expected asset return. As we have already mentioned that in the time series regression analysis, the constant term is the difference between the asset’s average return and the excess return predicted by the Sharpe-Lintner model. We cannot group assets in portfolios where the constant term is dependably different from zero and this applies only the model holds true. For example, for a portfolio, the constant term for a high earning to price ratio and low earning to price ratio should be zero. Therefore, in order to test the hypothesis that betas suffice to explain expected returns, we can estimate the time-series regression for the portfolios and then test the joint hypothesis for the intercepts against zero. In this kind of approach we have to choose the form of the portfolio in a way which will depict any limitation of the CAPM prediction.