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Ella · 2024年08月09日

yt = εt 为什么不是随机游走

  1. CFA II
  2. Quantitative

* 问题详情，请 查看题干

NO.PZ201709270100000502

问题如下：

2. Based on the regression output in Exhibit 1, the first-differenced series used to run Regression 2 is consistent with:

选项：

A.

a random walk.

B.

covariance stationarity.

C.

a random walk with drift.

解释：

B is correct. The critical t-statistic at a 5% confidence level is 1.98. As a result, neither the intercept nor the coefficient on the first lag of the first-differenced exchange rate in Regression 2 differs significantly from zero. Also, the residual autocorrelations do not differ significantly from zero. As a result, Regression 2 can be reduced to yt = εt with a mean-reverting level of b0/(1 – b1) = 0/1 = 0.

Therefore, the variance of yt in each period is Var(εt) = σ2. The fact that the residuals are not autocorrelated is consistent with the covariance of the times series, with itself being constant and finite at different lags. Because the variance and the mean of yt are constant and finite in each period, we can also conclude that yt is covariance stationary.

yt = εt 为什么不是随机游走

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袁园_品职助教 · 2024年08月10日

嗨，努力学习的PZer你好：


随机游走的定义就是b1=1,而不是b1=0.

简单随机游走（simple random walk）。

b1=1，b0=0，时间序列的方程为： Xt+1=Xt+εt

带漂移项的随机游走（random walk with a drift）。

b1=1，b0≠0，时间序列的方程为： Xt+1=b0+Xt+εt

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虽然现在很辛苦，但努力过的感觉真的很好，加油！

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