The correct statistic to calculate in order to test the hypothesis $H_0: \beta = 1$ against $H_1: \beta \neq 1$ is the t-statistic. The t-statistic is used in hypothesis testing to determine if there is a significant difference between the sample estimate and the hypothesized value of the population parameter. In this case, the analyst is testing the beta of stock CDM against the hypothesized value of 1.

The t-statistic is calculated using the formula:

$t = \frac{\beta_{\text{estimated}} - \beta_0}{\text{SE}(\text{estimated } \beta)}$

Where:

• $\beta_{\text{estimated}}$ is the estimated beta value from the regression, which is 0.86.
• $\beta_0$ is the hypothesized value of beta, which is 1.
• $\text{SE}(\text{estimated } \beta)$ is the standard error of the estimated beta, which is 0.80.

Plugging in the values, we get:

$t = \frac{0.86 - 1}{0.80} = -0.175$

The absolute value of the t-statistic is compared to the critical value from the t-distribution, which at a common significance level of 0.05 and with a certain degrees of freedom (which is not provided in the question), would typically be around 1.96 for a two-tailed test. Since $|t| = 0.175$ is less than 1.96, the null hypothesis cannot be rejected, indicating that there is not enough evidence to conclude that the beta of stock CDM is different from 1 at the given significance level.

This analysis is based on the principles of linear regression and hypothesis testing as outlined in the referenced material by the Global Association of Risk Professionals.