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. This is because the t-statistic is used to test hypotheses about regression parameters in a linear regression model. The t-statistic is calculated using the formula:

$t = \frac{\text{estimated } \beta - \text{hypothesized } \beta}{\text{standard error of the estimated } \beta}$

In this case, the estimated beta ($\hat{\beta}$) is 0.86, the hypothesized beta ($\beta_0$) is 1, and the standard error of the estimated beta ($SE(\hat{\beta})$) is 0.80. Plugging these values into the formula gives:

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

Since the absolute value of the calculated t-statistic ($|t|$) is less than the critical value of 1.96 (which corresponds to a 95% confidence level for a two-tailed test), we cannot reject the null hypothesis. This means that there is not enough evidence to conclude that the beta of stock CDM is different from 1 based on the given data and test. The other options provided (Chi-squared test statistic, Jarque-Bera test statistic, and Sum of squared residuals) are not appropriate for testing a hypothesis about a regression coefficient in this context.