Financial Risk Manager Part 1

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An analyst applies the following time series model to daily data:
$R_t = \text{returns} D_1 - \text{dummy variable for Monday and zero otherwise}$
$D_2 - \text{dummy variable for Tuesday and zero otherwise}$
$D_3 - \text{dummy variable for Wednesday and zero otherwise}$
$D_4 - \text{dummy variable for Thursday and zero otherwise}$

What is the interpretation of the parameter estimate for the intercept?_

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TTanishq

It's the average return for the 5 days from Monday to Friday.

It's the Friday deviation from the mean return for the week.

It's the average return on Monday.

It's the average return on Friday.

Explanation:

The intercept in this time series model represents the average return on Friday. This is because, for any observation made on a Friday, all the dummy variables ( $D_1$ , $D_2$ , $D_3$ , and $D_4$ ) will be zero. Therefore, the estimated value of the intercept, denoted as $\beta_0$ , will provide an estimate of the average return on Friday. This interpretation is based on the structure of the model, where the intercept represents the baseline return (in this case, for Friday) and the coefficients of the dummy variables represent the deviation of the returns on the other days from this baseline.

Choice A is incorrect. The intercept in this model does not represent the average return for the 5 days from Monday to Friday. Instead, it represents the average return on a specific day, which in this case is Friday.

Choice B is incorrect. The intercept does not represent the deviation of Friday's returns from the mean return for the week. Rather, it signifies the average return on Fridays.

Choice C is incorrect. The intercept does not denote Monday's average returns but rather indicates Friday's average returns._

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