Câu hỏi: Suppose that we wanted to sum the 2007 returns on ten shares to calculate the return on a portfolio over that year. What method of calculating the individual stock returns would enable us to do this?

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30/08/2021
3.5 6 Đánh giá

A. Simple

B. Continuously compounded

C. Neither approach would allow us to do this validly

D. Either approach could be used and they would both give the same portfolio return

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Câu hỏi khác cùng đề thi
Câu 1: Which of the following is the correct value for?

A. 2.89

B. 1.30

C. 0.84

D. We cannot determine the value of from the information given in the question

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30/08/2021 8 Lượt xem

Câu 2: Which one of the following is NOT an assumption of the classical linear regression model?

A. The explanatory variables are uncorrelated with the error terms

B. The disturbance terms have zero mean

C. The dependent variable is not correlated with the disturbance terms

D. The disturbance terms are independent of one another

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30/08/2021 8 Lượt xem

Câu 3: The value of the Durbin Watson test statistic in a regression with 4 regressors (including the constant term) estimated on 100 observations is 3.6. What might we suggest from this? 

A. The residuals are positively autocorrelated

B. The residuals are negatively autocorrelated

C. There is no autocorrelation in the residuals

D. The test statistic has fallen in the intermediate region

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30/08/2021 9 Lượt xem

Câu 4: Which of the following would you expect to be a problem associated with adding lagged values of the dependent variable into a regression equation?

A. The assumption that the regressors are non-stochastic is violated

B. A model with many lags may lead to residual non-normality

C. Adding lags may induce multicollinearity with current values of variables

D. The standard errors of the coefficients will fall as a result of adding more explanatory variables

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30/08/2021 8 Lượt xem

Câu 5: What is the relationship, if any, between t-distributed and F-distributed random variables?

A. A t-variate with z degrees of freedom is also an F(1, z)

B. The square of a t-variate with z degrees of freedom is also an F(1, z)

C. A t-variate with z degrees of freedom is also an F(z, 1)

D. There is no relationship between the two distributions

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