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Questions # 11:

Simple linear regression involves one dependent variable, one independent variable and one error variable. In contrast, multiple linear regression uses…

Options:

A.

One dependent variable, many independent variables, one error variable

B.

Many dependent variables, one independent variable, one error variable

C.

One dependent variable, one independent variable, many error variables

D.

Many dependent variables, many independent variables, many error variables

Questions # 12:

I have $5m to invest in two stocks: 75% of my capital is invested in stock 1 which has price 100 and the rest is invested in stock 2, which has price 125. If the price of stock 1 falls to 90 and the price of stock 2 rises to 150, what is the return on my portfolio?

Options:

A.

-2.50%

B.

-5%

C.

2.50%

D.

5%

Questions # 13:

A linear regression gives the following output:

Figures in square brackets are estimated standard errors of the coefficient estimates.

What is the value of the test statistic for the hypothesis that the coefficient of is less than 1?

Options:

A.

0.32

B.

0.64

C.

0.96

D.

1.92

Questions # 14:

An option has value 10 when the underlying price is 99 and value 9.5 when the underlying price is 101. Approximate the value of the option delta using a first order central finite difference.

Options:

A.

-4

B.

0.25

C.

-0.5

D.

-0.25

Questions # 15:

The gradient of a function f(x, y, z) = x + y2 - x y z at the point x = y = z = 1 is

Options:

A.

(0, 2, 1)

B.

(0, 0, 0)

C.

(1, 1, 1)

D.

(0, 1, -1)

Questions # 16:

You are investigating the relationship between weather and stock market performance. To do this, you pick 100 stock market locations all over the world. For each location, you collect yesterday's mean temperature and humidity and yesterday's local index return. Performing a regression analysis on this data is an example of…

Options:

A.

Simple time-series regression

B.

Multiple time-series regression

C.

Simple cross-section regression

D.

Multiple cross-section regression

Questions # 17:

Suppose we perform a principle component analysis of the correlation matrix of the returns of 13 yields along the yield curve. The largest eigenvalue of the correlation matrix is 9.8. What percentage of return volatility is explained by the first component? (You may use the fact that the sum of the diagonal elements of a square matrix is always equal to the sum of its eigenvalues.)

Options:

A.

64%

B.

75%

C.

98%

D.

Cannot be determined without estimates of the volatilities of the individual returns

Questions # 18:

An underlying asset price is at 100, its annual volatility is 25% and the risk free interest rate is 5%. A European put option has a strike of 105 and a maturity of 90 days. Its Black-Scholes price is 7.11. The options sensitivities are: delta = -0.59; gamma = 0.03; vega = 19.29. Find the delta-gamma approximation to the new option price when the underlying asset price changes to 105

Options:

A.

6.49

B.

5.03

C.

4.59

D.

4.54

Questions # 19:

An underlying asset price is at 100, its annual volatility is 25% and the risk free interest rate is 5%. A European call option has a strike of 85 and a maturity of 40 days. Its Black-Scholes price is 15.52. The options sensitivities are: delta = 0.98; gamma = 0.006 and vega = 1.55. What is the delta-gamma-vega approximation to the new option price when the underlying asset price changes to 105 and the volatility changes to 28%?

Options:

A.

17.33

B.

18.75

C.

19.23

D.

20.54

Questions # 20:

Which of the following is consistent with the definition of a Type I error?

Options:

A.

The probability of a Type I error is 100% minus the significance level

B.

A Type I error would have occurred if the performance of a stock was positively correlated with the performance of a hedge fund, but in a linear regression, the hypothesis of positive correlation was rejected

C.

A Type I error would have occurred if the performance of a stock was positively correlated with the performance of a hedge fund, but in a linear regression, the hypothesis of no correlation was rejected

D.

A Type I occurs whenever data series are serially correlated

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