8002 Practice Exam Questions and Answers

A.  

The commutativity of multiplication is defined by

B.  

The existence of negatives is defined by

C.  

The distributivity of multiplication is defined by

D.  

The associativity of multiplication is defined by

A.  

associativity

B.  

commutativity

C.  

distributivity

D.  

invertibility

A.  

30% and 40%

B.  

5% and 35%

C.  

10% and 40%

D.  

10% and 70%

A.  

1.50%

B.  

6%

C.  

6.14%

D.  

None of the above

A.  

a vector that shows the direction of fastest change of a function

B.  

matrix of second partial derivatives of a function

C.  

infinite at a maximum point

D.  

a matrix containing the function's second partial derivatives

A.  

A structural break in the dependent variable

B.  

A high positive correlation between two explanatory variables

C.  

The omission of a relevant explanatory variable

D.  

Using an inappropriate functional form in the model

A.  

(0, 2, 1)

B.  

(0, 0, 0)

C.  

(1, 1, 1)

D.  

(0, 1, -1)

A.  

2

B.  

1.95

C.  

1.86

D.  

1.75

A.  

b=0.5, a=2.5

B.  

b=0.5, a=1.5

C.  

b=2, a=4

D.  

b=2, a=0

A.  

0.125

B.  

2.5

C.  

-1.25

D.  

-2.5

A.  

1 / (x+y)

B.  

(∆x + ∆y) / (x+y)

C.  

-∆x/(x+y) - ∆y/(x+y)

D.  

ln(x+y) ∆x + ln(x+y) ∆y

A.  

Finding parameter estimates of a given density function

B.  

Estimating the solution of a partial differential equation

C.  

Solving a portfolio optimization problem

D.  

Estimating the implied volatility of a simple European option

A.  

the change in price (dS) is normally distributed

B.  

1/S is normally distributed

C.  

ln(dS/S) is normally distributed

D.  

ln(1+dS/S) is normally distributed

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

A.  

0.02

B.  

0.14

C.  

0.25

D.  

0.50

A.  

A technique that is applied in regression models to improve the accuracy of the coefficient estimates

B.  

A numerical method for finding portfolio weights to minimize the variance of a portfolio that has a given expected return

C.  

A numerical method for finding the variance of the underlying that is implicit in a market price of an option

D.  

A method for reducing the number of simulations required in a Monte Carlo simulation

A.  

F(x)G(x)

B.  

F(x)g(x) + f(x)G(x)

C.  

F(x)g(x) - ∫F(x)g'(x)dx

D.  

f(x)G(x) - ∫F(x)g'(x)dx

A.  

90 degrees

B.  

180 degrees

C.  

57 degrees

D.  

45 degrees

A.  

peaked and thin at the center and with heavy (fat) tails

B.  

peaked and thin at the center and with thin tails

C.  

flat and thick at the center and with heavy (fat) tails

D.  

flat and thick at the center and with thin tails