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Reading 9: Common Probability Distributions - LOS l ~ Q1-3

Q1. A drawback of historical simulation is it:

A)     depends on the accuracy of the random number generator.

B)     may not accurately reflect possible outcomes.

C)     may not account for very rare events.

Q2. Joan Biggs, CFA, acquires a large database of past returns on a variety of assets. Biggs then draws random samples of sets of returns from the database and analyzes the resulting distributions. Biggs is engaging in:

A)    historical simulation.

B)    Monte Carlo simulation.

C)    discrete analysis.

Q3. Monte Carlo simulation is necessary to:

A)     approximate solutions to complex problems.

B)     reduce sampling error.

C)     compute continuously compounded returns.

答案和详解如下:

Q1. A drawback of historical simulation is it:

A)     depends on the accuracy of the random number generator.

B)     may not accurately reflect possible outcomes.

C)     may not account for very rare events.

Correct answer is C)

There are two major problems with historical simulation. The first is that it cannot account for events that do not occur in the sample. If a security began trading after 1987, for example, there would be no evidence of its behavior in a market crash. The other drawback is that the analyst cannot change the parameters of the distribution to examine how small changes might affect the asset’s behavior.

Q2. Joan Biggs, CFA, acquires a large database of past returns on a variety of assets. Biggs then draws random samples of sets of returns from the database and analyzes the resulting distributions. Biggs is engaging in:

A)    historical simulation.

B)    Monte Carlo simulation.

C)    discrete analysis.

Correct answer is A)

This is a typical example of historical simulation.

Q3. Monte Carlo simulation is necessary to:

A)     approximate solutions to complex problems.

B)     reduce sampling error.

C)     compute continuously compounded returns.

Correct answer is A)

This is the purpose of this type of simulation. The point is to construct distributions using complex combinations of hypothesized parameters.

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