Reading 9: Common Probability Distributions LOS o习题精选 number, describe, face, depends

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LOS o: Explain Monte Carlo simulation and historical simulation and describe their major applications and limitations.

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.

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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.

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Many analysts prefer to use Monte Carlo simulation rather than historical simulation because:

A) computers can manipulate theoretical data much more quickly than historical data.

B) past distributions cannot address changes in correlations or events that have not happened before.

C) it is much easier to generate the required variables.

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While the past is often a good predictor of the future, simulations based on past distributions are limited to reflecting changes and events that actually occurred. Monte Carlo simulation can be used to model based on parameters that are not limited to past experience.

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The difference between a Monte Carlo simulation and a historical simulation is that a historical simulation uses randomly selected variables from past distributions, while a Monte Carlo simulation:

A) projects variables based on a priori principles.

B) uses a computer to generate random variables.

C) uses randomly selected variables from future distributions.

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A Monte Carlo simulation uses a computer to generate random variables from specified distributions.

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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) Monte Carlo simulation.

B) discrete analysis.

C) historical simulation.

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This is a typical example of historical simulation.

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Monte Carlo simulation is necessary to:

A) approximate solutions to complex problems.

B) reduce sampling error.

C) compute continuously compounded returns.

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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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In which of the following cases would Monte Carlo simulation least likely be needed? Payoff of a:

A) roulette wheel.

B) GNME.

C) European option.

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The probability distribution of a roulette wheel would be easy to estimate using empirical or a priori methodology.