1、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) uses randomly selected variables from future distributions.
B) uses a computer to generate random variables.
C) uses variables based on roulette odds.
D) projects variables based on a priori principles.
2、Many analysts prefer to use Monte Carlo simulation rather than historical simulation because:
A) it is much easier to generate the required variables.
B) past data is often proprietary and difficult to obtain.
C) past distributions cannot address changes in correlations or events that have not happened before.
D) computers can manipulate theoretical data much more quickly than historical data.
3、In which of the following cases would Monte Carlo simulation NOT be needed? Payoff of a:
A) European option.
B) roulette wheel.
C) GNME.
D) convertible bond with a call feature.
4、Monte Carlo simulation is necessary to:
A) approximate solutions to complex problems.
B) reduce sampling error.
C) determine a threshold return.
D) compute continuously compounded returns.
5、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)
B) historical simulation.
C) discrete analysis.
D) continuous analysis.
6、A drawback of historical simulation is it:
A) depends on the accuracy of the random number generator.
B) may not account for very rare events.
C) depends on the hypothesized parameters.
D) may not accurately reflect possible outcomes.
答案和详解如下：
1、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) uses randomly selected variables from future distributions.
B) uses a computer to generate random variables.
C) uses variables based on roulette odds.
D) projects variables based on a priori principles.
The correct answer was B)
A
2、Many analysts prefer to use Monte Carlo simulation rather than historical simulation because:
A) it is much easier to generate the required variables.
B) past data is often proprietary and difficult to obtain.
C) past distributions cannot address changes in correlations or events that have not happened before.
D) computers can manipulate theoretical data much more quickly than historical data.
The correct answer was C)
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.
3、In which of the following cases would Monte Carlo simulation NOT be needed? Payoff of a:
A) European option.
B) roulette wheel.
C) GNME.
D) convertible bond with a call feature.
The correct answer was B)
The probability distribution of a roulette wheel would be easy to estimate using empirical or a priori methodology.
4、Monte Carlo simulation is necessary to:
A) approximate solutions to complex problems.
B) reduce sampling error.
C) determine a threshold return.
D) compute continuously compounded returns.
The correct answer was A)
This is the purpose of this type of simulation. The point is to construct distributions using complex combinations of hypothesized parameters.
5、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)
B) historical simulation.
C) discrete analysis.
D) continuous analysis.
The correct answer was B)
This is a typical example of historical simulation.
6、A drawback of historical simulation is it:
A) depends on the accuracy of the random number generator.
B) may not account for very rare events.
C) depends on the hypothesized parameters.
D) may not accurately reflect possible outcomes.
The correct answer was B)
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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