Learning objectives: Describe the bootstrapping method and its advantage over Monte Carlo simulation. Describe the pseudo-random number generation method and how a good simulation design alleviates the effects the choice of the seed has on the properties of the generated series. Describe situations where the bootstrapping method is ineffective. Describe disadvantages of the simulation approach to financial problem solving.
Questions:
602.1. What is the crucial difference between bootstrapping and Monte Carlo simulation?
a. One uses artificial data but the other requires actual data
b. One requires random number generation but the other does not rely on randomness
c. One requires a distributional assumption but the other does not permit a distributional assumption
d. There is no crucial difference between bootstrapping and Monte Carlo simuation
602.2. When is bootstrapping likely to be ineffective?
a. When there are outliers in the data
b. When the data are not independent
c. Either when there are outliers in the data or when the data are not independent
d. In neither case: boostrapping easily handles outliers and non-independent data
602.3. Peter used a simple Monte Carlo simulation to estimate the price of an Asian option. In his first step, he specified a geometric Brownian motion (GBM) which is the same process used in the Black-Scholes-Merton model. His boss Sally observes, "This is nice work Peter, but the drawback to this approach is that you've assumed underlying returns are normally distributed. Yet we know that returns are fat-tailed in practice." How can Peter overcome this objection and include a fat-tailed assumption in his model?
a. He could assume the errors follow a GARCH process
b. He could assume the errors are drawn from a fat-tailed distribution; e.g., student's t
c. Either he could either assume errors follow a GARCH process or that errors are drawn from a fat-tailed distribution
d. Monte Carlo simulation cannot overcome this objection, this is a disadvantage of Monte Carlo simulation in comparison to bootstrapping
Answers here:
Questions:
602.1. What is the crucial difference between bootstrapping and Monte Carlo simulation?
a. One uses artificial data but the other requires actual data
b. One requires random number generation but the other does not rely on randomness
c. One requires a distributional assumption but the other does not permit a distributional assumption
d. There is no crucial difference between bootstrapping and Monte Carlo simuation
602.2. When is bootstrapping likely to be ineffective?
a. When there are outliers in the data
b. When the data are not independent
c. Either when there are outliers in the data or when the data are not independent
d. In neither case: boostrapping easily handles outliers and non-independent data
602.3. Peter used a simple Monte Carlo simulation to estimate the price of an Asian option. In his first step, he specified a geometric Brownian motion (GBM) which is the same process used in the Black-Scholes-Merton model. His boss Sally observes, "This is nice work Peter, but the drawback to this approach is that you've assumed underlying returns are normally distributed. Yet we know that returns are fat-tailed in practice." How can Peter overcome this objection and include a fat-tailed assumption in his model?
a. He could assume the errors follow a GARCH process
b. He could assume the errors are drawn from a fat-tailed distribution; e.g., student's t
c. Either he could either assume errors follow a GARCH process or that errors are drawn from a fat-tailed distribution
d. Monte Carlo simulation cannot overcome this objection, this is a disadvantage of Monte Carlo simulation in comparison to bootstrapping
Answers here:
