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PRMIA 8007 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Risk Measurement Techniques | - Volatility and correlation
|
| Probability and Statistics Foundations | - Statistical inference
|
| Simulation and Numerical Methods | - Monte Carlo simulation
|
| Stochastic Processes and Time Series | - Time series analysis
|
PRMIA Exam II: Mathematical Foundations of Risk Measurement - 2015 Edition Sample Questions:
1. You invest $100 000 for 3 years at a continuously compounded rate of 3%. At the end of 3 years, you redeem the investment. Taxes of 22% are applied at the time of redemption. What is your approximate after-tax profit from the investment, rounded to $10?
A) $7100
B) $7230
C) $7350
D) $9420
2. I have a portfolio of two stocks. The weights are equal. The one volatility is 30% while the other is 40%. The minimum and maximum possible values of the volatility of my portfolio are:
A) 5% and 35%
B) 30% and 40%
C) 10% and 40%
D) 10% and 70%
3. Which of the following is a false statement concerning the probability density function and the cumulative distribution function of a random variable?
A) the definite integral of the CDF from minus infinity to plus infinity is undefined.
B) the PDF is non-negative.
C) the definite integral of the PDF from minus infinity to plus infinity is zero.
D) the CDF approaches 1 as its argument approaches infinity.
4. Which of the following is consistent with the definition of a Type I error?
A) The probability of a Type I error is 100% minus the significance level
B) A Type I error would have occurred if the performance of a stock was positively correlated with the performance of a hedge fund, but in a linear regression, the hypothesis of positive correlation was rejected
C) A Type I occurs whenever data series are serially correlated
D) A Type I error would have occurred if the performance of a stock was positively correlated with the performance of a hedge fund, but in a linear regression, the hypothesis of no correlation was rejected
5. A typical leptokurtotic distribution can be described as a distribution that is relative to a normal distribution
A) flat and thick at the center and with heavy (fat) tails
B) peaked and thin at the center and with heavy (fat) tails
C) peaked and thin at the center and with thin tails
D) flat and thick at the center and with thin tails
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: A | Question # 3 Answer: C | Question # 4 Answer: B | Question # 5 Answer: B |







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