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PRMIA 8007 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Probability Theory | 25% | - Random variables and distributions
|
| Topic 2: Calculus and Functions | 20% | - Multivariable calculus
|
| Topic 3: Linear Algebra and Matrix Theory | 20% | - Vectors and eigenvalues
|
| Topic 4: Statistics and Regression Analysis | 20% | - Linear regression
|
| Topic 5: Numerical Methods & Financial Mathematics | 15% | - Root finding and approximation - Time value of money - Basics of risk modeling |
PRMIA Exam II: Mathematical Foundations of Risk Measurement - 2015 Edition Sample Questions:
What can be said about observations of random variables that are i.i.d. a normally distributed?
- A. The estimated mean divided by the estimated variance has a Chi2-distribution
- B. The estimated mean divided by the estimated standard deviation has a t-distribution
- C. The estimated mean divided by the estimated standard deviation has a Chi2-distribution
- D. The estimated mean divided by the estimated variance has a t-distribution
An option has value 10 when the underlying price is 99 and value 9.5 when the underlying price is 101.
Approximate the value of the option delta using a first order central finite difference.
- A. 0.25
- B. -4
- C. -0.5
- D. -0.25
The bisection method can be used for solving f(x)=0 for a unique solution of x, when
- A. The function f(x) is continuous and monotonic
- B. The function f(x) is differentiable
- C. The function f(x) is differentiable and we have an explicit expression for the derivative
- D. The function f(x) is continuous
Simple linear regression involves one dependent variable, one independent variable and one error variable. In contrast, multiple linear regression uses...
- A. One dependent variable, many independent variables, one error variable
- B. Many dependent variables, one independent variable, one error variable
- C. Many dependent variables, many independent variables, many error variables
- D. One dependent variable, one independent variable, many error variables
What is a Hessian?
- A. The vector of partial derivatives of a contingent claim
- B. The point at which a minimum of a multidimensional function is achieved
- C. A matrix of second derivatives of a function
- D. Correlation matrix of market indices
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