PRM Certification - Exam II: Mathematical Foundations of Risk Measurement
Last Update Dec 26, 2024
Total Questions : 132
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| Exam Name | PRM Certification - Exam II: Mathematical Foundations of Risk Measurement |
| Exam Code | 8002 |
| Actual Exam Duration | 120 minutes |
| Expected no. of Questions in Actual Exam | 90 |
| Official Information | https://prmia.org/Public/PRM/PRM_Handbook_Digital%20Resources.aspx |
| See Expected Questions | PRMIA 8002 Expected Questions in Actual Exam |
| Take Self-Assessment | Use PRMIA 8002 Practice Test to Assess your preparation - Save Time and Reduce Chances of Failure |
| Section | Weight | Objectives |
|---|---|---|
| Chapter 1 | Reviews the fundamental mathematical concepts: the symbols used and the basic rules of arithmetic, equations and inequalities, functions and graphs. |
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| Chapter 2 | Introduces the descriptive statistics that are commonly used to summarize the historical characte ristics of financial data: the sample moments of returns distributions, ‘downside’ risk statistics, and measures of covariation (e.g. correlation) between two random variables. | |
| Chapter 3 | Focuses on differentiation and integration, Taylor expansion and optimization. Financial applications include calculating the convexity of a bond portfolio and the estimation of the delta and gamma of an options portfolio. | |
| Chapter 4 | Covers matrix operations, special types of matrices and the laws of matrix algebra, the Cholesky decomposition of a matrix, and eigenvalues and igenvectors. Examples of financial applications include manipulating covariance matrices, calculating the variance of the returns to a portfolio of assets, hedging a vanilla option position, and simulating correlated sets of returns. | |
| Chapter 5 | First introduces the concept of probability and the rules that govern it. Then some common probability distributions for discrete and continuous random ariables are described, along with their expectation and variance and various concepts relating to joint distributions, such as covariance and correlation, and the expected value and variance of a linear combination of random variables. | |
| Chapter 6 | Covers the simple and multiple regression models, with applications to the capital asset pricing model and arbitrage pricing theory. The statistical inference section deals with both prediction and hypothesis testing, for instance, of the efficient market hypothesis. | |
| Chapter 7 | Looks at solving implicit equations (e.g. the Black-Scholes formula for implied volatility), lattice methods, finite differences and simulation. Financial applications include option valuation and estimating the ‘Greeks’ for complex options. |
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TESTED 26 Dec 2024
