FRM学习资料八：2010 FRM Examination Part I AIM Statements

金融风险管理师（FRM）学习资料：2010 FRM Examination Part I AIM Statements PDF电子书

5th HANDBOOK 中FRM一级需要看的内容

PART ONE Quantitative Analysis
Chapter1—4（全部）

PART TWO Capital Markets
Chapter5—9（6.4 奇异期权除外 7.6证券化除外）

PART THREE Market Risk Management
Chapter 10、12、13、14、15

PART FOUR Investment Risk Management
Chapter 16 中的 16.1和16.2

PART FIVE Credit Risk Management
Chapter 19 中的 19.1 19.2.1 19.2.4 19.4.2 19.4.3

2010 FRM Examination Part I AIM Statements

AIMS – Candidates, after completing this reading, should be able to:
Describe the responsibility of each GARP member with respect to professional integrity,
ethical conduct, conflicts of interest, confidentiality of information and adherence to
generally accepted practices in risk management.
Describe the potential consequences of violating the GARP Code of Conduct.

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AIM Statements, 2010 FRM Part I Page 9 of 39
2010 by Global Association of Risk Professionals, Inc.

Quantitative Analysis
Part I Exam Weight: 20%
Probability distributions
Mean, standard deviation, correlation, skewness, and kurtosis
Estimating parameters of distributions
Linear regression
Statistical inference and hypothesis testing
Estimating correlation and volatility: EWMA and GARCH Models
Maximum likelihood methods
Volatility term structures
Simulation methods
Readings for Quantitative Analysis
8. Damodar Gujarati, Essentials of Econometrics, 3rd
Edition (New York: McGraw‐Hill,
2006).
Chapter 1 – The Nature and Scope of Econometrics
Chapter 2 – Review of Statistics: Probability and Probability Distributions
Chapter 3 – Characteristics of Probability Distributions
Chapter 4 – Some Important Probability Distributions
Chapter 5 – Statistical Inference: Estimation and Hypothesis Testing
Chapter 6 – Basic Ideas of Linear Regression: The Two-Variable Model
Chapter 7 – The Two-Variable Model: Hypothesis Testing
Chapter 8 – Multiple Regression: Estimation and Hypothesis Testing

9. Jorion, Value‐at‐Risk, 3rd Edition
Chapter 12- Monte Carlo Methods
10. John Hull, Options, Futures, and Other Derivatives, 7
th
Edition (New York: Pearson,
2009).
Chapter 21 – Estimating Volatilities and Correlations
11. Svetlozar Rachev, Christian Menn, and Frank Fabozzi, Fat‐Tailed and Skewed Asset
Return Distributions: Implications for Risk Management, Portfolio Selection and Option
Pricing (Hoboken, NJ: Wiley, 2005).
Chapter 2 – Discrete Probability Distributions
Chapter 3 – Continuous Probability Distributions
12. Linda Allen, Jacob Boudoukh and Anthony Saunders, Understanding Market, Credit and
Operational Risk: The Value at Risk Approach (Oxford: Blackwell Publishing, 2004).
Chapter 2 – Quantifying Volatility in VaR Models
AIM Statements, 2010 FRM Part I Page 10 of 39
2010 by Global Association of Risk Professionals, Inc.

Readings for Quantitative Analysis ‐ AIMS
Damodar Gujarati, Essentials of Econometrics, 3rd
Edition (New York: McGraw‐Hill, 2006).
Chapter 1 – The Nature and Scope of Econometrics
AIMS – Candidates, after completing this reading, should be able to:
Describe the methodology of econometrics.
Distinguish between the different types of data used for empirical analysis.
Describe the process of specifying, interpreting, and validity testing an econometric model.

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Damodar Gujarati, Essentials of Econometrics, 3rd
Edition (New York: McGraw‐Hill, 2006).
Chapter 2 – Review of Statistics: Probability and Probability Distributions
AIMS – Candidates, after completing this reading, should be able to:
Define random variables, and distinguish between continuous and discrete random
variables.
Define the probability of an event.
Describe the relative frequency or empirical definition of probability.
Define Bayes’ theorem and apply Bayes’ formula to determine the probability of an event.
Describe and interpret the probability mass function, probability density function, and
cumulative density function for a random variable.
Distinguish between univariate and multivariate probability density functions.
Describe marginal and conditional probability functions.
Explain the difference between statistical independence and statistical dependence.

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Damodar Gujarati, Essentials of Econometrics, 3rd
Edition (New York: McGraw‐Hill, 2006).
Chapter 3 – Characteristics of Probability Distributions
AIMS – Candidates, after completing this reading, should be able to:
Define, calculate and interpret the expected value of a random variable.
Define, calculate and interpret the variance of a random variable.
Define and apply Chebyshev’s inequality to determine the probability that a random
variable lies in a certain range.
Define, calculate and interpret the covariance and correlation of two random variables.
AIM Statements, 2010 FRM Part I Page 11 of 39
2010 by Global Association of Risk Professionals, Inc.

Define, calculate and interpret the mean and variance of a set of random variables.
Describe the difference between conditional and unconditional expectation.
Define, calculate and interpret the skewness and kurtosis of a random variable.
Describe and identify a platykurtic and leptokurtic distribution.
Define the skewness and kurtosis of a normally distributed random variable.
Distinguish between population and sample, and calculate the sample mean, variance,
covariance, correlation, skewness, and kurtosis.

Damodar Gujarati, Essentials of Econometrics, 3rd
Edition (New York: McGraw‐Hill, 2006).
Chapter 5 – Statistical Inference: Estimation and Hypothesis Testing
AIMS – Candidates, after completing this reading, should be able to:
Describe the concept of statistical inference, including estimation and hypothesis testing.
Define and distinguish an estimator and a parameter.
Define and distinguish between point estimate and interval estimation.
Define and interpret critical t-values.
Define, calculate and interpret a confidence interval.
Describe the properties of point estimators.
o Distinguish between unbiased and biased estimators.
o Define an efficient estimator and consistent estimator.
AIM Statements, 2010 FRM Part I Page 12 of 39
2010 by Global Association of Risk Professionals, Inc.

Explain and apply the process of hypothesis testing.
o Define and interpret the null hypothesis and the alternative hypothesis.
o Distinguish between one-sided and two-sided hypotheses.
o Describe the confidence interval approach to hypothesis testing.
o Describe the test of significance approach to hypothesis testing.
o Define, calculate and interpret type I and type II errors.
o Define and interpret the p value.
Describe and interpret the chi-squared test of significance and the F-test of significance.         

Damodar Gujarati, Essentials of Econometrics, 3rd
Edition (New York: McGraw‐Hill, 2006).
Chapter 6 – Basic Ideas of Linear Regression: The Two-Variable Model
AIMS – Candidates, after completing this reading, should be able to:
Explain how regression analysis in econometrics measures the relationship between
dependent and independent variables.
Define and interpret the results of a scattergram.
Define and interpret a population regression function, regression coefficients, parameters,
slope and the intercept.
Define and interpret the stochastic error term (or noise component).
Define and interpret a sample regression function, regression coefficients, parameters,
slope and the intercept.
Describe the key properties of a linear regression.
Distinguish between two-variable and multivariable regression.
Describe the method of ordinary least squares for estimation of parameters.
o Define and interpret the residual sum of squares.
o Interpret the results of an ordinary least squares regression.         

Damodar Gujarati, Essentials of Econometrics, 3rd
Edition (New York: McGraw‐Hill, 2006).
Chapter 7 – The Two-Variable Model: Hypothesis Testing
AIMS – Candidates, after completing this reading, should be able to:
Explain the assumptions of the classical linear regression model.
Define and distinguish homoscedasticity and heteroscedasticity.
Define, calculate and interpret the standard errors in an OLS model.
AIM Statements, 2010 FRM Part I Page 13 of 39
2010 by Global Association of Risk Professionals, Inc.

Define and interpret the residual sum of squares and the standard error of a regression.
Describe hypothesis testing in an OLS regression model.
Define, calculate and interpret the coefficient of determination and the coefficient of
correlation.
Describe and interpret normality testing using histograms and normal probability plots.
Describe and interpret the Jarque-Bera test of normality.
Describe forecasting, or prediction, error.