FRM真题练习对于考生重要吗？

发布时间：2021-08-31 09:11编辑：融跃教育FRM

FRM真题练习对于考生重要吗？这当然是重要的，尤其是备考冲刺阶段，考生一定要做大量的真题练习。下面是小编列举的相关真题，希望对备考的你有所帮助~

New copula correlation models were used by traders and risk managers during the 2007-2009 global financial crisis. This led to miscalculations in the underlying risk for structured products such as collateralized debt obligation (CDO) models. Which of the following statements least likely explains the failure of these new copula correlation models during the financial crisis?》》》戳：免费领取FRM各科视频讲义+历年真题+21年原版书（PDF版）

A) The copula correlation models assumed a negative correlation between the equity and senior tranches of CDOs.

B) Correlations for equity tranches of CDOs increased during the financial crisis.

C) The correlation copula models were calibrated with data from time periods that had low risk.

D) Correlations for senior tranches of CDOs decreased during the financial crisis.

答案：D

解析：During the crisis, the correlations for both the equity and senior tranches of CDOs significantly increased causing losses in value for both. 【资料下载】[融跃财经]FRM一级ya题-pdf版

Which of the following statements about correlation and copula are correct?

I. Copula enables the structures of correlation between variables to be calculated separately from their marginal distributions.

II. Transformation of variables does not change their correlation structure.

III. Correlation can be a useful measure of the relationship between variables drawn from a distribution without a defined variance.

IV. Correlation is a good measure of dependence when the measured variables are distributed as multivariate elliptical.

A) I and IV only.

B) II, III, and IV only.

C) I and III only.

D) II and IV only.

答案：A

解析：I is true. Using the copula approach, we can calculate the structures of correlation between variables separately from the marginal distributions. IV is also true. Correlation is a good measure of dependence when the measured variables are distributed as multivariate elliptical. II is false. The correlation between transformed variables will not always be the same as the correlation between those same variables before transformation. Data transformation can sometimes alter the correlation estimate. III is also false. Correlation is not defined unless variances are finite.

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