2012年云南省CFA二级考试试题一点通

试卷II：固定收益估值和分析衍生产品估值和分析组合管理问题最终考试2012年9月问题1：固定收益估值和分析(55分) 按照投资政策委员会规定，养老基金只能把资产投资于国内债券，但是可以自行决定投资于何种国内债券。

于是，作为一家养老基金的投资组合经理，你开始研究你的债券组合。

国内政府债券的市场价格如表1所示。

所有债券面值是100而且无交易单位的限制，付息刚刚结束。

表1政府债券息票（每年）到期期限（年）每100面值的价格A 0% 1 97.09B 4% 2 100.97C 5% 3 103.14D 5% 4 102.72E 0% 5 79.73注：a)在开始，你需要进行一些基本的计算。

a1)计算表2空格中第四年的数据。

写出计算过程，利率（用百分数表示）保留小数点后两位，贴现因子保留小数点后四位。

此处，远期利率是指对应期限的一年远期利率（从t-1期到t期）；贴现因子是对应期限的1单位面值的贴现债券的价格。

（8分）期限（年）平价利率当前利率远期利率贴现因子1 3.00% 3.00% 3.00% 0.97092 3.49% 3.50% 4.00% 0.93353 3.88% 3.90% 4.70% 0.891645 4.57% 4.63% 6.00% 0.7973a2)表2显示到期日越长，远期利率越高。

请解释这是否表明市场参与者预期未来的短期利率将增加，并就利率风险溢价发表看法。

（3分）b)接下来，你需要考虑如何对冲表3中的养老金负债的利率风险：表3：假设养老基金的融资率是100%。

“养老金融资率” 是“养老金资产价值/负债市场价格”。

b1)根据表2和表3计算负债的市场价值和修正久期，写出计算过程并保留小数点后两位数。

负债的修正久期是每个现金流修正久期的加权平均。

（5分）b2)目前，所有的资产都投资于政府贴现债券A。

如果利率变化，融资率也会发生变化。

为对冲此风险，你计划转移部分资产投资政府贴现债券E，以保证资产的修正久期等于负债的修正久期。

CFA二级模拟试题及解析Reading 7Correlation and RegressionThe following information relates to Questions 5–10Kenneth McCoin, CFA, is a fairly tough interviewer. Last year, he handed each job applicant a sheet of paper with the information in the following table, and he then asked several questions about regression analysis. Some of McCoin’s questions, along with a sample of the answers he received to each, are given below. McCoin told the applicants that the independent variable is the ratio of net income to sales for restaurants with a market cap of more than $100 million and the dependent variable is the ratio of cash flow from operations to sales for those restaurants. Which of the choices provided is the best answer to each of McCoin’s questions?RegressionStatisticsMultiple R0.8623R-squared0.7436Standard error0.0213Observations24ANOVA df SS MSS F Significance FRegression10.0290.02900063.810 Residual220.0100.000455Total230.040CoefficientsStandardErrort-Statisticp-ValueIntercept0.0770.00711.3280Slope0.8260.1037.9880What is the value of the coefficient of determination?0.8261.0.7436.0.8623.Suppose that you deleted several of the observations that had small residual values. If you re-estimated the regression equation using this reduced sample, what would likely happen to the standard error of the estimate and the R-squared?Standard Error of the Estimate R-SquaredA Decrease DecreaseB Decrease IncreaseC Increase Decreaseis the correlation between X and Y?−0.7436.0.7436.0.8623.Where did the F-value in the ANOVA table come from?You look up the F-value in a table. The F depends on the numerator and denominator degrees of freedom.Divide the “Mean Square” for the regression by the “Mean Square” of the residuals.The F-value is equal to the reciprocal of the t-value for the slope coefficient.If the ratio of net income to sales for a restaurant is 5 percent, what is the predicted ratio of cash flow from operations to sales?0.007 + 0.103(5.0) = 0.524.0.077 −0.826(5.0) = −4.054.0.077 + 0.826(5.0) = 4.207.Is the relationship between the ratio of cash flow to operations and the ratio of net income to sales significant at the 5 percent level? No, because the R-squared is greater than 0.05.No, because the p-values of the intercept and slope are less than 0.05.Yes, because the p-values for F and t for the slope coefficient are less than 0.05.The following information relates to Questions 11–16Howard Golub, CFA, is preparing to write a research report on Stellar Energy Corp. common stock. One of the world’s largest companies, Stellar is in the business of refining and marketing oil. As part of his analysis, Golub wants to evaluate the sensitivity of the stock’s returns to various economic factors. For example, a client recently asked Golub whether the price of Stellar Energy Corporation stock has tended to rise following increases in retail energy prices. Golub believes the association between the two variables to be negative, but he does not know the strength of the association.Golub directs his assistant, Jill Batten, to study the relationships between Stellar monthly common stock returns versus the previous month’s percent change in the US Consumer Price Index for Energy (CPIENG), and Stellar monthly common stock returns versus the previous month’s percent change in the US Producer Price Index for Crude Energy Materials (PPICEM). Golub wants Batten to run both a correlation and a linear regression analysis. In response, Batten compiles the summary statistics shown in Exhibit 1 for the 248 months between January 1980 and August 2000. All of the data are in decimal form, where 0.01 indicates a 1 percent return. Batten also runs a regression analysis using Stellar monthly returns as the dependent variable and the monthly change in CPIENG as the independent variable. Exhibit 2 displays the results of this regression model.Exhibit 1.Descriptive StatisticsMonthly Return Stellar Common StockLagged Monthly ChangeCPIENG PPICEMMean0.01230.00230.0042Standard0.07170.01600.0534 DeviationCovariance,−0.00017Stellar vs. CPIENGCovariance,−0.00048Stellar vs. PPICEMCovariance,0.00044CPIENG vs. PPICEMCorrelation,−0.1452Stellar vs. CPIENGExhibit 2.Regression Analysis with CPIENGRegression StatisticsMultiple R0.1452R-squared0.0211Standard error ofthe estimate0.0710Observations248CoefficientsStandardErrort-StatisticIntercept0.01380.0046 3.0275Slope coefficient−0.64860.2818−2.3014Batten wants to determine whether the sample correlation between the Stellar and CPIENG variables (−0.1452) is statistically significant. The critical value for the test statistic at the 0.05 level of significance is approximately 1.96. Batten should conclude that the statistical relationship between Stellar and CPIENG is: significant, because the calculated test statistic has a lower absolute value than the critical value for the test statistic.significant, because the calculated test statistic has a higher absolute value than the critical value for the test statistic.not significant, because the calculated test statistic has a higher absolute value than the critical value for the test statistic.Did Batten’s regression analyze cross-sectional or time-series data, and what was the expected value of the error term from that regression?Data Type Expected Value of Error TermA Time-series0B Time-seriesεiC Cross-sectionalBased on the regression, which used data in decimal form, if the CPIENG decreases by 1.0 percent, what is the expected return on Stellar common stock during the next period?0.0073 (0.73 percent).0.0138 (1.38 percent).0.0203 (2.03 percent).Based on Batten’s regression model, the coefficient of determination indicates that:Stellar’s returns explain 2.11 percent of the variability in CPIENG.Stellar’s returns explain 14.52 percent of the variability in CPIENG.Changes in CPIENG explain 2.11 percent of the variability in Stellar’s returns.For Batten’s regression model, the standard error of the estimate shows that the standard deviation of:the residuals from the regression is 0.0710.values estimated from the regression is 0.0710.Stellar’s observed common stock returns is 0.0710.For the analysis run by Batten, which of the following is an incorrect conclusion from the regression output?The estimated intercept coefficient from Batten’s regression is statistically significant at the 0.05 level.In the month after the CPIENG declines, Stellar’s common stock is expected to exhibit a positive return.Viewed in combination, the slope and intercept coefficients from Batten’s regression are not statistically significant at the 0.05 level.习题答案The following information relates to Questions 5–10Kenneth McCoin, CFA, is a fairly tough interviewer. Last year, he handed each job applicant a sheet of paper with the information in the following table, and he then asked several questions about regression analysis. Some of McCoin’s questions, along with a sample of the answers he received to each, are given below. McCoin told the applicants that the independent variable is the ratio of net income to sales for restaurants with a market cap of more than $100 million and the dependent variable is the ratio of cash flow from operations to sales for those restaurants. Which of the choices provided is the best answer to each of McCoin’s questions?RegressionStatisticsMultiple R0.8623R-squared0.7436Standard error0.0213Observations24ANOVA df SS MSS F Significance F Regression10.0290.02900063.810Residual220.0100.000455Total230.040CoefficientsStandardErrort-Statisticp-ValueIntercept0.0770.00711.3280 Slope0.8260.1037.9880What is the value of the coefficient of determination?0.8261.0.7436.0.8623.答案：B is correct. The coefficient of determination is the same as R-squared.解析：R2=0.7436,即为coefficient of determination。

CFA考试《CFA二级》历年真题精选及答案1122-371、In replying to Hextall’s recollection of the financial crisis, Klink most likely considered which risk measure?【单选题】A.VaRB.Scenario analysisC.Sensitivity analysis正确答案:B答案解析:B is correct. Scenario analysis is used for estimating how a portfolio might perform under conditions of market stress. Scenario risk measures estimate the portfolio returns that would result from a hypothetical change in markets. Stress tests and reverse stress tests are closely related to scenario risk measures. In addressing the possibility of direct exposure to extreme, negative events, Klink is describing a reverse stress test in which specific exposures of the portfolio (10 in this example) are identified. A hypothetical stress test (“reverse stress test”) is designed to measure its effect on each of these exposures.A is incorrect. VaR is used to measure the probability of a large loss. One limitation of VaR is its failure to take intoaccount illiquidity.C is incorrect. Sensitivity analysis is used to estimate how gains and losses in the portfolio change with changes in the underlying risk factors. For a short-term investment portfolio consisting entirely of short-duration, high-credit-quality fixed-income securities, there is likely little or no exposure to market sensitivity risk measures, such as beta, duration, convexity, delta, and gamma.2、Stephenson’s return objective and risk tolerance are most appropriately described as:【单选题】A.Return Objective: Below average; Risk Tolerance: Above average.B.Return Objective: Above average; Risk Tolerance: Below average.C.Return Objective: Above average; Risk Tolerance: Above average.正确答案:C答案解析:C is correct.Risk: Stephenson has an above-average risk tolerance based on both his ability and willingness to assume risk. His large asset base, long time horizon, ample income to cover expenses, and lack of need for liquidity or cash flow indicate an above-average ability to assume risk. His concentration in US small-capitalization stocks and his desire forhigh returns indicate substantial willingness to assume risk.Return: Stephenson’s financial circumstances (long time horizon, sizable asset base, ample income, and low liquidity needs) and his risk tolerance warrant an above-average total return objective. His expressed desire for a continued return of 20 percent, however, is unrealistic. Coppa should counsel Stephenson on what level of returns to reasonably expect from the financial markets over long periods of time and to define an achievable return objective.3、Stephenson’s time horizon is best characterized as:【单选题】A.short-term and single-stage.B.long-term and single-stage.C.long-term and multistage.正确答案:C答案解析:C is correct. Stephenson’s time horizon is long—he is currently only 55 years old. The time horizon consists of two stages: the first stage extends to his retirement in 15 years; the second stage may last for 20 years or more and extends from retirement until his death.4、Is Quek’s response to Yusuf most likely correct?【单选题】A.Yes.B.No, she is incorrect regarding the number of factors.C.No, she is incorrect regarding the identity of the factors.正确答案:B答案解析:B is correct. Quek is incorrect in stating that APT specifies the number of factors in a multifactor model but is correct in stating that APT does not specify the identity of factors in a multifactor model. APT does not indicate the number of factors or their identity.A is incorrect. Quek is incorrect in stating that APT specifies the number of factors in a multifactor model but correct in stating that APT does not specify the identity of factors in a multifactor model. APT does not indicate the number of factors or their identity.C is incorrect. Quek is correct in stating that APT does not specify the identity of factors in a multifactor model. APT does not indicate the number of factors or their identity.5、Is Hextall’s statement regarding the private wealth division likely correct?【单选题】A.Yes.B.No, it is incorrect about forward-looking beta.C.No, it is incorrect about ex ante tracking error.正确答案:A答案解析:A is correct. Hextall’s statement is correct. Risk measures for banks are typically focused on liquidity, solvency,and capital sufficiency, whereas risk measures for traditional asset managers are typically focused on investment performance. Ex ante tracking error correctly compares the current portfolio with its benchmark in attempting to measure future potential performance. Forward-looking beta is a current risk measure of a current portfolio and measures an equity portfolio’s sensitivity to the broad equity market.B is incorrect. Hextall’s statement about forward-looking beta is correct.C is incorrect. Hextall’s statement about ex ante tracking error is correct.。

Quant (1 Item Sets)•Simple linear regression with one independent variable (the basics)•Multiple regression with more than one independent variable (basics again plus model misspecification and problems with regression)•Time series analysis (observations over successive time periods; e.g. monthly stock returns, quarterly revenue data)Focus1.Simple linear regression1)计算：Covariance, correlation coefficient；并解释r的取值范围2)correlation coefficient等于0的假设检验，在给定置信度的情况下，判断接受还是拒绝原假设3)计算：估计标准误、回归系数、以及回归的置信区间4)使用H0和Ha来判断回归系数是否可靠5)计算：在回归模型已知的情况下模型的因变量 & 因变量的置信区间6)学会使用AVOVA分析，计算F统计量2.Multiple regression1)多元回归的显著性检验a)多元回归模型的假设b)P-Valuec)回归系数的t-test统计量d)判断在一定的置信度和单双尾的条件下，确定是否应该接受原假设e)计算：多元回归系数的置信区间、预测的因变量值f) F 统计量在回归分析中使用2)计算并区分：多元回归中R2和调整后的R23)ANOVA表格：也许会出现计算4)dummy variable，要会解释5)多元回归的问题（每个细节都需要把握）a)异方差性i.带来的影响ii.什么检验iii.出现异方差容易犯第几类错误iv.如何调整b)序列相关性i.带来的影响ii.什么检验iii.出现序列相关性容易犯第几类错误iv.如何调整c)多重共线性i.对回归分析的影响ii.什么检验iii.出现多重共线性容易犯第几类错误iv.如何调整d)知道模型不确定性对回归分析结果的影响，怎么样可以避免这些模型不确定性3.Time-Series Analysis1)知道将时间序列转化为线性趋势或log-linear trend模型2)解释自回归性，计算往前一期或两期的预测3)用残差的自相关性来检验是否自回归性模型适合该时间序列4)mean reversion，计算mean-reverting level5)In-sample forecasts & Out-sample forecasts，基于均方根误差下，比较不同时间序列模型的准确性6)Covariance Stationarity & Mean Reversion7)Random Walk & Random Walk with a Drift，并比较随机游走和协方差平稳过程的差异8)Unit Root Definition，一般什么时候会出现，如何对单位根进行测试9)Unit Root Test for Nonstationarity，有单位根的时间序列可以被转化成Autoregressive (AR)model10)First Differencing11)知道如何检验和修正季节性时间序列模型。