Gfedu slides quantitative

www.gfedu.net Quantitative Methods 2017CFA二级培训项目 讲师：周琪 1-90 www.gfedu.net 周琪 工作职称：金程教育金融研究院CFA/FRM高级培训师 教育背景：中央财经大学国际经济学学士、澳大利亚维多利亚大学金融风 险管理学学士 工作背景：学术功底深厚、培训经验丰富，曾任课AFP、CFP多年，参与教 学研究及授课，现为金程教育CFA/FRM双证培训老师，担任CFA项目学术 研发负责人，对CFA教学产品的研发工作负责，曾亲自参与中国工商银行总 行、中国银行总行、杭州联合银行等CFA、FRM培训项目。累计课时达400 0小时，课程清晰易懂，深受学员欢迎。 服务客户：中国工商银行、中国银行、中国建设银行、杭州联合银行、杭 州银行、国泰君安证券、深圳综合开发研究院、中国CFP标准委员会、太平 洋保险等 主编出版：参与金程CFA项目参考书目的编写工作，包括金程CFA一级中文 Notes等 2-90 www.gfedu.net Topic Weightings in CFA Level II Content Session NO Weightings Study Session 1-2 Ethics & Professional Standards 10-15 Study Session Quantitative Methods 5-10 Study Session Economic Analysis 5-10 Study Session 5-6 Financial Statement Analysis 15-20 Study Session 7-8 Corporate Finance 5-15 Study Session 9-11 Equity Analysis 15-25 Study Session 12-13 Fixed Income Analysis 10-20 Study Session 14 Derivative Investments 5-15 Study Session 15 Alternative Investments 5-10 Study Session 16-17 Portfolio Management 5-10 3-90 www.gfedu.net  SS3 Quantitative Methods for Valuation Framework • R9 Correlation and regression Quantitative Methods • R10 Multiple regression and issues in regression analysis • R11 Time-series analysis • R12 Excerpt from ’’Probabilistic Approaches: Scenario Analysis, Decision Trees, and Simulation’’ 4-90 www.gfedu.net Reading Correlation and regression 5-90 www.gfedu.net Framework Scatter Plots Covariance and Correlation Interpretations of Correlation Coefficients Significance Test of the Correlation Limitations to Correlation Analysis The Basics of Simple Linear Regression Interpretation of regression coefficients Standard Error of Estimate & Coefficient of Determination (R2) Analysis of Variance (ANOVA) 10 Regression coefficient confidence interval 11 Hypothesis Testing about the Regression Coefficient 12 Predicted Value of the Dependent Variable 13 Limitations of Regression Analysis 6-90 www.gfedu.net Scatter Plots  A scatter plots is a graph that shows the relationship between the observations for two data series in two dimensions 7-90 www.gfedu.net Sample Covariance and Correlation  Covariance:  Covariance measures how one random variable moves with another random variable It captures the linear relationship  Cov( X , Y )  n (X i 1 i  X )(Yi  Y ) /( n  1)  Covariance ranges from negative infinity to positive infinity Cov( X , Y )  Correlation: r  sx s y  Correlation measures the linear relationship between two random variables  Correlation has no units, ranges from –1 to +1 8-90 www.gfedu.net Interpretations of Correlation Coefficients  The correlation coefficient is a measure of linear association  It is a simple number with no unit of measurement attached, so the correlation coefficient is much easier to explain than the covariance Correlation coefficient Interpretation r = +1 perfect positive correlation < r < +1 positive linear correlation r=0 no linear correlation −1 < r < negative linear correlation r = −1 perfect negative correlation 9-90 www.gfedu.net Interpretations of Correlation Coefficients 10-90 www.gfedu.net Unit root test  The unit root test of nonstationarity  The time series is said to have a unit root if the lag coefficient is equal to one  A common t-test of the hypothesis that b1=1 is invalid to test the unit root, however, it is not often the case  Dickey-Fuller test (DF test) to test the unit root  Start with an AR(1) model xt=b0+b1 xt-1+εt Subtract xt-1 from both sides xt-xt-1 =b0+(b1 –1) xt-1+εt xt-xt-1 =b0+g xt-1+εt  H0: g=0 (has a unit root and is nonstationary) not have a unit root and is stationary) Ha: g