金融数据分析-教学大纲

1、金融数据分析教学大纲The Course Outline of Financial Data Analysis课程编号：151222B课程类型：专业选修课总学时：32 讲课学时：16 实验(上机)学时：16学 分：2适用对象：金融学(金融经济实验班)先修课程：计量经济学、微观经济学、宏观经济学、概率论与数 理统计、线性代数、微积分Course Code:Course Type: Discipline Elective CoursePeriods: 32 Lecture: 16 Experiment (Computer): 16 Credits: 2Applicable Subjects: Fi

2、nance (Finance and Economics Experiment Class)Prerequisite Courses: Econometrics, Microeconomics, Macroeconomics, Probability and Statistics, Linear Algebra, Calculus一、课程简介本课程是面向金融学、经济学和管理学相关专业的高年级本科生开设的学科 专业选修课程，主要介绍应用于金融数据分析中的经典计量方法，并注重培养学生的实际操作能力。Financial data analysis is an elective course for

3、advanced undergraduate students majored in finance, economics and management. In this course, we not only introduce basic econometric methods applied to financial data, but also train students practical skills of handling financial data.二、教学目标本课程包括以下教学目标：（1）使学生能运用所学的金融计量理论分析和 解释实际金融数据；（2）着重为对金融理论进行实

4、证分析提供所需的估计和检 验方法；（3）使学生运用计算机软件（以 R语言为主）进行金融数据研究。This course has the following teaching goals: （1） equipping students with econometric tools to analyze and explain financial data, （2） focusing on the estimation and inferential methods used in empirical analysis of finance theory, and （3） enabling stu

5、dents to implement these techniques using computer software, primarily R programming language.二、教学基本要求这门课程主要讲授如何利用海量金融数据对金融理论进行实证分析，以及在处理实际金融数据时所需的计量方法和计算机技术。因此，在教学内容的讲授过 程中，授课老师需要做到理论与实践并重。理论学习包括金融数据概述，平稳时 间序列分析，非平稳性时间序列分析，波动率分析，股票回报率预测，等。在时 间允许的情况下，我们还将介绍，多维向量自回归模型，以及协整和误差纠正模型。对于上述所有的模型的分析，本课程提供相应

6、的实际金融数据，以及完整的R程序与讲解说明文件。本课程教学方法将以课堂讲授为主。由于课程内容难度相对较大，我们鼓励 学生课前预习，课上积极参与讨论，并课后复习和独立完成作业。R语言的学习以课堂展示和学生上机实际操作的方式完成。课程考核由考勤、作业、研究项目、和期末考试组成。考核成绩为百分制， 各项分数分配见表（一）。出勤作业出勤作业10%30%30%30%期末闭卷考试30%30%研究项目The main content of this course is to explain how to handle a huge amount of financial data and to conduc

7、t empirical analysis based on financial theories. Thus, this course will weight equally on both learning econometric theories and real applications. Theoretical study will mainly cover such topics as introduction to financial data, stationary time series analysis, non-stationary time series analysis

8、, volatility analysis, and stock return prediction. If time permits, we will also learn vector autoregressive models (VAR) and cointegration and error correction models. All the models are accompanied with real-data examples with complete R programs and tutorials.The basic teaching strategy of this

9、course mainly involves in-class lectures. Due to the difficulty of the course, we encourage students to browse the assigned reading materials before class, to proactively participate discussionsin class, and to peruse lecture notes and independently complete homework after class. Students will learn

10、 R through in-class instructions and practice.The methods of the course evaluation include attendance, homework, project work, and final exam. The grade distribution of each component within one hundred percentage points is presented in Table 1Table 1: The grade distributionAttendance10%Assignments3

11、0%Final Exam (closed) 30%Project Work30%、各教学环节学时分配教学课时分配 (Class Schedule)厅P京P内谷讲课实验其他合计1金融数据概述The Characteristics of Financial Market Data2242平稳时间序列Stationary Time Series3363非平稳时间序列Nonstationary Time Series3364波动率Volatility3365股票回报率预测Stock Return Predictability2246多元时间序列建模Multivariate Time Series336

12、合计161632四、教学内容第一章金融市场数据的特征第一节数据：金融数据的实证特征对金融建模很重要第二节概括性的统计量第三节分位数相关的分析.在险价值.期望损失.密度预测第四节实证分析.有效市场假说.方差比检验教学重点、难点：在险价值和期望损失的计算课程的考核要求：计算在险价值和期望损失，用计量软件检验有效市场假 说第二章平稳时间序列第一节ARMA过程.数据.模型性质第二节估计，检验和模型选择MLE估计方法统计量ARMA模型选择第三节预测.预测过程.预测评估第四节实证分析教学重点、难点：ARMA过程模型性质和预测课程的考核要求：分析ARMA过程模型性质和预测，用计量软件预测 ARMA过程第三章

13、非平稳时间序列第一节单位根过程和确定性时间序列.数据.单位根过程的模型性质.含有确定性时间序列模型的性质第二节估计，检验和模型选择. MLE估计方法.单位根检验.滞后阶数的选择第三节预测.预测过程.预测评估第四节实证分析.泡沫检验.泡沫起始时间的估计教学重点、难点：单位根检验和泡沫检验课程的考核要求：分析单位根过程的模型性质，用计量软件进行单位根检第四章波动率第一节资产价格过程的波动聚类第二节自回归条件异方差模型（ARCH）.模型性质.估计方法.模型选择第三节广义自回归条件异方差模型（GARCH ）.模型性质.估计方法.模型选择第四节实证分析教学重点、难点：ARCH和GARCH模型的估计课程的

14、考核要求：用ARCH和GARCH模型来分析现实金融数据的波动性第五章股票回报率预测第一节股票回报率预测（i）.预测回归.长期预测回归第二节股票回报率预测（ii）.资本资产定价模型.多因子模型第三节实证分析教学重点、难点：预测回归和多因子模型课程的考核要求：用计量软件检验股票收益的可预测性第六章 多元时间序列建模第一节向量自回归过程.数据.向量自回归过程的模型性质.估计，检验和模型选择.脉冲响应函数分析.预测方差分解第二节伪回归和协整模型.伪回归模型.协整模型第三节向量误差修正模型.模型性质.模型估计教学重点、难点：向量自回归过程的模型性质和协整关系检验课程的考核要求：分析向量自回归过程的模型性

15、质，用计量软件进行协整关系检验The Characteristics of Financial Market DataThe Data: The empirical characteristics of financial data are important for building financial models.Summary StatisticsPercentiles related statisticsValue-at-RiskExpected ShortfallDensity Forecasting EstimationEmpirical AnalysisEffcient Mar

16、ket HypothesisVariance Ratio TestsKey and Difficult Points: calculation of Value-at-Risk and Expected ShortfallEvaluation Requirements: calculation of Value-at-Risk and Expected Shortfall, and testing the Effcient Market Hypothesis by econometric softwareChapter 2 Stationary Time SeriesARMA processe

17、sDataPropertiesEstimation, Inference and Model SelectionEstimation by MLETest StatisticsARMA Model SelectionForecastingForecasting ProcedureForecasting EvaluationEmpirical AnalysisKey and Difficult Points: the properties of ARMA process and forecasting using ARMA modelEvaluation Requirements: the pr

18、operties ofARMA process, and forecasting using ARMA model by econometric softwareNonstationary Time SeriesUnit Root and Deterministic TrendsDataProperties of Unit Root ModelProperties of Model with Deterministic TrendsEstimation, Inference and Model SelectionEstimation by MLEUnit Root TestLag-length

19、 Model SelectionForecastingForecasting ProcedureForecasting EvaluationEmpirical AnalysisBubble TestData Stamping of BubblesKey and Difficult Points: Unit root test and Data Stamping of BubblesEvaluation Requirements: Properties of Unit Root Model, and test unit root by econometric softwareVolatility

20、Volatility Clustering of Asset Price ProcessesAutoregressive Conditional Heteroscedasticity Mode l( ARCH)PropertiesEstimationModel SelectionGeneralized Autoregressive Conditional Heteroscedasticity Model (GARCH)PropertiesEstimationModel SelectionEmpirical AnalysisKey and Difficult Points: estimation

21、 of ARCH and GARCH modelsEvaluation Requirements: apply ARCH and GARCH models to real data in financeStock Return PredictabilityStock Return Predictability (i)Predictive RegressionLong-horizon Predictive RegressionStock Return Predictability (ii)Capital Asset Pricing Model (CAPM)Multifactor ModelsEmpirical AnalysisKey and Difficult Points: Predictive Regression and Multifactor Models Evaluation Requirements: Test stock return predictability by Predictive Regression using econometric softwareMultivariate Time SeriesVector Autoregressive Models10DataProperties of Vector Auto