《金融計量學》教學大綱“FinancialEconometrics”CourseOutline課程編號：151163A課程類型：專業選修課總學時：48講課學時：32實驗（上機）學時：16學分：3適用對象：金融學（數據與計量分析）先修課程：微觀經濟學、宏觀經濟學、概率論與數理統計、線性代數、微積分CourseCode:151163APeriods:48Lecture:32Experiment(Computer):16Credits:3ApplicableSubjects:：FinancePreparatoryCourses:Microeconomics,Macroeconomics,ProbabilityandMathematicalStatistics,LinearAlgebra,MathematicalAnalysis一、課程的教學目標這是一門向金融學、經濟學和管理學相關專業的高年級本科生開設的學科基礎課程。這門課程在學生的知識結構中占有重要位置，是訓練學生研究能力和分析能力的重要課程之一。旨在引領學生從政治認同、國家意識、文化自信等方面提升金融素養，發揮金融專業的引航作用。在這門課中，我們主要介紹應用于金融經濟學中各種經典計量分析方法，包括以下教學目標：（1）為金融計量分析中其它相關課程提供所需要的基本概率知識；（2）著重于現代金融理論進行實證分析所需要的估計和檢驗方法；（3）學生能運用所學的金融計量理論分析和解釋實際金融數據；（4）學生能在計算機上用計量軟件EViews或STATA實踐計量方法。學完這門課程后，學生能熟練運用各種經典的金融計量分析方法和軟件來分析金融數據，包括金融變量間的建模，運用計量軟件來檢驗、預測、理解和模擬金融數據，并擁有了解新的分析方法的工具。最后，通過金融計量學理論加實踐的學習，培養學生金融分析能力，樹立正確的世界觀和社會主義價值觀。FinancialEconometricsisabasicmajorcourseforadvancedundergraduatestudentsmajoringinfinance,economicsandmanagement.Thiscourseplaysavitalroleinstudents’structureofknowledge,andisanimportanttrainingcoursefordevelopingstudents’researchandanalysisskills.Thiscourseaimstoleadstudentstoimprovetheirfinancialliteracyintermsofpoliticalidentity,nationalawareness,andculturalself-confidence,takingasaleadingroleintheareaoffinance.Throughthesystematicstudyoffinancialeconometrics,studentswillcultivatetheirfinancialanalysisabilityandestablishacorrectworldoutlookandsocialistvaluation.Inthiscourse,weintroducebasiceconometricmethodsappliedinfinancialeconomics,includingthefollowingaims:(1)providesbackgroundinprobabilitytheoryforothercoursesintheFinancialEconometrics;(2)focusesontheestimationandinferentialmethodsusedinempiricalanalysisofmodernfinancetheory;(3)studentsareabletoapplythemethodstaughtinFinancialEconometricstoanalyzeandexplainthefinancialdata;(4)studentsareabletoimplementthesetechniquesusingeconometricsoftwarepackagesEViewsorSTATA.Withsuccessfulcompletionofthiscourse,studentsshouldbeabletoapplyallsortsofempiricalmethodsinFinancialEconometricstoquantitativelyanalyzeandinterpretthefinancialdata,includingmodellingtherelationshipbetweenmultivariatevariables,usingeconometricsoftwaretotest,predict,understandandsimulatethefinancialdata,andhavingthetoolstoreadtextswhichintroducenewmethods.二、教學基本要求這門課程主要闡述的是關于金融理論的實證分析，以及如何分析實際金融數據所需要的計量理論和實證技術。因此，在教學內容的講授過程中，授課老師需要做到理論與實踐并重。這門課從金融數據的概述開始，包括一些基本的分析統計量，分位數相關的分析比如風險的計算以及有效市場的檢驗。之后，我們開始分析數據的平穩性與非平穩性。基于此基本性質，我們進而分析股票票回報率、金融市場泡沫的存在性。于此同時，我們用ARCH和GARCH模型分析市場的波動效應。我們還特別關注多維變量的建模和分析。基于多維向量自回歸模型，我們繼而學習協整模型和向量誤差修正模型。于此同時，我們對相關的脈沖響應函數以及預測方差分解給予詳細介紹。對于上述所有的模型的分析，該課程都提供相應的實證數據例子以及標準的計量分析程序。由于教學理論與實踐并重，授課老師應采取理論教授與上機實踐緊密結合的教學方法。鼓勵學生課前預習；課上安排課堂討論，提高學生的課堂參與積極性，以便學生能夠深入理解知識要點；課后指定學生進行經典論文的選讀，指導學生撰寫小論文并組織課堂宣講。課程的考核方式及其所占權重如下：出勤10%作業30%期末閉卷考試30%課程論文30% 在上述考核方式中，作業來自于授課相關知識的理論和實踐習題；期末閉卷考試考查計量理論知識；課程論文考查計量應用實踐。Themaincontentofthiscourseiselaboratingtheeconometrictheoryandempiricaltechniqueswhicharemostlyusedintheempiricalanalysisoffinancialtheoryandhowtheyareappliedtoactualfinancialdata.Therefore,inthiscourse,theinstructorshouldequalweightsonboththeoryandapplication.Thecoursestartswiththeoverviewoffinancialdata.Itcoverssomestatistics,percentilerelatedanalysissuchasriskcomputation,andcontinueswithtestingefficientmarketmodels.Wethenproceedtoanalyzingthestationarityandnon-stationarityofdata,withapplicationonreturnpredictabilityandbubbleexistence.WecontinuewithvolatilityeffectsofthemarketdatausingARCHandGARCHmodels.Aspecialattentionispaidtomultivariatemodels.BasedonbasicVectorAutoregressivemodels,wecovercointegrationandVECMmodel,othermoreadvancedtechniqueslikeimpulseresponseanalysisandforecasterrordecompositionareincludedaswell.Allthemodelsareaccompaniedwithreal-dataexamplesinstandardcomputerpackages.Becauseoftheequalimportanceoftheoryandapplication,instructorshouldadopttheteachingstrategyofcloseintegrationoflecturingintheoryandpracticingoncomputer.Encouragestudenttopreviewcoursematerialsbeforeclass,arrangediscussionsduringclass,enhancestudent’senthusiasmandinitiativeinclassparticipationsothatstudentcanunderstandthosekeypointsofknowledgedeeply;assignstudenttoreadclassicalacademicpapersselectively,guidestudenttowritetermpaperandorganizepresentationsinclass.Themethodsofevaluationofthiscourseandtheirweightsareasfollows:Attendance10%Assignments30%FinalExam(closed)30%Termpaper30% Inthemethodsofevaluationabove,theassignmentscomefromthetheoreticalandapplicationexercisesfromcourserelatedmaterial;theclosedformfinalexamfocusesontheexaminationofknowledgeintheory;andthetermpaperfocusesontheexaminationofapplication.三、各教學環節學時分配教學課時分配(ClassSchedule)序號章節內容講課實驗其他合計金融數據概述TheCharacteristicsofFinancialMarketData44平穩時間序列StationaryTimeSeries628非平穩時間序列NonstationaryTimeSeries628股票回報率預測StockReturnPredictability426多元時間序列建模MultivariateTimeSeries10414波動率Volatility628合計361248四、教學內容第一章金融市場數據的特征數據：金融數據的實證特征對金融建模很重要概括性的統計量分位數相關的分析在險價值期望損失密度預測實證分析有效市場假說方差比檢驗教學重點、難點：在險價值和期望損失的計算課程的考核要求：計算在險價值和期望損失，用計量軟件檢驗有效市場假說第二章平穩時間序列ARMA過程數據模型性質估計，檢驗和模型選擇MLE估計方法統計量ARMA模型選擇預測預測過程預測評估實證分析教學重點、難點：ARMA過程模型性質和預測課程的考核要求：分析ARMA過程模型性質和預測，用計量軟件預測ARMA過程第三章非平穩時間序列單位根過程和確定性時間序列數據單位根過程的模型性質含有確定性時間序列模型的性質估計，檢驗和模型選擇MLE估計方法單位根檢驗滯后階數的選擇預測預測過程預測評估實證分析泡沫檢驗泡沫起始時間的估計教學重點、難點：單位根檢驗和泡沫檢驗課程的考核要求：分析單位根過程的模型性質，用計量軟件進行單位根檢驗第四章多元時間序列建模向量自回歸過程數據向量自回歸過程的模型性質估計，檢驗和模型選擇脈沖響應函數分析預測方差分解偽回歸和協整模型偽回歸模型協整模型向量誤差修正模型模型性質模型估計預測預測過程預測評估實證分析泡沫檢驗泡沫起始時間的估計教學重點、難點：向量自回歸過程的模型性質和協整關系檢驗課程的考核要求：分析向量自回歸過程的模型性質，用計量軟件進行協整關系檢驗第五章股票回報率預測股票回報率預測（i）預測回歸長期預測回歸股票回報率預測（ii）資本資產定價模型多因子模型實證分析教學重點、難點：預測回歸和多因子模型課程的考核要求：用計量軟件檢驗股票收益的可預測性第六章波動率資產價格過程的波動聚類自回歸條件異方差模型（ARCH）模型性質估計方法模型選擇廣義自回歸條件異方差模型（GARCH）模型性質估計方法模型選擇實證分析教學重點、難點：ARCH和GARCH模型的估計課程的考核要求：用ARCH和GARCH模型來分析現實金融數據的波動性Chapter1TheCharacteristicsofFinancialMarketDataSection1TheData:Theempiricalcharacteristicsoffinancialdataareimportantforbuildingfinancialmodels.Section2SummaryStatisticsSection3PercentilesrelatedstatisticsValue-at-RiskExpectedShortfallDensityForecastingEstimationSection4EmpiricalAnalysisEffcientMarketHypothesisVarianceRatioTestsKeyandDifficultPoints:calculationofValue-at-RiskandExpectedShortfallEvaluationRequirements:calculationofValue-at-RiskandExpectedShortfall,andtestingtheEffcientMarketHypothesisbyeconometricsoftwareChapter2StationaryTimeSeriesSection1ARMAprocessesDataPropertiesSection2Estimation,InferenceandModelSelectionEstimationbyMLETestStatisticsARMAModelSelectionSection3ForecastingForecastingProcedureForecastingEvaluationSection4EmpiricalAnalysisKeyandDifficultPoints:thepropertiesofARMAprocessandforecastingusingARMAmodelEvaluationRequirements:thepropertiesofARMAprocess,andforecastingusingARMAmodelbyeconometricsoftwareChapter3NonstationaryTimeSeriesSection1UnitRootandDeterministicTrendsDataPropertiesofUnitRootModelPropertiesofModelwithDeterministicTrendsSection2Estimation,InferenceandModelSelectionEstimationbyMLEUnitRootTestLag-lengthModelSelectionSection3ForecastingForecastingProcedureForecastingEvaluationSection4EmpiricalAnalysisBubbleTestDataStampingofBubblesKeyandDifficultPoints:UnitroottestandDataStampingofBubblesEvaluationRequirements:PropertiesofUnitRootModel,andtestunitrootbyeconometricsoftwareChapter4MultivariateTimeSeriesSection1VectorAutoregressiveModelsDataPropertiesofVectorAutoregressiveModelsEstimation,InferenceandModelSelectionImpulseResponseAnalysisForecasterrorvariancedecompositionsSection2SpuriousRegressionandCointegrationSpuriousRegressionCointegrationSection3VectorErrorCorrectionModelPropertiesEstimationSection4ForecastingForecastingProcedureForecastingEvaluationSection5EmpiricalAnalysisKeyandDifficultPoints:VectorAutoregressiveModelsandCointegrationEvaluationRequirements:PropertiesofVectorAutoregressiveModelsandCointegrationtestbyeconometricsoftwareChapter5StockReturnPredictabilitySection1StockReturnPredictability(i)PredictiveRegressionLong-horizonPredictiveRegressionSection2StockReturnPredictability(ii)CapitalAssetPricingModel(CAPM)MultifactorModelsSection3EmpiricalAnalysisKeyandDifficultPoints:PredictiveRegressionandMultifactorModelsEvaluationRequirements:TeststockreturnpredictabilitybyPredictiveRegressionusingeconometricsoftwareChapter6VolatilitySection1Volatility