用S-Plus做金融数据统计分析

用S-Plus做金融数据统计分析

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作者: [美] 卡莫纳著

出版社: 世界图书出版公司

出版时间: 2010-09

版次: 1

ISBN: 9787510027451

定价: 79.00

装帧: 平装

开本: 16开

纸张: 胶版纸

页数: 451页

正文语种: 英语

分类: 计算机与互联网

1 张插图图片

5人买过

Thisbookgrewoutoflecturesnoteswrittenforaone-semesterjuniorstatisticscourseofferedtotheundergraduatestudentsmajoringintheDepartmentofOper-ationsResearchandFinancialEngineeringatPrincetonUniversity.Tidbitsofthehistoryofthiscoursewillshedlightonthenatureandspiritofthebook.
Thepurposeofthecourseistointroducethestudentstomodemdataanalysiswithanemphasisonadomainofapplicationthatisofinteresttomostofthem：financialengineering.Theprerequisitesforthiscourseareminimal，howeveritisfairtosaythatallofthestudentshavealreadytakenabasicintroductorystatisticscourse.Thustheelementarynotionsofrandomvariables，expectationandcorrelationaretakenforgranted，andearlierexposuretostatisticalinference（estimation，testsandconfidenceintervals）isassumed.Itisalsoexpectedthatthestudentsarefamiliarwithaminimumoflinearalgebraaswellasvectorandmatrixcalculus. PartⅠDATAEXPLORATION，ESTIMATIONANDSIMULATION
UNIVARIATEEXPLORATORYDATAANALYSIS
1.1Data，RandomVariablesandTheirDistributions
1.1.1ThePCSData
1.1.2TheS&P500IndexandFinancialReturns
1.1.3RandomVariablesandTheirDistributions
1.1.4ExamplesofProbabilityDistributionFamilies.，
1.2FirstExploratoryDataAnalysisTools
1.2.1RandomSamples
1.2.2Histograms
1.3MoreNonparametricDensityEstimation
1.3.1KernelDensityEstimation
1.3.2ComparisonwiththeHistogram
1.3.3S&PDailyReturns
1.3.4ImportanceoftheChoiceoftheBandwidth
1.4QuantilesandQ-QPlots
1.4.1UnderstandingtheMeaningofQ-QPlots
1.4.2ValueatRiskandExpectedShortfall
1.5EstimationfromEmpiricalData
1.5.1TheEmpiricalDistributionFunction
1.5.2OrderStatistics
1.5.3EmpiricalQ-QPlots
1.6RandomGeneratorsandMonteCarloSamples
1.7ExtremesandHeavyTailDistributions
1.7.1S&PDailyReturns，OnceMore
1.7.2TheExampleofthePCSIndex
1.7.3TheExampleoftheWeeklyS&PReturns
Problems
Notes&Complements
2MULTIVARIATEDATAEXPLORATION
2.1MultivariateDataandFirstMeasureofDependence
2.1.1DensityEstimation
2.1.2TheCorrelationCoefficient
2.2TheMultivariateNormalDistribution
2.2.1SimulationofRandomSamples
2.2.2TheBivariateCase
2.2.3ASimulationExample
2.2.4LetsHaveSomeCoffee
2.2.5IstheJointDistributionNormal？
2.3MarginalsandMoreMeasuresofDependence
2.3.1EstimationoftheCoffeeLog-ReturnDistributions
2.3.2MoreMeasuresofDependence
2.4CopulasandRandomSimulations
2.4.1Copulas
2.4.2FirstExamplesofCopulaFamilies
2.4.3CopulasandGeneralBivariateDistributions
2.4.4FittingCopulas
2.4.5MonteCarloSimulationswithCopulas
2.4.6ARiskManagementExample
2.5PrincipalComponentAnalysis
2.5.1IdentificationofthePrincipalComponentsofaDataSet
2.5.2PCAwithS-Plus
2.5.3EffectiveDimensionoftheSpaceofYieldCurves
2.5.4SwapRateCurves
Appendix1：CalculuswithRandomVectorsandMatrices
Appendix2：FamiliesofCopulas
Problems
Notes&Complements

PartⅡREGRESSION
3PARAMETRICREGRESSION
3.1SimpleLinearRegression
3.1.1GettingtheData
3.1.2FirstPlots
3.1.3RegressionSet-up
3.1.4SimpleLinearRegression
3.1.5CostMinimizations
3.1.6RegressionasaMinimizationProblem
3.2RegressionforPrediction&Sensitivities
3.2.1Prediction
3.2.2IntroductoryDiscussionofSensitivityandRobustness
3.2.3ComparingL2andL1Regressions
3.2.4TakingAnotherLookattheCoffeeData
3.3SmoothingversusDistributionTheory
3.3.1RegressionandConditionalExpectation
3.3.2MaximumLikelihoodApproach
3.4MultipleRegression
3.4.1Notation
3.4.2TheS-PlusFunctionim
3.4.3R2asaRegressionDiagnostic
3.5MatrixFormulationandLinearModels
3.5.1LinearModels
3.5.2LeastSquares（Linear）RegressionRevisited
3.5.3FirstExtensions
3.5.4TestingtheCAPM
3.6PolynomialRegression
3.6.1PolynomialRegressionasaLinearModel
3.6.2ExampleofS-PlusCommands
3.6.3ImportantRemark
3.6.4PredictionwithPolynomialRegression
3.6.5ChoiceoftheDegreep
3.7NonlinearRegression
3.8TermStructureofInterestRates：ACrashCourse
3.9ParametricYieldCurveEstimation
3.9.1EstimationProcedures
3.9.2PracticalImplementation
3.9.3S-PlusExperiments
3.9.4ConcludingRemarks
Appendix：CautionaryNotesonSomeS-PlusIdiosyncracies
Problems
Notes&Complements
LOCAL&NONPARAMETRICREGRESSION
4.1ReviewoftheRegressionSetup
4.2NaturalSplinesasLocalSmoothers
4.3NonparametricScatterplotSmoothers
4.3.1SmoothingSplines
4.3.2LocallyWeightedRegression
4.3.3ARobustSmoother
4.3.4TheSuperSmoother
4.3.5TheKernelSmoother
4.4MoreYieldCurveEstimation
4.4.1AFirstEstimationMethod
4.4.2ADirectApplicationofSmoothingSplines
4.4.3USandJapaneseInstantaneousForwardRates
4.5MultivariateKernelRegression
4.5.1RunningtheKernelinS-plus
4.5.2AnExampleInvolvingtheJune1998S&PFuturesContra
4.6ProjectionPursuitRegression
4.6.1TheS-PlusFunctionppreg
4.6.2ppregPredictionoftheS&PIndicators
4.7NonparametricOptionPricing
4.7.1GeneralitiesonOptionPricing
4.7.2NonparametricPricingAlternatives
4.7.3DescriptionoftheData
4.7.4TheActualExperiment
4.7.5NumericalResults
Appendix：KernelDensityEstimation&KernelRegression
Problems
Notes&Complements

PartⅢTIMESERIES&STATESPACEMODELS
5TIMESERIESMODELS：AR，MA，ARMA，&ALLTHAT
5.1NotationandFirstDefinitions
5.1.1Notation
5.1.2RegularTimeSeriesandSignals
5.1.3CalendarandIrregularTimeSeries
5.1.4ExampleofDallyS&P500FuturesContracts
5.2HighFrequencyData
5.2.1TimeDateManipulations
5.3TimeDependentStatisticsandStationarity
5.3.1StatisticalMoments
5.3.2TheNotionofStationarity
5.3.3TheSearchforStationarity
5.3.4TheExampleoftheC02Concentrations
5.4FirstExamplesofModels
5.4.1WhiteNoise
5.4.2RandomWalk
5.4.3AutoRegressiveTimeSeries
5.4.4MovingAverageTimeSeries
5.4.5UsingtheBackwardShiftOperatorB
5.4.6LinearProcesses
5.4：7Causality，StationarityandInvertibility
5.4.8ARMATimeSeries
5.4.9ARIMAModels
5.5FittingModelstoData
5.5.1PracticalSteps
……
内容简介:
Thisbookgrewoutoflecturesnoteswrittenforaone-semesterjuniorstatisticscourseofferedtotheundergraduatestudentsmajoringintheDepartmentofOper-ationsResearchandFinancialEngineeringatPrincetonUniversity.Tidbitsofthehistoryofthiscoursewillshedlightonthenatureandspiritofthebook.
Thepurposeofthecourseistointroducethestudentstomodemdataanalysiswithanemphasisonadomainofapplicationthatisofinteresttomostofthem：financialengineering.Theprerequisitesforthiscourseareminimal，howeveritisfairtosaythatallofthestudentshavealreadytakenabasicintroductorystatisticscourse.Thustheelementarynotionsofrandomvariables，expectationandcorrelationaretakenforgranted，andearlierexposuretostatisticalinference（estimation，testsandconfidenceintervals）isassumed.Itisalsoexpectedthatthestudentsarefamiliarwithaminimumoflinearalgebraaswellasvectorandmatrixcalculus.
目录:
PartⅠDATAEXPLORATION，ESTIMATIONANDSIMULATION
UNIVARIATEEXPLORATORYDATAANALYSIS
1.1Data，RandomVariablesandTheirDistributions
1.1.1ThePCSData
1.1.2TheS&P500IndexandFinancialReturns
1.1.3RandomVariablesandTheirDistributions
1.1.4ExamplesofProbabilityDistributionFamilies.，
1.2FirstExploratoryDataAnalysisTools
1.2.1RandomSamples
1.2.2Histograms
1.3MoreNonparametricDensityEstimation
1.3.1KernelDensityEstimation
1.3.2ComparisonwiththeHistogram
1.3.3S&PDailyReturns
1.3.4ImportanceoftheChoiceoftheBandwidth
1.4QuantilesandQ-QPlots
1.4.1UnderstandingtheMeaningofQ-QPlots
1.4.2ValueatRiskandExpectedShortfall
1.5EstimationfromEmpiricalData
1.5.1TheEmpiricalDistributionFunction
1.5.2OrderStatistics
1.5.3EmpiricalQ-QPlots
1.6RandomGeneratorsandMonteCarloSamples
1.7ExtremesandHeavyTailDistributions
1.7.1S&PDailyReturns，OnceMore
1.7.2TheExampleofthePCSIndex
1.7.3TheExampleoftheWeeklyS&PReturns
Problems
Notes&Complements
2MULTIVARIATEDATAEXPLORATION
2.1MultivariateDataandFirstMeasureofDependence
2.1.1DensityEstimation
2.1.2TheCorrelationCoefficient
2.2TheMultivariateNormalDistribution
2.2.1SimulationofRandomSamples
2.2.2TheBivariateCase
2.2.3ASimulationExample
2.2.4LetsHaveSomeCoffee
2.2.5IstheJointDistributionNormal？
2.3MarginalsandMoreMeasuresofDependence
2.3.1EstimationoftheCoffeeLog-ReturnDistributions
2.3.2MoreMeasuresofDependence
2.4CopulasandRandomSimulations
2.4.1Copulas
2.4.2FirstExamplesofCopulaFamilies
2.4.3CopulasandGeneralBivariateDistributions
2.4.4FittingCopulas
2.4.5MonteCarloSimulationswithCopulas
2.4.6ARiskManagementExample
2.5PrincipalComponentAnalysis
2.5.1IdentificationofthePrincipalComponentsofaDataSet
2.5.2PCAwithS-Plus
2.5.3EffectiveDimensionoftheSpaceofYieldCurves
2.5.4SwapRateCurves
Appendix1：CalculuswithRandomVectorsandMatrices
Appendix2：FamiliesofCopulas
Problems
Notes&Complements

PartⅡREGRESSION
3PARAMETRICREGRESSION
3.1SimpleLinearRegression
3.1.1GettingtheData
3.1.2FirstPlots
3.1.3RegressionSet-up
3.1.4SimpleLinearRegression
3.1.5CostMinimizations
3.1.6RegressionasaMinimizationProblem
3.2RegressionforPrediction&Sensitivities
3.2.1Prediction
3.2.2IntroductoryDiscussionofSensitivityandRobustness
3.2.3ComparingL2andL1Regressions
3.2.4TakingAnotherLookattheCoffeeData
3.3SmoothingversusDistributionTheory
3.3.1RegressionandConditionalExpectation
3.3.2MaximumLikelihoodApproach
3.4MultipleRegression
3.4.1Notation
3.4.2TheS-PlusFunctionim
3.4.3R2asaRegressionDiagnostic
3.5MatrixFormulationandLinearModels
3.5.1LinearModels
3.5.2LeastSquares（Linear）RegressionRevisited
3.5.3FirstExtensions
3.5.4TestingtheCAPM
3.6PolynomialRegression
3.6.1PolynomialRegressionasaLinearModel
3.6.2ExampleofS-PlusCommands
3.6.3ImportantRemark
3.6.4PredictionwithPolynomialRegression
3.6.5ChoiceoftheDegreep
3.7NonlinearRegression
3.8TermStructureofInterestRates：ACrashCourse
3.9ParametricYieldCurveEstimation
3.9.1EstimationProcedures
3.9.2PracticalImplementation
3.9.3S-PlusExperiments
3.9.4ConcludingRemarks
Appendix：CautionaryNotesonSomeS-PlusIdiosyncracies
Problems
Notes&Complements
LOCAL&NONPARAMETRICREGRESSION
4.1ReviewoftheRegressionSetup
4.2NaturalSplinesasLocalSmoothers
4.3NonparametricScatterplotSmoothers
4.3.1SmoothingSplines
4.3.2LocallyWeightedRegression
4.3.3ARobustSmoother
4.3.4TheSuperSmoother
4.3.5TheKernelSmoother
4.4MoreYieldCurveEstimation
4.4.1AFirstEstimationMethod
4.4.2ADirectApplicationofSmoothingSplines
4.4.3USandJapaneseInstantaneousForwardRates
4.5MultivariateKernelRegression
4.5.1RunningtheKernelinS-plus
4.5.2AnExampleInvolvingtheJune1998S&PFuturesContra
4.6ProjectionPursuitRegression
4.6.1TheS-PlusFunctionppreg
4.6.2ppregPredictionoftheS&PIndicators
4.7NonparametricOptionPricing
4.7.1GeneralitiesonOptionPricing
4.7.2NonparametricPricingAlternatives
4.7.3DescriptionoftheData
4.7.4TheActualExperiment
4.7.5NumericalResults
Appendix：KernelDensityEstimation&KernelRegression
Problems
Notes&Complements

PartⅢTIMESERIES&STATESPACEMODELS
5TIMESERIESMODELS：AR，MA，ARMA，&ALLTHAT
5.1NotationandFirstDefinitions
5.1.1Notation
5.1.2RegularTimeSeriesandSignals
5.1.3CalendarandIrregularTimeSeries
5.1.4ExampleofDallyS&P500FuturesContracts
5.2HighFrequencyData
5.2.1TimeDateManipulations
5.3TimeDependentStatisticsandStationarity
5.3.1StatisticalMoments
5.3.2TheNotionofStationarity
5.3.3TheSearchforStationarity
5.3.4TheExampleoftheC02Concentrations
5.4FirstExamplesofModels
5.4.1WhiteNoise
5.4.2RandomWalk
5.4.3AutoRegressiveTimeSeries
5.4.4MovingAverageTimeSeries
5.4.5UsingtheBackwardShiftOperatorB
5.4.6LinearProcesses
5.4：7Causality，StationarityandInvertibility
5.4.8ARMATimeSeries
5.4.9ARIMAModels
5.5FittingModelstoData
5.5.1PracticalSteps
……

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