Principles of Quantitative Finance

Name: Principles of Quantitative Finance
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Häftad, Engelska, 2019

Av Juan Castro, Esther Castro, Castro-Castro,

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Produktinformation

Utgivningsdatum2019-08-21
FormatHäftad
SpråkEngelska
Antal sidor277
FörlagKendall/Hunt Publishing Co ,U.S.
ISBN9781524985813

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Chapter 1 Introduction IntroductionWhy Statistics in Finance?From Data to Financial Decision MakingPopulation, Parameter, and Statistical ExperimentParameterStatistic (or Sample Statistic)What Is a Sampling Distribution?Standard ErrorDescriptive Statistics versus Inferential StatisticsDescriptive StatisticsInferential StatisticsEstimation of the Population VarianceExcel ApplicationEnd-of-Chapter QuestionsCaseChapter 2 Review of Basic Statistics IntroductionMeasures of Central Tendency, Dispersion, and ShapeMeasures of Central Tendency SummariesMeanMedianModeCriteria for Selecting Among the Mean, Median, and ModeMeasures of Return (Central Tendency)Quartiles and PercentilesQuartilesPercentilesMeasures of DispersionMeasures of Risk (Variation of Measures of Dispersion)RangeVarianceStandard DeviationCoefficient of VariationSharpe RatioThe Normal DistributionProperties of the Normal DistributionEmpirical RuleChebyshev's TheoremSkewness and KurtosisSkewnessKurtosisNumerical Example & DiscussionsComments about VariabilityCentral Limit TheoremZ-Score and Normal ProbabilityConfidence IntervalsCovariance and CorrelationBeta RiskExcel FunctionsProblemsCasesChapter 3 Hypothesis Testing IntroductionThe Null and Alternative HypothesesZ-Test of Hypothesis for the Mean (s Known)Risks in Decision Making Using Hypothesis-Testing MethodologyThe Level of SignificanceThe Confidence CoefficientThe b RiskThe Power of a TestRegions of Rejection and NonrejectionCompute Z-Test StatisticThe r-Value Approach to Hypothesis TestingA Connection between Confidence Interval Estimation and Hypothesis TestingOne-tailed TestsThe Critical Value ApproachT-Test of Hypothesis for the Mean (s Unknown)Two-Sample T-Test 70 c2 Test of Hypothesis for the Variance or Standard DeviationComment – Checking the Assumptions of the c2 Test for the Variance or Standard DeviationF-Test—Testing for Multiple VariancesUsing Excel for Hypothesis TestingProblemsCasesChapter 4 Managing Data Patterns IntroductionTime Series AnalysesTwo Basic Forecasting MethodsTime Series ComponentsLagsAutocorrelationAutocorrelation AnalysisMain Questions to Determine the Pattern of DataAre the Data Random?Do the Data have a Trend?Autocorrelation for Different Types of Time-series DataMeasuring Forecasting ErrorMethods to Evaluate the Forecasting ErrorsProblemsCasesConsumer Credit Outstanding Revolving 2006–2009 (billions)Chapter 5 Financial Forecasting Techniques IntroductionNaïve ModelsRate of Change, or a Percentage ValueNaïve Models and Seasonal VariationsAveraging MethodsSimple AveragesMoving AveragesDouble Moving AveragesExponential Smoothing MethodsData Series with Linear TrendExponential Smoothing Adjusted for Trend—Holt's MethodExponential Smoothing Adjusted for Trend and Seasonal Variation: Winter ModelProblemsCasesChapter 6 Simple Regression and Finance Finding the Sample Simple Regression ModelExports versus Imports—ExampleInterpretation of the Coefficients and ModelTesting the Sample CoefficientCorrelation of the Simple Regression ModelCoefficient of Determination for Simple RegressionSummary of the Export and Import ExampleSiriaco Mutual Fund and DJIA Returns—ExampleWalt Disney Linear Simple Regression—ExamplePredicting Mutual Fund with Gold—ExampleAssumptions Required to Use a Linear RegressionProblemsChapter 7 Multiple Regression and Finance IntroductionStart with Simple RegressionQualitative Financial Analysis of Multiple RegressionTesting the Overall Model—The F-DistributionTest for the Model CorrelationTest for Coefficient of DeterminationTest for Partial Significance for Each Independent VariableFinal Predicting Regression ModelPredicting Home Heating Gas ExampleCollinearity (Multicollinearity)Collinearity Example Using Disney's DataDetecting Collinearity Using Correlation MatrixDetecting Collinearity Using VIF (Variance Inflationary Factor)Using Subsets Model to Predict a ModelSerial Correlation and Error VarianceTesting Serial CorrelationDurbin–Watson Analysis of Serial CorrelationDurbin–Watson Test CriteriaSolutions to Serial Correlation ProblemsProblemsChapter 8 Event Study Application The Five Steps in an Event StudySelect an Event StudyIdentify the Event Date WindowCollect and Analyze the Sample Event DataDivide the Data between Pre- and Post-Event PeriodsTesting for Normal and Abnormal ReturnsConclusionCumulative Abnormal Returns TestAppendixAppendix A: Standard Normal DistributionAppendix B: Student's T-DistributionAppendix C: Chi-Square DistributionAppendix D: F-Distribution Table (Right-hand tail is .05)F-Distribution Table (Right-hand tail is .025)F-Distribution Table (Right-hand tail is .01)F-Distribution Table (Right-hand tail is .005)