The Principles of Quantitative Finance

ISBN-13: 9781524985813 / Angielski

Castro-Castro

Spis treści

• Chapter 1 Introduction
• Introduction
• Why Statistics in Finance?
• From Data to Financial Decision Making
• Population, Parameter, and Statistical Experiment
• Parameter
• Statistic (or Sample Statistic)
• What Is a Sampling Distribution?
• Standard Error
• Descriptive Statistics versus Inferential Statistics
• Descriptive Statistics
• Inferential Statistics
• Estimation of the Population Variance
• Excel Application
• End-of-Chapter Questions
• Case
• Chapter 2 Review of Basic Statistics
• Introduction
• Measures of Central Tendency, Dispersion, and Shape
• Measures of Central Tendency Summaries
• Mean
• Median
• Mode
• Criteria for Selecting Among the Mean, Median, and Mode
• Measures of Return (Central Tendency)
• Quartiles and Percentiles
• Quartiles
• Percentiles
• Measures of Dispersion
• Measures of Risk (Variation of Measures of Dispersion)
• Range
• Variance
• Standard Deviation
• Coefficient of Variation
• Sharpe Ratio
• The Normal Distribution
• Properties of the Normal Distribution
• Empirical Rule
• Chebyshev's Theorem
• Skewness and Kurtosis
• Skewness
• Kurtosis
• Numerical Example & Discussions
• Comments about Variability
• Central Limit Theorem
• Z-Score and Normal Probability
• Confidence Intervals
• Covariance and Correlation
• Beta Risk
• Excel Functions
• Problems
• Cases
• Chapter 3 Hypothesis Testing
• Introduction
• The Null and Alternative Hypotheses
• Z-Test of Hypothesis for the Mean (s Known)
• Risks in Decision Making Using Hypothesis-Testing Methodology
• The Level of Significance
• The Confidence Coefficient
• The b Risk
• The Power of a Test
• Regions of Rejection and Nonrejection
• Compute Z-Test Statistic
• The r-Value Approach to Hypothesis Testing
• A Connection between Confidence Interval Estimation and Hypothesis Testing
• One-tailed Tests
• The Critical Value Approach
• T-Test of Hypothesis for the Mean (s Unknown)
• Two-Sample T-Test 70 c2 Test of Hypothesis for the Variance or Standard Deviation
• Comment – Checking the Assumptions of the c2 Test for the Variance or Standard Deviation
• F-Test—Testing for Multiple Variances
• Using Excel for Hypothesis Testing
• Problems
• Cases
• Chapter 4 Managing Data Patterns
• Introduction
• Time Series Analyses
• Two Basic Forecasting Methods
• Time Series Components
• Lags
• Autocorrelation
• Autocorrelation Analysis
• Main Questions to Determine the Pattern of Data
• Are the Data Random?
• Do the Data have a Trend?
• Autocorrelation for Different Types of Time-series Data
• Measuring Forecasting Error
• Methods to Evaluate the Forecasting Errors
• Problems
• Cases
• Consumer Credit Outstanding Revolving 2006–2009 (billions)
• Chapter 5 Financial Forecasting Techniques
• Introduction
• Naïve Models
• Rate of Change, or a Percentage Value
• Naïve Models and Seasonal Variations
• Averaging Methods
• Simple Averages
• Moving Averages
• Double Moving Averages
• Exponential Smoothing Methods
• Data Series with Linear Trend
• Exponential Smoothing Adjusted for Trend—Holt's Method
• Exponential Smoothing Adjusted for Trend and Seasonal Variation: Winter Model
• Problems
• Cases
• Chapter 6 Simple Regression and Finance
• Finding the Sample Simple Regression Model
• Exports versus Imports—Example
• Interpretation of the Coefficients and Model
• Testing the Sample Coefficient
• Correlation of the Simple Regression Model
• Coefficient of Determination for Simple Regression
• Summary of the Export and Import Example
• Siriaco Mutual Fund and DJIA Returns—Example
• Walt Disney Linear Simple Regression—Example
• Predicting Mutual Fund with Gold—Example
• Assumptions Required to Use a Linear Regression
• Problems
• Chapter 7 Multiple Regression and Finance
• Introduction
• Start with Simple Regression
• Qualitative Financial Analysis of Multiple Regression
• Testing the Overall Model—The F-Distribution
• Test for the Model Correlation
• Test for Coefficient of Determination
• Test for Partial Significance for Each Independent Variable
• Final Predicting Regression Model
• Predicting Home Heating Gas Example
• Collinearity (Multicollinearity)
• Collinearity Example Using Disney's Data
• Detecting Collinearity Using Correlation Matrix
• Detecting Collinearity Using VIF (Variance Inflationary Factor)
• Using Subsets Model to Predict a Model
• Serial Correlation and Error Variance
• Testing Serial Correlation
• Durbin–Watson Analysis of Serial Correlation
• Durbin–Watson Test Criteria
• Solutions to Serial Correlation Problems
• Problems
• Chapter 8 Event Study Application
• The Five Steps in an Event Study
• Select an Event Study
• Identify the Event Date Window
• Collect and Analyze the Sample Event Data
• Divide the Data between Pre- and Post-Event Periods
• Testing for Normal and Abnormal Returns
• Conclusion
• Cumulative Abnormal Returns Test
• Appendix
• Appendix A: Standard Normal Distribution
• Appendix B: Student's T-Distribution
• Appendix C: Chi-Square Distribution
• Appendix 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)