A Course on Statistics for Finance
Features
- Incorporates both applied statistics and mathematical statistics
- Covers fundamental statistical concepts and tools, including averages, measures of variability, histograms, non-numerical variables, rates of return, and univariate, multivariate, two-way, and seasonal data sets
- Presents a careful development of regression, from simple to more complex models
- Integrates regression and time series analysis with applications in finance
- Requires no prior background in finance
- Includes many exercises within and at the end of each chapter
Figure slides available upon qualifying course adoption
Summary
Taking a data-driven approach, A Course on Statistics for Finance presents statistical methods for financial investment analysis. The author introduces regression analysis, time series analysis, and multivariate analysis step by step using models and methods from finance.
The book begins with a review of basic statistics, including descriptive statistics, kinds of variables, and types of data sets. It then discusses regression analysis in general terms and in terms of financial investment models, such as the capital asset pricing model and the Fama/French model. It also describes mean-variance portfolio analysis and concludes with a focus on time series analysis.
Providing the connection between elementary statistics courses and quantitative finance courses, this text helps both existing and future quants improve their data analysis skills and better understand the modeling process.
Table of Contents
INTRODUCTORY CONCEPTS AND DEFINITIONS
Review of Basic Statistics
What Is Statistics?
Characterizing Data
Measures of Central Tendency
Measures of Variability
Higher Moments
Summarizing Distributions
Bivariate Data
Three Variables
Two-Way Tables
Stock Price Series and Rates of Return
Introduction
Sharpe Ratio
Value-at-Risk
Distributions for RORs
Several Stocks and Their Rates of Return
Introduction
Review of Covariance and Correlation
Two Stocks
Three Stocks
m Stocks
REGRESSION
Simple Linear Regression; CAPM and Beta
Introduction
Simple Linear Regression
Estimation
Inference Concerning the Slope
Testing Equality of Slopes of Two Lines through the Origin
Linear Parametric Functions
Variances Dependent upon X
A Financial Application: CAPM and "Beta"
Slope and Intercept
Multiple Regression and Market Models
Multiple Regression Models
Market Models
Models with Both Numerical and Dummy Explanatory Variables
Model Building
PORTFOLIO ANALYSIS
Mean-Variance Portfolio Analysis
Introduction
Two Stocks
Three Stocks
m Stocks
m Stocks and a Risk-Free Asset
Value-at-Risk
Selling Short
Market Models and Beta
Utility-Based Portfolio Analysis
Introduction
Single-Criterion Analysis
TIME SERIES ANALYSIS
Introduction to Time Series Analysis
Introduction
Control Charts
Moving Averages
Need for Modeling
Trend, Seasonality, and Randomness
Models with Lagged Variables
Moving-Average Models
Identification of ARIMA Models
Seasonal Data
Dynamic Regression Models
Simultaneous Equations Models
Regime Switching Models
Introduction
Bull and Bear Markets
Appendix A: Vectors and Matrices
Appendix B: Normal Distributions
Appendix C: Lagrange Multipliers
Appendix D: Abbreviations and Symbols
Index
A Summary, Exercises, and Bibliography appear at the end of each chapter.
