Figure slides available upon qualifying course adoption
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.
INTRODUCTORY CONCEPTS AND DEFINITIONS
Review of Basic Statistics
What Is Statistics?
Measures of Central Tendency
Measures of Variability
Stock Price Series and Rates of Return
Distributions for RORs
Several Stocks and Their Rates of Return
Review of Covariance and Correlation
Simple Linear Regression; CAPM and Beta
Simple Linear Regression
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
Models with Both Numerical and Dummy Explanatory Variables
Mean-Variance Portfolio Analysis
m Stocks and a Risk-Free Asset
Market Models and Beta
Utility-Based Portfolio Analysis
TIME SERIES ANALYSIS
Introduction to Time Series Analysis
Need for Modeling
Trend, Seasonality, and Randomness
Models with Lagged Variables
Identification of ARIMA Models
Dynamic Regression Models
Simultaneous Equations Models
Regime Switching Models
Bull and Bear Markets
Appendix A: Vectors and Matrices
Appendix B: Normal Distributions
Appendix C: Lagrange Multipliers
Appendix D: Abbreviations and Symbols
A Summary, Exercises, and Bibliography appear at the end of each chapter.
"… Through numerous examples, the book explains how the theory of RDS can describe the asymptotic and qualitative behavior of systems of random and stochastic differential-difference equations in terms of stability, invariant manifolds and attractors. … provides a variety of RDS for approximating financial models, and studies the stability and optimal control of RDS. The book is useful for graduate students in RDS and mathematical _nance as well as practitioners working in the financial industry."
— Ahmed Hegazi (Mansoura ), Zentralblatt MATH
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