A Course on Statistics for Finance

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ISBN 9781439892541
Cat# K14149



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ISBN 9781466578210
Cat# KE21802



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  • 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


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

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
Sharpe Ratio
Distributions for RORs

Several Stocks and Their Rates of Return
Review of Covariance and Correlation
Two Stocks
Three Stocks
m Stocks

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
Market Models
Models with Both Numerical and Dummy Explanatory Variables
Model Building

Mean-Variance Portfolio Analysis
Two Stocks
Three Stocks
m Stocks
m Stocks and a Risk-Free Asset
Selling Short
Market Models and Beta

Utility-Based Portfolio Analysis
Single-Criterion Analysis

Introduction to Time Series Analysis
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
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.

Author Bio(s)

Editorial Reviews

"… 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