A Course on Statistics for Finance

 
 
Chapman & Hall/CRC (Verlag)
  • 1. Auflage
  • |
  • erschienen am 3. September 2018
  • |
  • 269 Seiten
 
E-Book | ePUB mit Adobe DRM | Systemvoraussetzungen
978-1-315-36047-8 (ISBN)
 

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.

1. Auflage
  • Englisch
  • London
  • |
  • Großbritannien
Taylor & Francis Ltd
  • Für höhere Schule und Studium
  • |
  • Für Beruf und Forschung
4 schwarz-weiße Abbildungen, 33 schwarz-weiße Tabellen
  • 11114,99 MB
978-1-315-36047-8 (9781315360478)

Stanley L. Sclove is a professor of statistics in the Department of Information and Decision Sciences of the College of Business Administration at the University of Illinois at Chicago (UIC). His areas of specialization within statistics include multivariate statistical analysis, cluster analysis, time series analysis, and model selection criteria. Dr. Sclove's research interests include time series segmentation and regime switching via Markov models. He is an officer of the Classification Society and the Section of Risk Analysis of the American Statistical Association.

<strong>INTRODUCTORY CONCEPTS AND DEFINITIONS
</strong><strong>Review of Basic Statistics
</strong>What Is Statistics?
Characterizing Data
Measures of Central Tendency
Measures of Variability
Higher Moments
Summarizing Distributions
Bivariate Data
Three Variables
Two-Way Tables



<strong>Stock Price Series and Rates of Return
</strong>Introduction
Sharpe Ratio
Value-at-Risk
Distributions for RORs



<strong>Several Stocks and Their Rates of Return
</strong>Introduction
Review of Covariance and Correlation
Two Stocks
Three Stocks
<i>m</i> Stocks



<strong>REGRESSION
</strong><strong>Simple Linear Regression; CAPM and Beta
</strong>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 <i>X</i>
A Financial Application: CAPM and "Beta"
Slope and Intercept



<strong>Multiple Regression and Market Models
</strong>Multiple Regression Models
Market Models
Models with Both Numerical and Dummy Explanatory Variables
Model Building



<strong>PORTFOLIO ANALYSIS
</strong><strong>Mean-Variance Portfolio Analysis
</strong>Introduction
Two Stocks
Three Stocks
<i>m </i>Stocks
<i>m </i>Stocks and a Risk-Free Asset
Value-at-Risk
Selling Short
Market Models and Beta



<strong>Utility-Based Portfolio Analysis
</strong>Introduction
Single-Criterion Analysis



<strong>TIME SERIES ANALYSIS
</strong><strong>Introduction to Time Series Analysis
</strong>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



<strong>Regime Switching Models
</strong>Introduction
Bull and Bear Markets


<strong>Appendix A: Vectors and Matrices
Appendix B: Normal Distributions
Appendix C: Lagrange Multipliers
Appendix D: Abbreviations and Symbols</strong>


<strong></strong>


<strong>Index</strong>



<em>A Summary, Exercises, and Bibliography appear at the end of each chapter.</em>

"... 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
 

"... 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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