A COURSE ON STATISTICS FOR FINANCE SCLOVE ST.L. Księgarnia

A Course on Statistics for Finance

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? 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.

269 pages, Hardcover

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