TY  - BOOK
AU  - Severini,Thomas A.
TI  - Introduction to statistical methods for financial models
T2  - Chapman & Hall/CRC Texts in Statistical Science
SN  - 9781351981903
AV  - HG176.5 .S49 2018
PY  - 2018///]
CY  - Boca Raton, FL
PB  - CRC Press
KW  - Statistics for Business, Finance & Economics
KW  - Financial Mathematics
KW  - Finance
KW  - Statistical methods
KW  - Mathematical models
KW  - MATHEMATICS / Probability & Statistics / General
KW  - bisacsh
KW  - BUSINESS & ECONOMICS / Finance
N1  - Description based upon print version of record; Title Page; Copyright Page; Dedication; Table of Contents; Preface; 1 Introduction; 2 Returns; 2.1 Introduction; 2.2 Basic Concepts; 2.3 Adjusted Prices; 2.4 Statistical Properties of Returns; 2.5 Analyzing Return Data; 2.6 Suggestions for Further Reading; 2.7 Exercises; 3 Random Walk Hypothesis; 3.1 Introduction; 3.2 Conditional Expectation; 3.3 Efficient Markets and the Martingale Model; 3.4 Random Walk Models for Asset Prices; 3.5 Tests of the Random Walk Hypothesis; 3.6 Do Stock Returns Follow the Random Walk Model?; 3.7 Suggestions for Further Reading; 3.8 Exercises; 4 Portfolios; 4.1 Introduction4.2 Basic Concepts; 4.3 Negative Portfolio Weights: Short Sales; 4.4 Optimal Portfolios of Two Assets; 4.5 Risk-Free Assets; 4.6 Portfolios of Two Risky Assets and a Risk-Free Asset; 4.7 Suggestions for Further Reading; 4.8 Exercises; 5 Efficient Portfolio Theory; 5.1 Introduction; 5.2 Portfolios of N Assets; 5.3 Minimum-Risk Frontier; 5.4 The Minimum-Variance Portfolio; 5.5 The Efficient Frontier; 5.6 Risk-Aversion Criterion; 5.7 The Tangency Portfolio; 5.8 Portfolio Constraints; 5.9 Suggestions for Further Reading; 5.10 Exercises; 6 Estimation; 6.1 Introduction; 6.2 Basic Sample Statistics6.3 Estimation of the Mean Vector and Covariance Matrix; 6.4 Weighted Estimators; 6.5 Shrinkage Estimators; 6.6 Estimation of Portfolio Weights; 6.7 Using Monte Carlo Simulation to Study the Properties of Estimators; 6.8 Suggestions for Further Reading; 6.9 Exercises; 7 Capital Asset Pricing Model; 7.1 Introduction; 7.2 Security Market Line; 7.3 Implications of the CAPM; 7.4 Applying the CAPM to a Portfolio; 7.5 Mispriced Assets; 7.6 The CAPM without a Risk-Free Asset; 7.7 Using the CAPM to Describe the Expected Returns on a Set of Assets; 7.8 Suggestions for Further Reading7.9 Exercises; 8 The Market Model; 8.1 Introduction; 8.2 Market Indices; 8.3 The Model and Its Estimation; 8.4 Testing the Hypothesis that an Asset Is Priced Correctly; 8.5 Decomposition of Risk; 8.6 Shrinkage Estimation and Adjusted Beta; 8.7 Applying the Market Model to Portfolios; 8.8 Diversification and the Market Model; 8.9 Measuring Portfolio Performance; 8.10 Standard Errors of Estimated Performance Measures; 8.11 Suggestions for Further Reading; 8.12 Exercises; 9 The Single-Index Model; 9.1 Introduction; 9.2 The Model; 9.3 Covariance Structure of Returns under the Single-Index Model9.4 Estimation; 9.5 Applications to Portfolio Analysis; 9.6 Active Portfolio Management and the Treynor-Black Method; 9.7 Suggestions for Further Reading; 9.8 Exercises; 10 Factor Models; 10.1 Introduction; 10.2 Limitations of the Single-Index Model; 10.3 The Model and Its Estimation; 10.4 Factors; 10.5 Arbitrage Pricing Theory; 10.6 Factor Premiums; 10.7 Applications of Factor Models; 10.8 Suggestions for Further Reading; 10.9 Exercises; References; Index
N2  - "This book provides an introduction to the use of statistical concepts and methods to model and analyze financial data. The ten chapters of the book fall naturally into three sections. Chapters 1 to 3 cover some basic concepts of finance, focusing on the properties of returns on an asset. Chapters 4 through 6 cover aspects of portfolio theory and the methods of estimation needed to implement that theory. The remainder of the book, Chapters 7 through 10, discusses several models for financial data, along with the implications of those models for portfolio theory and for understanding the properties of return data. The audience for the book is students majoring in Statistics and Economics as well as in quantitative fields such as Mathematics and Engineering. Readers are assumed to have some background in statistical methods along with courses in multivariate calculus and linear algebra. "--Provided by publisher
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