Svetlozar T. Rachev, Markus Hoechstoetter, Frank J. Fabozzi, Sergio M. Focardi

Probability and Statistics for Finance

• Gebundenes Buch

Produktbeschreibung

A comprehensive look at how probability and statistics is applied to the investment process

Finance has become increasingly more quantitative, drawing on techniques in probability and statistics that many finance practitioners have not had exposure to before. In order to keep up, you need a firm understanding of this discipline.
Probability and Statistics for Finance addresses this issue by showing you how to apply quantitative methods to portfolios, and in all matter of your practices, in a clear, concise manner. Informative and accessible, this guide starts off with the basics and builds to an intermediate level of mastery.
Outlines an array of topics in probability and statistics and how to apply them in the world of finance
Includes detailed discussions of descriptive statistics, basic probability theory, inductive statistics, and multivariate analysis
Offers real-world illustrations of the issues addressed throughout the text
The authors cover a wide range of topics in this book, which can be used by all finance professionals as well as students aspiring to enter the field of finance.

Produktdetails

• Frank J. Fabozzi Series
• Verlag: Wiley & Sons
• 1. Auflage
• Seitenzahl: 672
• Erscheinungstermin: 7. September 2010
• Englisch
• Abmessung: 235mm x 157mm x 40mm
• Gewicht: 937g
• ISBN-13: 9780470400937
• ISBN-10: 0470400935
• Artikelnr.: 29337814

Autorenporträt

SVETLOZAR T. RACHEV, PhD, DSC, is Chair Professor at the University of Karlsruhe in the School of Economics and Business Engineering, and Professor Emeritus at the University of California, Santa Barbara, in the Department of Statistics and Applied Probability. He was cofounder of Bravo Risk Management Group, acquired by FinAnalytica, where he currently serves as Chief Scientist. MARKUS HÖCHSTÖTTER, PhD, is an Assistant Professor in the Department of Econometrics and Statistics, University of Karlsruhe. FRANK J. FABOZZI, PhD, CFA, CPA, is Professor in the Practice of Finance and Becton Fellow at the Yale School of Management and Editor of the Journal of Portfolio Management. He is an Affiliated Professor at the University of Karlsruhe's Institute of Statistics, Econometrics and Mathematical Finance, and is on the Advisory Council for the Department of Operations Research and Financial Engineering at Princeton University. SERGIO M. FOCARDI, PhD, is a Professor of Finance at EDHEC Business School and founding partner of the Paris-based consulting firm Intertek Group plc.

Inhaltsangabe

Preface. About the Authors. CHAPTER 1 Introduction. Probability Versus
Statistics. Overview of the Book. PART ONE Descriptive Statistics. CHAPTER
2 Basic Data Analysis. Data Types. Frequency Distributions. Empirical
Cumulative Frequency Distribution. Data Classes. Cumulative Frequency
Distributions. Concepts Explained in this Chapter (In Order of
Presentation). CHAPTER 3 Measures of Location and Spread. Parameters versus
Statistics. Center and Location. Variation. Measures of the Linear
Transformation. Summary of Measures. Concepts Explained in this Chapter (In
Order of Presentation). CHAPTER 4 Graphical Representation of Data. Pie
Charts. Bar Chart. Stem and Leaf Diagram. Frequency Histogram. Ogive
Diagrams. Box Plot. QQ Plot. Concepts Explained in this Chapter (In Order
of Presentation). CHAPTER 5 Multivariate Variables and Distributions. Data
Tables and Frequencies. Class Data and Histograms. Marginal Distributions.
Graphical Representation. Conditional Distribution. Conditional Parameters
and Statistics. Independence. Covariance. Correlation. Contingency
Coefficient. Concepts Explained in this Chapter (In Order of Presentation).
CHAPTER 6 Introduction to Regression Analysis. The Role of Correlation.
Regression Model: Linear Functional Relationship Between Two Variables.
Distributional Assumptions of the Regression Model. Estimating the
Regression Model. Goodness of Fit of the Model. Linear Regression of Some
Non-Linear Relationship. Two Applications in Finance. Concepts Explained in
this Chapter (In Order of Presentation). CHAPTER 7 Introduction to Time
Series Analysis. What Is Time Series? Decomposition of Time Series.
Representation of Time Series with Difference Equations. Application: The
Price Process. Concepts Explained in this Chapter (In Order of
Presentation). PART TWO Basic Probability Theory. CHAPTER 8 Concepts of
Probability Theory. Historical Development of Alternative Approaches to
Probability. Set Operations and Preliminaries. Probability Measure. Random
Variable. Concepts Explained in this Chapter (In Order of Presentation).
CHAPTER 9 Discrete Probability Distributions. Discrete Law. Bernoulli
Distribution. Binomial Distribution. Hypergeometric Distribution.
Multinomial Distribution. Poisson Distribution Discrete Uniform
Distribution. Concepts Explained in this Chapter (In Order of
Presentation). CHAPTER 10 Continuous Probability Distributions. Continuous
Probability Distribution Described. Distribution Function. Density
Function. Continuous Random Variable. Computing Probabilities from the
Density Function. Location Parameters. Dispersion Parameters. Concepts
Explained in this Chapter (In Order of Presentation). CHAPTER 11 Continuous
Probability Distributions with Appealing Statistical Properties. Normal
Distribution. Chi-Square Distribution. Student's t-Distribution.
F-Distribution. Exponential Distribution. Rectangular Distribution. Gamma
Distribution. Beta Distribution. Log-Normal Distribution. Concepts
Explained in this Chapter (In Order of Presentation). CHAPTER 12 Continuous
Probability Distributions Dealing with Extreme Events. Generalized Extreme
Value Distribution. Generalized Pareto Distribution. Normal Inverse
Gaussian Distribution. a-Stable Distribution. Concepts Explained in this
Chapter (In Order of Presentation). CHAPTER 13 Parameters of Location and
Scale of Random Variables. Parameters of Location. Parameters of Scale.
Concepts Explained in this Chapter (In Order of Presentation). Appendix:
Parameters for Various Distribution Functions. CHAPTER 14 Joint Probability
Distributions. Higher Dimensional Random Variables. Joint Probability
Distribution. Marginal Distributions. Dependence. Covariance and
Correlation. Selection of Multivariate Distributions. Concepts Explained in
this Chapter (In Order of Presentation). CHAPTER 15 Conditional Probability
and Bayes' Rule. Conditional Probability. Independent Events.
Multiplicative Rule of Probability. Bayes' Rule. Conditional Parameters.
Concepts Explained in this Chapter (In Order of Presentation). CHAPTER 16
Copula and Dependence Measures. Copula. Alternative Dependence Measures.
Concepts Explained in this Chapter (In Order of Presentation). PART THREE
Inductive Statistics. CHAPTER 17 Point Estimators. Sample, Statistic, and
Estimator. Quality Criteria of Estimators. Large Sample Criteria. Maximum
Likehood Estimator. Exponential Family and Sufficiency. Concepts Explained
in this Chapter (In Order of Presentation). CHAPTER 18 Confidence
Intervals. Confidence Level and Confidence Interval. Confidence Interval
for the Mean of a Normal Random Variable. Confidence Interval for the Mean
of a Normal Random Variable with Unknown Variance. Confidence Interval for
the Parameter p of a Binomial Distribution. Concepts Explained in this
Chapter (In Order of Presentation). CHAPTER 19 Hypothesis Testing.
Hypotheses. Error Types. Quality Criteria of a Test. Examples. Concepts
Explained in this Chapter (In Order of Presentation). PART FOUR
Multivariate Linear Regression Analysis. CHAPTER 20 Estimates and
Diagnostics for Multivariate Linear Regression Analysis. The Multivariate
Linear Regression Model. Assumptions of the Multivariate Linear Regression
Model. Estimation of the Model Parameters. Designing the Model. Diagnostic
Check and Model Significance. Applications to Finance. Concepts Explained
in this Chapter (In Order of Presentation). CHAPTER 21 Designing and
Building a Multivariate Linear Regression Model. The Problem of
Multicollinearity. Incorporating Dummy Variables as Independent Variables.
Model Building Techniques 561 Concepts Explained in this Chapter (In Order
of Presentation). CHAPTER 22 Testing the Assumptions of the Multivariate
Linear Regression Model. Tests for Linearity. Assumed Statistical
Properties About the Error Term. Tests for the Residuals Being Normally
Distributed. Tests for Constant Variance of the Error Term
(Homoskedasticity). Absence of Autocorrelation of the Residuals. Concepts
Explained in this Chapter (In Order of Presentation). APPENDIX A Important
Functions and Their Features. Continuous Function. Indicator Function.
Derivatives. Monotonic Function. Integral. Some Functions. APPENDIX B
Fundamentals of Matrix Operations and Concepts. The Notion of Vector and
Matrix. Matrix Multiplication. Particular Matrices. Positive Semidefinite
Matrices. APPENDIX C Binomial and Multinomial Coefficients. Binomial
Coefficient. Multinomial Coefficient. APPENDIX D Application of the
Log-Normal Distribution to the Pricing of Call Options. Call Options.
Deriving the Price of a European Call Option. Illustration. REFERENCES.
INDEX.