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Informationen zum Autor 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. Klappentext A comprehensive look at how probability and statistics is applied to the investment processFinance 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 textThe 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. Zusammenfassung A comprehensive look at how probability and statistics is applied to the investment processFinance 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 textThe 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. Inhaltsverzeichnis Preface xv About the Authors xvii Chapter 1 Introduction 1 Probability vs. Statistics 4 Overview of the Book 5 Part One Descriptive Statistics 15 Chapter 2 Basic Data Analysis 17 Data Types 17 Frequency Distributions 22 Empirical Cumulative Frequency Distribution 27 Data Classes 32 Cumulative Frequency Distributions 41 Concepts Explained in this Chapter 43 Chapter 3 Measures of Location and Spread 45 Parameters vs....
List of contents
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.<
