Probability and Statistics for Finance

Name: Probability and Statistics for Finance
Price: 60.32 USD
Availability: InStock
ISBN: 9780470400937

ISBN-10: 0470400935

ISBN-13: 9780470400937

Edition: 2010

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

List price: $79.00

This item qualifies for FREE shipping.

30 day, 100% satisfaction guarantee!

Sell

Get cash fast!

Buy new: $60.32

Marketplace

3 new & used from $75.79

what's this?

Rush Rewards U
Members Receive:

You have reached 400 XP and carrot coins. That is the daily max!

Description:

In this book, the authors provide readers with an array of topics in probability and statistics for applying in the finance world. The book covers the basics through the intermediate applications typically not provided in a general statistics course for business majors. Even though these topics are not usually covered in MBA studies, they are becoming more and more important to finance practitioners in today's world. Rachev, Hoechstoetter, Fabozzi, and Focardi offer real-world illustrations throughout the text. Chapters include detailed discussions of Descriptive Statistics, Basic Probability Theory, Inductive Statistics, Multivariate Analysis, and Stochastic Processes.

Book details

List price: $79.00
Copyright year: 2010
Publisher: John Wiley & Sons, Limited
Publication date: 10/1/2010
Binding: Hardcover
Pages: 672
Size: 6.30" wide x 9.30" long x 2.00" tall
Weight: 2.046
Language: English

Douglas J. Lucas is Executive Director and Head of CDO Research at UBS. He is also Chairman of The Bond Market Association's CDO Research Committee and ranked top three in CDO research in the Institutional Investor's fixed income analyst survey. Lucas has been involved in the CDO market for nearly two decades, having developed Moody's rating methodology for CDOs in 1989.LAURIE S. GOODMAN, PhD, is Managing Director and co-Head of Global Fixed Income Research at UBS. She manages U.S. Securitized Products and Treasury/Agency/Derivatives Research. Goodman has worked on Wall Street for over twenty years and is well regarded by the investor community, having won more #1 slots on the Institutional…

FRANK J. FABOZZI, PHD, CFA, CPA, is the Frederick Frank Adjunct Professor of Finance at Yale University's School of Management and a Fellow of the International Center for Finance. Prior to joining the Yale faculty, Fabozzi was a visiting professor of finance in the Sloan School of Management at MIT. Fabozzi is the Editor of the Journal of Portfolio Management.Sergio M. Focardi is a founding partner of the Paris-based consulting firm, The Intertek Group. He consults on, trains on, and implements quantitative financial models. He is also a member of the editorial board of the Journal of Portfolio Management and author of numerous articles and books on financial modeling.Petter N. Kolm, PHD,…



Preface


About the Authors



Introduction


Probability Versus Statistics


Overview of the Book



Descriptive Statistics



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)



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)



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)



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)



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)



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)



Basic Probability Theory



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)



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)



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)



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)



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)



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



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)



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)



Copula and Dependence Measures


Copula


Alternative Dependence Measures


Concepts Explained in this Chapter (In Order of Presentation)



Inductive Statistics



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)



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)



Hypothesis Testing


Hypotheses


Error Types


Quality Criteria of a Test


Examples


Concepts Explained in this Chapter (In Order of Presentation)



Multivariate Linear Regression Analysis



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)



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)



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)



Important Functions and Their Features


Continuous Function


Indicator Function


Derivatives


Monotonic Function


Integral


Some Functions



Fundamentals of Matrix Operations and Concepts


The Notion of Vector and Matrix


Matrix Multiplication


Particular Matrices


Positive Semidefinite Matrices



Binomial and Multinomial Coefficients


Binomial Coefficient


Multinomial Coefficient



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