{"product_id":"probability-and-statistics-for-finance-isbn-9780470400937","title":"Probability and Statistics for Finance","description":"A comprehensive look at how probability and statistics is applied to the investment process\u003cbr\u003e \u003cbr\u003e   \u003cp\u003eFinance 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.\u003cbr\u003e \u003ci\u003eProbability and Statistics for Finance\u003c\/i\u003e 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.\u003cbr\u003e •    Outlines an array of topics in probability and statistics and how to apply them in the world of finance\u003cbr\u003e •    Includes detailed discussions of descriptive statistics, basic probability theory, inductive statistics, and multivariate analysis\u003cbr\u003e •    Offers real-world illustrations of the issues addressed throughout the text\u003cbr\u003e 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.\u003c\/p\u003e \u003cp\u003ePreface xv\u003c\/p\u003e \u003cp\u003eAbout the Authors xvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eProbability vs. Statistics 4\u003c\/p\u003e \u003cp\u003eOverview of the Book 5\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart One Descriptive Statistics 15\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Basic Data Analysis 17\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eData Types 17\u003c\/p\u003e \u003cp\u003eFrequency Distributions 22\u003c\/p\u003e \u003cp\u003eEmpirical Cumulative Frequency Distribution 27\u003c\/p\u003e \u003cp\u003eData Classes 32\u003c\/p\u003e \u003cp\u003eCumulative Frequency Distributions 41\u003c\/p\u003e \u003cp\u003eConcepts Explained in this Chapter 43\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Measures of Location and Spread 45\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eParameters vs. Statistics 45\u003c\/p\u003e \u003cp\u003eCenter and Location 46\u003c\/p\u003e \u003cp\u003eVariation 59\u003c\/p\u003e \u003cp\u003eMeasures of the Linear Transformation 69\u003c\/p\u003e \u003cp\u003eSummary of Measures 71\u003c\/p\u003e \u003cp\u003eConcepts Explained in this Chapter 73\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Graphical Representation of Data 75\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePie Charts 75\u003c\/p\u003e \u003cp\u003eBar Chart 78\u003c\/p\u003e \u003cp\u003eStem and Leaf Diagram 81\u003c\/p\u003e \u003cp\u003eFrequency Histogram 82\u003c\/p\u003e \u003cp\u003eOgive Diagrams 89\u003c\/p\u003e \u003cp\u003eBox Plot 91\u003c\/p\u003e \u003cp\u003eQQ Plot 96\u003c\/p\u003e \u003cp\u003eConcepts Explained in this Chapter 99\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Multivariate Variables and Distributions 101\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eData Tables and Frequencies 101\u003c\/p\u003e \u003cp\u003eClass Data and Histograms 106\u003c\/p\u003e \u003cp\u003eMarginal Distributions 107\u003c\/p\u003e \u003cp\u003eGraphical Representation 110\u003c\/p\u003e \u003cp\u003eConditional Distribution 113\u003c\/p\u003e \u003cp\u003eConditional Parameters and Statistics 114\u003c\/p\u003e \u003cp\u003eIndependence 117\u003c\/p\u003e \u003cp\u003eCovariance 120\u003c\/p\u003e \u003cp\u003eCorrelation 123\u003c\/p\u003e \u003cp\u003eContingency Coefficient 124\u003c\/p\u003e \u003cp\u003eConcepts Explained in this Chapter 126\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Introduction to Regression Analysis 129\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Role of Correlation 129\u003c\/p\u003e \u003cp\u003eRegression Model: Linear Functional Relationship Between Two Variables 131\u003c\/p\u003e \u003cp\u003eDistributional Assumptions of the Regression Model 133\u003c\/p\u003e \u003cp\u003eEstimating the Regression Model 134\u003c\/p\u003e \u003cp\u003eGoodness of Fit of the Model 138\u003c\/p\u003e \u003cp\u003eLinear Regression of Some Nonlinear Relationship 140\u003c\/p\u003e \u003cp\u003eTwo Applications in Finance 142\u003c\/p\u003e \u003cp\u003eConcepts Explained in this Chapter 149\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Introduction to Time Series Analysis 153\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat Is Time Series? 153\u003c\/p\u003e \u003cp\u003eDecomposition of Time Series 154\u003c\/p\u003e \u003cp\u003eRepresentation of Time Series with Difference Equations 159\u003c\/p\u003e \u003cp\u003eApplication: The Price Process 159\u003c\/p\u003e \u003cp\u003eConcepts Explained in this Chapter 163\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Two Basic Probability Theory 165\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Concepts of Probability Theory 167\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eHistorical Development of Alternative Approaches to Probability 167\u003c\/p\u003e \u003cp\u003eSet Operations and Preliminaries 170\u003c\/p\u003e \u003cp\u003eProbability Measure 177\u003c\/p\u003e \u003cp\u003eRandom Variable 179\u003c\/p\u003e \u003cp\u003eConcepts Explained in this Chapter 185\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Discrete Probability Distributions 187\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDiscrete Law 187\u003c\/p\u003e \u003cp\u003eBernoulli Distribution 192\u003c\/p\u003e \u003cp\u003eBinomial Distribution 195\u003c\/p\u003e \u003cp\u003eHypergeometric Distribution 204\u003c\/p\u003e \u003cp\u003eMultinomial Distribution 211\u003c\/p\u003e \u003cp\u003ePoisson Distribution 216\u003c\/p\u003e \u003cp\u003eDiscrete Uniform Distribution 219\u003c\/p\u003e \u003cp\u003eConcepts Explained in this Chapter 221\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 Continuous Probability Distributions 229\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eContinuous Probability Distribution Described 229\u003c\/p\u003e \u003cp\u003eDistribution Function 230\u003c\/p\u003e \u003cp\u003eDensity Function 232\u003c\/p\u003e \u003cp\u003eContinuous Random Variable 237\u003c\/p\u003e \u003cp\u003eComputing Probabilities from the Density Function 238\u003c\/p\u003e \u003cp\u003eLocation Parameters 239\u003c\/p\u003e \u003cp\u003eDispersion Parameters 239\u003c\/p\u003e \u003cp\u003eConcepts Explained in this Chapter 245\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11 Continuous Probability Distributions with Appealing Statistical Properties 247\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eNormal Distribution 247\u003c\/p\u003e \u003cp\u003eChi-Square Distribution 254\u003c\/p\u003e \u003cp\u003eStudent’s t-Distribution 256\u003c\/p\u003e \u003cp\u003eF-Distribution 260\u003c\/p\u003e \u003cp\u003eExponential Distribution 262\u003c\/p\u003e \u003cp\u003eRectangular Distribution 266\u003c\/p\u003e \u003cp\u003eGamma Distribution 268\u003c\/p\u003e \u003cp\u003eBeta Distribution 269\u003c\/p\u003e \u003cp\u003eLog-Normal Distribution 271\u003c\/p\u003e \u003cp\u003eConcepts Explained in this Chapter 275\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12 Continuous Probability Distributions Dealing with Extreme Events 277\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGeneralized Extreme Value Distribution 277\u003c\/p\u003e \u003cp\u003eGeneralized Pareto Distribution 281\u003c\/p\u003e \u003cp\u003eNormal Inverse Gaussian Distribution 283\u003c\/p\u003e \u003cp\u003eα-Stable Distribution 285\u003c\/p\u003e \u003cp\u003eConcepts Explained in this Chapter 292\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 13 Parameters of Location and Scale of Random Variables 295\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eParameters of Location 296\u003c\/p\u003e \u003cp\u003eParameters of Scale 306\u003c\/p\u003e \u003cp\u003eConcepts Explained in this Chapter 321\u003c\/p\u003e \u003cp\u003eAppendix: Parameters for Various Distribution Functions 322\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 14 Joint Probability Distributions 325\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eHigher Dimensional Random Variables 326\u003c\/p\u003e \u003cp\u003eJoint Probability Distribution 328\u003c\/p\u003e \u003cp\u003eMarginal Distributions 333\u003c\/p\u003e \u003cp\u003eDependence 338\u003c\/p\u003e \u003cp\u003eCovariance and Correlation 341\u003c\/p\u003e \u003cp\u003eSelection of Multivariate Distributions 347\u003c\/p\u003e \u003cp\u003eConcepts Explained in this Chapter 358\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 15 Conditional Probability and Bayes’ Rule 361\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eConditional Probability 362\u003c\/p\u003e \u003cp\u003eIndependent Events 365\u003c\/p\u003e \u003cp\u003eMultiplicative Rule of Probability 367\u003c\/p\u003e \u003cp\u003eBayes’ Rule 372\u003c\/p\u003e \u003cp\u003eConditional Parameters 374\u003c\/p\u003e \u003cp\u003eConcepts Explained in this Chapter 377\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 16 Copula and Dependence Measures 379\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCopula 380\u003c\/p\u003e \u003cp\u003eAlternative Dependence Measures 406\u003c\/p\u003e \u003cp\u003eConcepts Explained in this Chapter 412\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Three Inductive Statistics 413\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 17 Point Estimators 415\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSample, Statistic, and Estimator 415\u003c\/p\u003e \u003cp\u003eQuality Criteria of Estimators 428\u003c\/p\u003e \u003cp\u003eLarge Sample Criteria 435\u003c\/p\u003e \u003cp\u003eMaximum Likehood Estimator 446\u003c\/p\u003e \u003cp\u003eExponential Family and Sufficiency 457\u003c\/p\u003e \u003cp\u003eConcepts Explained in this Chapter 461\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 18 Confidence Intervals 463\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eConfidence Level and Confidence Interval 463\u003c\/p\u003e \u003cp\u003eConfidence Interval for the Mean of a Normal Random Variable 466\u003c\/p\u003e \u003cp\u003eConfidence Interval for the Mean of a Normal Random Variable with Unknown Variance 469\u003c\/p\u003e \u003cp\u003eConfidence Interval for the Variance of a Normal Random Variable 471\u003c\/p\u003e \u003cp\u003eConfidence Interval for the Variance of a Normal Random Variable with Unknown Mean 474\u003c\/p\u003e \u003cp\u003eConfidence Interval for the Parameter p of a Binomial Distribution 475\u003c\/p\u003e \u003cp\u003eConfidence Interval for the Parameter λ of an Exponential Distribution 477\u003c\/p\u003e \u003cp\u003eConcepts Explained in this Chapter 479\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 19 Hypothesis Testing 481\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eHypotheses 482\u003c\/p\u003e \u003cp\u003eError Types 485\u003c\/p\u003e \u003cp\u003eQuality Criteria of a Test 490\u003c\/p\u003e \u003cp\u003eExamples 496\u003c\/p\u003e \u003cp\u003eConcepts Explained in this Chapter 518\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Four Multivariate Linear Regression Analysis 519\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 20 Estimates and Diagnostics for Multivariate Linear Regression Analysis 521\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Multivariate Linear Regression Model 522\u003c\/p\u003e \u003cp\u003eAssumptions of the Multivariate Linear Regression Model 523\u003c\/p\u003e \u003cp\u003eEstimation of the Model Parameters 523\u003c\/p\u003e \u003cp\u003eDesigning the Model 526\u003c\/p\u003e \u003cp\u003eDiagnostic Check and Model Significance 526\u003c\/p\u003e \u003cp\u003eApplications to Finance 531\u003c\/p\u003e \u003cp\u003eConcepts Explained in this Chapter 543\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 21 Designing and Building a Multivariate Linear Regression Model 545\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Problem of Multicollinearity 545\u003c\/p\u003e \u003cp\u003eIncorporating Dummy Variables as Independent Variables 548\u003c\/p\u003e \u003cp\u003eModel Building Techniques 561\u003c\/p\u003e \u003cp\u003eConcepts Explained in this Chapter 565\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 22 Testing the Assumptions of the Multivariate Linear Regression Model 567\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTests for Linearity 568\u003c\/p\u003e \u003cp\u003eAssumed Statistical Properties about the Error Term 570\u003c\/p\u003e \u003cp\u003eTests for the Residuals Being Normally Distributed 570\u003c\/p\u003e \u003cp\u003eTests for Constant Variance of the Error Term (Homoskedasticity) 573\u003c\/p\u003e \u003cp\u003eAbsence of Autocorrelation of the Residuals 576\u003c\/p\u003e \u003cp\u003eConcepts Explained in this Chapter 581\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix A Important Functions and Their Features 583\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eContinuous Function 583\u003c\/p\u003e \u003cp\u003eIndicator Function 586\u003c\/p\u003e \u003cp\u003eDerivatives 587\u003c\/p\u003e \u003cp\u003eMonotonic Function 591\u003c\/p\u003e \u003cp\u003eIntegral 592\u003c\/p\u003e \u003cp\u003eSome Functions 596\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix B Fundamentals of Matrix Operations and Concepts 601\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Notion of Vector and Matrix 601\u003c\/p\u003e \u003cp\u003eMatrix Multiplication 602\u003c\/p\u003e \u003cp\u003eParticular Matrices 603\u003c\/p\u003e \u003cp\u003ePositive Semidefinite Matrices 614\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix C Binomial and Multinomial Coefficients 615\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBinomial Coefficient 615\u003c\/p\u003e \u003cp\u003eMultinomial Coefficient 622\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix D Application of the Log-Normal Distribution to the Pricing of Call Options 625\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCall Options 625\u003c\/p\u003e \u003cp\u003eDeriving the Price of a European Call Option 626\u003c\/p\u003e \u003cp\u003eIllustration 631\u003c\/p\u003e \u003cp\u003eReferences 633\u003c\/p\u003e \u003cp\u003eIndex 635\u003c\/p\u003e  \u003cp\u003eSVETLOZAR 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.\u003c\/p\u003e \u003cp\u003eMARKUS HÖCHSTÖTTER, PhD, is an Assistant Professor in the Department of Econometrics and Statistics, University of Karlsruhe.\u003c\/p\u003e \u003cp\u003eFRANK 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.\u003c\/p\u003e \u003cp\u003eSERGIO M. FOCARDI, PhD, is a Professor of Finance at EDHEC Business School and founding partner of the Paris-based consulting firm Intertek Group plc.\u003c\/p\u003e  \u003cp\u003eIn order to remain competitive in the intensely quantitative field of finance, you need a firm understanding of the foundations of finance: probability and statistics. Expert authors Svetlozar (Zari) Rachev, Markus Höchstötter, Frank Fabozzi, and Sergio Focardi have written Probability and Statistics for Finance for that reason.\u003c\/p\u003e \u003cp\u003eEngaging and informative, this reliable guide not only covers introductory material on probability and statistics for students and practitioners, but also provides unique coverage of specific topics that are of special interest to finance professionals, researchers, and academics.\u003c\/p\u003e \u003cp\u003eDivided into four comprehensive parts, Probability and Statistics for Finance:\u003c\/p\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eDiscusses descriptive statistics: the different methods for gathering data and presenting them in the most succinct way\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eCovers the basics of probability theory as well as the different types of probability distributions, parameters of a probability distribution, joint and conditional probability distributions, continuous probability distributions dealing with extreme events, and dependence measures for two random variables beyond the correlation measure such as the copula function\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eExplores statistical inference: the method of drawing information from sample data about unknown parameters of the population from which the sample was drawn\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eProvides in-depth coverage of the most widely used statistical tools in finance—multivarate regression analysis—from the basics to building models\u003c\/p\u003e \u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eTo provide readers with a complete understanding of the various topics touched upon throughout, the book includes appendices that cover the fundamentals of functions and their features, matrix operations, and binomial and multinomial coefficients.\u003c\/p\u003e \u003cp\u003eWritten for both financial professionals and individuals aspiring to enter this field, Probability and Statistics for Finance addresses an array of important issues—from applying probability to portfolio management, asset pricing, risk management, and credit risk modeling to probability distributions that deal with extreme events and statistical measures. With this book, discover how probability and statistics can help you effectively navigate even the most complex financial terrain.\u003c\/p\u003e  \u003cp\u003eThe recent upheaval of the global financial system has enhanced the need for improved statistical tools for financial modeling and analysis. To fill that need, expert authors Svetlozar Rachev, Markus Höchstötter, Frank Fabozzi, and Sergio Focardi have written Probability and Statistics for Finance.\u003c\/p\u003e \u003cp\u003eFilled with in-depth insights and practical advice, this book guides readers from the basic elements of probability and statistics to the most advanced topics. Along the way, it covers everything from the application of probability to portfolio management, asset pricing, risk management, and credit risk modeling to probability distributions that deal with extreme events and statistical measures.\u003c\/p\u003e \u003cp\u003eThis book:\u003c\/p\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eOutlines an array of topics in probability and statistics and how to apply them in the world of finance\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eOffers detailed discussions of descriptive statistics, basic probability theory, inductive statistics, and multivariate analysis\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eProvides real-world illustrations of the issues addressed throughout the text\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eAnd much more\u003c\/p\u003e \u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eWritten with financial professionals, academics, and aspiring students in mind, Probability and Statistics for Finance has what you need to stay current and succeed in this fast-moving field.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989857681637,"sku":"NP9780470400937","price":100.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470400937.jpg?v=1761785697","url":"https:\/\/k12savings.com\/es\/products\/probability-and-statistics-for-finance-isbn-9780470400937","provider":"K12savings","version":"1.0","type":"link"}