Mathematics and Statistics for Financial Risk Management.

Mathematics and Statistics for Financial Risk Management is a practical guide to modern financial risk management for both practitioners and academics. Now in its second edition with more topics, more sample problems and more real world examples, this popular guide to financial risk management introduces readers to practical quantitative techniques for analyzing and managing financial risk. In a concise and easy-to-read style, each chapter introduces a different topic in mathematics or statistics. As different techniques are introduced, sample problems and application sections demonstrate how these techniques can be applied to actual risk management problems. Exercises at the end of each chapter and the accompanying solutions at the end of the book allow readers to practice the techniques they are learning and monitor their progress. A companion Web site includes interactive Excel spreadsheet examples and templates. Mathematics and Statistics for Financial Risk Management is an indispensable reference for today's financial risk professional.

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Year of publication:	2014 ; Second edition
Authors:	Miller, Michael B.
Publisher:	Hoboken, New Jersey : John Wiley & Sons, Incorporated
Subject:	Finanzmathematik \| Mathematical finance \| Statistische Methode \| Statistical method \| Ökonometrie \| Econometrics \| Portfolio-Management \| Portfolio selection \| Theorie \| Theory \| Risikomanagement \| Statistik
Description of contents:	Description [zbmath.org] ; Description [swbplus.bsz-bw.de]

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Extent:	1 online resource (333 pages)
Series:	Wiley finance series
Type of publication:	Book / Working Paper
Type of publication (narrower categories):	Lehrbuch
Language:	English
Notes:	Description based on publisher supplied metadata and other sources. Mathematics and Statistics for Financial Risk Management; Contents; Preface; What's New in the Second Edition; Acknowledgments; Chapter 1 Some Basic Math; Logarithms; Log Returns; Compounding; Limited Liability; Graphing Log Returns; Continuously Compounded Returns; Combinatorics; Discount Factors; Geometric Series; Infinite Series; Finite Series; Problems; Chapter 2 Probabilities; Discrete Random Variables; Continuous Random Variables; Probability Density Functions; Cumulative Distribution Functions; Inverse Cumulative Distribution Functions; Mutually Exclusive Events; Independent Events Probability MatricesConditional Probability; Problems; Chapter 3 Basic Statistics; Averages; Population and Sample Data; Discrete Random Variables; Continuous Random Variables; Expectations; Va riance and Standard Deviation; Standardized Variables; Covariance; Correlation; Application: Portfolio Variance and Hedging; Moments; Skewness; Kurtosis; Coskewness and Cokurtosis; Best Linear Unbiased Estimator (BLUE); Problems; Chapter 4 Distributions; Parametric Distributions; Uniform Distribution; Bernoulli Distribution; Binomial Distribution; Poisson Distribution; Normal Distribution Lognormal DistributionCentral Limit Theorem; Application: Monte Carlo Simulations Part I: Creating Normal Random Variables; Chi-Squared Distribution; Student's t Distribution; F-Distribution; Triangular Distribution; Beta Distribution; Mixture Distributions; Problems; Chapter 5 Multivariate Distributions and Copulas; Multivariate Distributions; Discrete Distributions; Continuous Distributions; Visualization; Correlation; Marginal Distributions; Copulas; What Is a Copula?; Graphing Copulas; Using Copulas in Simulations; Parameterization of Copulas; Problems; Chapter 6 Bayesian Analysis OverviewBayes' Theorem; Bayes versus Frequentists; Many-State Problems; Continuous Distributions; Bayesian Networks; Bayesian Networks versus Correlation Matrices; Problems; Chapter 7 Hypothesis Testing and Confidence Intervals; Sample Mean Revisited; Sample Variance Revisited; Confidence Intervals; Hypothesis Testing; Which Way to Test?; One Tail or Two?; The Confidence Level Returns; Chebyshev's Inequality; Application: VaR; Backtesting; Subadditivity; Expected Shortfall; Problems; Chapter 8 Matrix Algebra; Matrix Notation; Matrix Operations; Addition and Subtraction; Multiplication Zero MatrixTranspose; Application: Transition Matrices; Application: Monte Carlo Simulations Part II: Cholesky Decomposition; Problems; Chapter 9 Vector Spaces; Vectors Revisited; Orthogonality; Rotation; Principal Component Analysis; Application: The Dynamic Term Structure of Interest Rates; Application: The Structure of Global Equity Markets; Problems; Chapter 10 Linear Regression Analysis; Linear Regression (One Regressor); Ordinary Least Squares; Estimating the Parameters; Evaluating the Regression; Linear Regression (Multivariate); Multicollinearity; Estimating the Parameters Evaluating the Regression
ISBN:	978-1-118-75755-0 ; 978-1-118-75029-2
Classification:	Investition, Finanzierung
Source:	ECONIS - Online Catalogue of the ZBW

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