Mathematics and statistics for financial risk management /

"This is an excellent book to grasp the basics of financial risk management. Everything in the book is explained from scratch and the concepts are very well exemplified with real life situations. Accompanied with a website filled with excel sheets for application, the book is great for future c...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Miller, Michael B. (Michael Bernard), 1973-
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Hoboken, NJ : John Wiley & Sons, 2013.
Edición:2nd edition.
Colección:Wiley finance
Temas:
Risk management > Mathematical models.
Risk management > Statistical methods.
Gestion du risque > Modèles mathématiques.
Gestion du risque > Méthodes statistiques.
BUSINESS & ECONOMICS > Finance.
Risk management > Mathematical models
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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 Matrices
  • Conditional 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 Distribution
  • Central 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
  • Overview
  • Bayes' 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 Matrix
  • Transpose
  • 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
  • Application: Factor Analysis
  • Application: Stress Testing
  • Problems
  • Chapter 11 Time Series Models
  • Random Walks
  • Drift-Diffusion Model
  • Autoregression
  • Variance and Autocorrelation
  • Stationarity
  • Moving Average
  • Continuous Models
  • Application: GARCH
  • Application: Jump-Diffusion Model
  • Application: Interest Rate Models
  • Problems
  • Chapter 12 Decay Factors
  • Mean
  • Variance
  • Weighted Least Squares
  • Other Possibilities
  • Application: Hybrid VaR
  • Problems.

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