A practical guide to modern financial risk management for both practitioners and academics The recent financial crisis and its impact on the broader economy underscore the importance of financial risk management in today's world. At the same time, financial products and investment strategies are becoming increasingly complex. Today, it is more important than ever that risk managers possess a sound understanding of mathematics and statistics. In a concise and easy-to-read style, each chapter of this book introduces a different topic in mathematics or statistics. As different techniques are introduced, sample problemsand 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 website includes interactive Excel spreadsheet examples and templates. Covers basic statistical concepts from volatility and Bayes' Law to regression analysis and hypothesis testing Introduces risk models, including Value-at-Risk, factor analysis, Monte Carlo simulations, and stress testing Explains time series analysis, including interest rate, GARCH, and jump-diffusion models Explores bond pricing, portfolio credit risk, optimal hedging, and many other financial risk topicsIf you're looking for a book that will help you understand the mathematics and statistics of financial risk management, look no further. INDICE : Preface Acknowledgments Chapter 1: Some Basic Math Logarithms LogReturns Compounding Limited Liability Graphing Log Returns Continuously Compounded Returns Combinatorics Discount Factors Geometric Series Problems Chapter2: Probabilities Discrete Random Variables Mutually Exclusive Events Independent Events Probability Matrices Conditional Probability Bayes' Law Problems Chapter 3: Basic Statistics Averages Expectations Variance and Standard Deviation Standardized Variables Covariance Correlation Moments Skewness Kurtosis Coskewness and Cokurtosis BLUE Problems Chapter 4: Distributions Parametric Distributions Uniform Bernoulli Binomial Poisson Distribution Normal Lognormal Central Limit Theorem Chi-Squared Distribution Student's t Distribution F-Distribution Mixture Distributions Problems Chapter 5: Hypothesis Testing The Sample Mean Revisited Sample Variance Revisited Confidence Intervals Hypothesis TestingChebyshev's Inequality Application: VaR Problems Chapter 6: Matrix Algebra Matrix Notation Matrix Operations Application: Transition Matrices Application: Monte Carlool Simulations Part II: Cholesky Decomposition Problems Chapter 7: Vector Spaces Vectors Revisited Orthogonality Rotation Principal Component Analysis Problems Chapter 8: Linear Regression Analysis Linear Regression (one regressor) Optimal Hedging Revisited Linear Regression (multivariate) Application: Factor Analysis Application: Stress Testing Problems Chapter 9: Time SeriesModels Random Walks Drift-Diffusion Auto-regression Variance and Autocorrelation Stationarity Moving Average Continuous Models Application: GARCH Application: Jump-Diffusion Application: Interest Rate Models Problems Chapter 10: Decay Factors Mean Variance Weighted Least Squares Other Possibilities Application: Hybrid VaR Problems Appendix 1: Binary Numbers Appendix 2: Taylor ExpansionsAppendix 3: Vector Spaces Appendix 4: Greek Alphabet Appendix 5: Common Abbreviations Answers Bibliography About the Author Index
- ISBN: 978-1-118-17062-5
- Editorial: John Wiley & Sons
- Encuadernacion: Cartoné
- Páginas: 304
- Fecha Publicación: 14/03/2012
- Nº Volúmenes: 1
- Idioma: Inglés