Mathematics and Statistics for Financial Risk Management (Wiley Finance) - Hardcover

Über die Autorin bzw. den Autor

Michael B. Miller studied economics at the American University of Paris and the University of Oxford before starting a career in finance. He is currently the CEO of Northstar Risk Corp. Before that, he was the Chief Risk Officer of Tremblant Capital Group, and prior to that, Head of Quantitative Risk Management at Fortress Investment Group. Mr. Miller is also a certified FRM and an adjunct professor at Rutgers Business School.

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Mathematics and Statistics for Financial Risk Management is 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 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.

This comprehensive resource covers basic statistical concepts from standard deviation and correlation to regression analysis and hypothesis testing. Widely used risk models, including value at risk, factor analysis, Monte Carlo simulation, and stress testing are also explored. Time series analysis, interest rate modeling, optimal hedging, and many other financial topics are covered as well.

The Second Edition of this popular guide includes two new chapters. The first new chapter, on multivariate distributions, explores important concepts for measuring the risk of portfolios, including joint distributions and copulas. The other new chapter, on Bayesian analysis, explores an approach to statistical analysis that is particularly useful in dealing with the short, noisy data sets that risk managers often face in practice.

Mathematics and Statistics for Financial Risk Management is an indispensable reference for today’s financial risk professional.

Aus dem Klappentext

Mathematics and Statistics for Financial Risk Management is 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 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.

This comprehensive resource covers basic statistical concepts from standard deviation and correlation to regression analysis and hypothesis testing. Widely used risk models, including value at risk, factor analysis, Monte Carlo simulation, and stress testing are also explored. Time series analysis, interest rate modeling, optimal hedging, and many other financial topics are covered as well.

The Second Edition of this popular guide includes two new chapters. The first new chapter, on multivariate distributions, explores important concepts for measuring the risk of portfolios, including joint distributions and copulas. The other new chapter, on Bayesian analysis, explores an approach to statistical analysis that is particularly useful in dealing with the short, noisy data sets that risk managers often face in practice.

Mathematics and Statistics for Financial Risk Management is an indispensable reference for today's financial risk professional.