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

Acerca del 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.

De la contraportada

"The first edition of this work was clear, comprehensive, and up-to-date. The Second Edition is all that and includes important new material on Bayesian and classical methods. Extensive examples and problems make clear how these concepts are used in the world's top financial institutions. The book is perfect for self-study or classroom use."
&;Aaron Brown, author of Red-Blooded Risk, A World of Chance, and The Poker Face of Wall Street

"Michael B. Miller provides a very accessible ride across the daunting waters of mathematics for quantitative risk management."
&;Attilio Meucci, founder, SYMMYS

"At every turn, this book shows the relevance of mathematical and statistical concepts to risk management. They are no longer the desiccated notions found in most textbooks but assume a sense of vibrancy. So, if you're trying to hone your skills, this book is a great place to start."
&;Seeking Alpha

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.

This incisive resource:

• Covers basic statistical concepts&;from standard deviation and correlation to regression analysis and hypothesis testing
• Explores widely used risk models, including value at risk, factor analysis, Monte Carlo simulation, and stress testing
• Contains numerous sample problems and end-of-chapter exercises (with answers)
• Includes a companion website with Excel examples and templates
• Features two new chapters, which cover multivariate distributions, copulas, and Bayesian analysis

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

De la solapa interior

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.