Mathematics and Statistics for Financial Risk Management
- 4h 3m
- Michael B. Miller
- John Wiley & Sons (US)
- 2012
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 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.
- 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 topics
If you're looking for a book that will help you understand the mathematics and statistics of financial risk management, look no further.
About the Author
Michael B. Miller studied economics at the American University of Paris and the University of Oxford before starting a career in finance. He has worked in risk management for more than ten years, most recently as the chief risk officer for a hedge fund in New York City.
In this Book
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Some Basic Math
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Probabilities
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Basic Statistics
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Distributions
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Hypothesis Testing & Confidence Intervals
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Matrix Algebra
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Vector Spaces
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Linear Regression Analysis
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Time Series Models
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Decay Factors
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Answers
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References
