Financial Risk Forecasting: The Theory and Practice of Forecasting Market Risk, with Implementation in R and MATLAB
Language: English
Published by Wiley, 2012
- Hardcover
- Used
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Add to basketItem description from seller
273pp, large octavo, hc w/jacket, tight binding, clean pages, clean boards with sharp corners, clean and glossy jacket.
Seller Inventory # 136775
- Title
- Financial Risk Forecasting: The Theory and Practice of Forecasting Market Risk, with Implementation in R and MATLAB
- Author
- DANIELSSON, Jon
- Publisher
- Wiley
- Publication year
- 2012
- Condition
- Fine
- Dust jacket
- Fine
- Binding
- HARDCOVER
- Language
- English
- ISBN 10
- 0470669438
- ISBN 13
- 9780470669433
- Edition
- 2nd printing.
- Seller catalogs
- FINANCE
Written by renowned risk expert Jon Danielsson, the book begins with an introduction to financial markets and market prices, volatility clusters, fat tails and nonlinear dependence. It then goes on to present volatility forecasting with both univatiate and multivatiate methods, discussing the various methods used by industry, with a special focus on the GARCH family of models. The evaluation of the quality of forecasts is discussed in detail. Next, the main concepts in risk and models to forecast risk are discussed, especially volatility, value-at-risk and expected shortfall. The focus is both on risk in basic assets such as stocks and foreign exchange, but also calculations of risk in bonds and options, with analytical methods such as delta-normal VaR and duration-normal VaR and Monte Carlo simulation. The book then moves on to the evaluation of risk models with methods like backtesting, followed by a discussion on stress testing. The book concludes by focussing on the forecasting of risk in very large and uncommon events with extreme value theory and considering the underlying assumptions behind almost every risk model in practical use – that risk is exogenous – and what happens when those assumptions are violated.
Every method presented brings together theoretical discussion and derivation of key equations and a discussion of issues in practical implementation. Each method is implemented in both MATLAB and R, two of the most commonly used mathematical programming languages for risk forecasting with which the reader can implement the models illustrated in the book.
The book includes four appendices. The first introduces basic concepts in statistics and financial time series referred to throughout the book. The second and third introduce R and MATLAB, providing a discussion of the basic implementation of the software packages. And the final looks at the concept of maximum likelihood, especially issues in implementation and testing.
The book is accompanied by a website - www.financialriskforecasting.com – which features downloadable code as used in the book.
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About the Author
"About the title" may belong to another edition of this title.
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