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Financial Risk Modelling and Portfolio Optimization with R, by Bernhard Pfaff
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Introduces the latest techniques advocated for measuring financial market risk and portfolio optimization, and provides a plethora of R code examples that enable the reader to replicate the results featured throughout the book.
Financial Risk Modelling and Portfolio Optimization with R:
- Demonstrates techniques in modelling financial risks and applying portfolio optimization techniques as well as recent advances in the field.
- Introduces stylized facts, loss function and risk measures, conditional and unconditional modelling of risk; extreme value theory, generalized hyperbolic distribution, volatility modelling and concepts for capturing dependencies.
- Explores portfolio risk concepts and optimization with risk constraints.
- Enables the reader to replicate the results in the book using R code.
- Is accompanied by a supporting website featuring examples and case studies in R.
Graduate and postgraduate students in finance, economics, risk management as well as practitioners in finance and portfolio optimization will find this book beneficial. It also serves well as an accompanying text in computer-lab classes and is therefore suitable for self-study.�
- Sales Rank: #1163528 in Books
- Published on: 2013-01-22
- Original language: English
- Number of items: 1
- Dimensions: 9.40" h x .94" w x 6.40" l, 1.52 pounds
- Binding: Hardcover
- 374 pages
Most helpful customer reviews
31 of 32 people found the following review helpful.
Appealing
By Dimitri Shvorob
I like the book and go with four stars for three reasons
* Exploitative pricing. It's a $40 book, the rest is "finance" and "R" premia. Don't push it, Wiley.
* Somewhat lacking editing. The author has done a good job, but blemishes remain. Two easy-to-spot examples are the ugly typesetting of code snippets and the where-did-this-come-from Section 2.4.
* The book takes R very seriously, but the author's choice to give complete listings means that much of the code on display is data manipulation; for "non-central" tasks, you only get a lead to a relevant package or function, not an actual usage example.
After brief introductions to R, financial time series, risk measures and mean-variance portfolio optimization, the book explores four subjects.
(70 pages) Approximation of stock-return distributions, primarily via the trio of generalized lambda, generalized hyperbolic and generalized extreme-value distributions.
(50 pages) The GARCH-copula returns-dependence model, whose discussion is preceded by discussion of GARCH and copulae.
(100 pages) Construction of a range of "optimal" portfolios, including a tweak on the Markowitz problem and recipes optimizing over alternative risk measures like VaR, CVaR, expected shortfall, drawdown, and (loosely) relative-to-benchmark loss.
(40 pages) Two variations on the Black-Litterman model, one due to Meucci (and implemented in R package BLCOP) and another proposed by the author. (This material is preceded by a survey of Box-Jenkins time-series models).
R users will benefit the most, but the book has got to be appreciated by quantitative risk managers of all statistical-tool persuasions. "Financial risk modeling and portfolio optimization with R" is a credible, practical, does-what-it-says-on-the-tin book.
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