Optimizing Optimization
1st Edition
The Next Generation of Optimization Applications and Theory
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Table of Contents
Optimizing Optimization
Stephen Satchell
Section 1: Practitioners and Products
Chapter 1: Robust Portfolio Optimization Using Second Order Cone Programming
Fiona Kolbert and Laurence Wormald
Chapter 2: Novel Approaches to Portfolio Construction: Multiple Risk Models and Multi-Solution Generation
Sebastian Ceria, Francis Margot, Anthony Renshaw, and Anureet Saxena
Chapter 3: Bitter Lessons Learned from Practical Optimization or A Holding Hand Through the Dark Valley of Infeasibility
Daryl Roxburgh, Katja Scherer, and Tim Matthews
Chapter 4: The Windham Portfolio Advisor
Mark Kritzman
Section 2: Theory
Chapter 5: Modeling, Estimation, and Optimization of Equity Portfolios with Heavy-tailed Distributions
Amira Biglova, Sergio Ortobelli, Svetlozar Rachev, and Frank J. Fabozzi
Chapter 6: Staying Ahead on Downside Risk
Giuliano De Rossi
Chapter 7: Optimization and Portfolio Selection
Hal Forsey and Frank Sortino
Chapter 8: Computing Optimal Mean/Downside Risk Frontiers: the Role of Ellipticity
A.D. Hall and Stephen Satchell
Chapter 9: Portfolio Optimization with ‘Threshold Accepting’: A Practical Guide
Manfred Gilli and Enrico Schumann
Chapter 10: Some Properties Averaging Simulated Optimization Methods
J. Knight and Stephen Satchell
Chapter 11: Heuristic Portfolio Optimization: Bayesian Updating with the Johnson Family of Distributions
Richard Louth
Chapter 12: More Than You Ever Wanted to Know about Conditional Value at Risk-Optimization
Bernd Scherer
Description
The practical aspects of optimization rarely receive global, balanced examinations. Stephen Satchell’s nuanced assembly of technical presentations about optimization packages (by their developers) and about current optimization practice and theory (by academic researchers) makes available highly practical solutions to our post-liquidity bubble environment. The commercial chapters emphasize algorithmic elements without becoming sales pitches, and the academic chapters create context and explore development opportunities. Together they offer an incisive perspective that stretches toward new products, new techniques, and new answers in quantitative finance.
Key Features
- Presents a unique "confrontation" between software engineers and academics
- Highlights a global view of common optimization issues
- Emphasizes the research and market challenges of optimization software while avoiding sales pitches
- Accentuates real applications, not laboratory results
Readership
• Portfolio managers in buy-side firms (hedge funds, mutual funds, pension funds) and investment houses
• CTOs who make purchasing decisions for financial optimization software.
• Research staff at top quantitative investing companies like BGI and SSgA.
• Masters and PhD students in financial engineering programs worldwide.
Details
- No. of pages:
- 328
- Language:
- English
- Copyright:
- © Academic Press 2009
- Published:
- 19th September 2009
- Imprint:
- Academic Press
- eBook ISBN:
- 9780080959207
- Hardcover ISBN:
- 9780123749529
Ratings and Reviews
About the Author
Stephen Satchell
Stephen Satchell is a Fellow of Trinity College, the Reader in Financial Econometrics at the University of Cambridge and Visiting Professor at Birkbeck College, City University Business School and University of Technology, Sydney. He provides consultancy for a range of city institutions in the broad area of quantitative finance. He has published papers in many journals and has a particular interest in risk.
Affiliations and Expertise
Consultant to financial institutions and Reader in Financial Econometrics at Trinity College, Cambridge, Stephen Satchell is Editor-in-Chief of the Journal of Asset Management and Derivatives, Use, Trading, and Regulation. He has edited or authored over 20 books on finance.
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