- 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
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.
• 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.
Optimizing Optimization, 1st Edition
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
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
Chapter 12: More Than You Ever Wanted to Know about Conditional Value at Risk-Optimization