Editor Stephen Satchell brings us a book that truly lives up to its title: optimizing optimization by taking the lessons learned about the failures of portfolio optimization from the credit crisis and collecting them into one book, providing a variety of perspectives from the leaders in both industry and academia on how to solve these problems both in theory and in practice. Industry leaders are invited to present chapters that explain how their new breed of optimization software addresses the faults of previous versions. Software vendors present their best of breed optimization software, demonstrating how it addresses the faults of the credit crisis. Cutting-edge academic articles complement the commercial applications to provide a well-rounded insight into the current landscape of portfolio optimization. Optimization is the holy grail of portfolio management, creating a portfolio in which return is highest in light of the risk the client is willing to take. Portfolio optimization has been done by computer modeling for over a decade, and several leading software companies make a great deal of money by selling optimizers to investment houses and hedge funds. Hedge funds in particular were enamored of heavily computational optimizing software, and many have been burned when this software did not perform as, er, expected during the market meltdown. The software providers are currently reworking their software to address any shortcomings that became apparent during the meltdown, and are eager for a forum to address their market and have the space to describe in detail how their new breed of software can manage not only the meltdown problems but also perform faster and better than ever before-that is, optimizing the optimizers!! In addition, there is a strong line of serious well respected research on portfolio optimization coming from the academic side of the finance world. Many different academic approaches have appeared toward optimization: some favor stochastic methods, others numerical methods, others heuristic methods. All focus on the same issues of optimizing performance at risk levels. This book will provide the forum that the software vendors are looking for to showcase their new breed of software. It will also provide a forum for the academics to showcase their latest research. It will be a must-read book for portfolio managers who need to know whether their current optimization software provider is up to snuff compared to the competition, whether they need to move to a competitor product, whether they need to be more aware of the cutting-edge academic research as well. This work: 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; and, accentuates real applications, not laboratory results.
Sect. 1 Practitioners and Products 1
1 Robust portfolio optimization using second-order cone programming by Fiona Kolbert and Laurence Wormald 3
2 Novel approaches to portfolio construction: multiple risk models and multisolution generation by Sebastian Ceria and Francois Margot and Anthony Renshaw and Anureet Saxena 23
3 Optimal solutions for optimization in practice by Daryl Roxburgh and Katja Scherer and Tim Matthews 53
4 The Windham Portfolio Advisor by Mark Kritzman 93
Sect. 2 Theory 115
5 Modeling, estimation, and optimization of equity portfolios with heavy-tailed distributions by Almira Biglova and Sergio Ortobelli and Svetlozar Rachev and Frank J. Fabozzi 117
6 Staying ahead on downside risk by Giuliano De Rossi 143
7 Optimization and portfolio selection by Hal Forsey and Frank Sortino 161
8 Computing optimal mean/downside risk frontiers: the role of ellipticity by Tony Hall and Stephen E. Satchell 179
9 Portfolio optimization with Threshold Accepting : a practical guide by Manfred Gilli and Enrico Schumann 201
10 Some properties of averaging simulated optimization methods by John Knight and Stephen E. Satchell 225
11 Heuristic portfolio optimization: Bayesian updating with the Johnson family of distributions by Richard Louth 247
12 More than you ever wanted to know about conditional value at risk optimization by Bernd Scherer 283
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