Optimizing optimization the next generation of optimization applications and theory / [electronic resource] :

• 其他作者：
  • Satchell, S. (Stephen)
• 其他題名：
  • Quantitative finance series
• 出版： Amsterdam ;Boston : Academic Press
• 叢書名： Quantitative finance series
• 主題： Portfolio management--Data processing , Portfolio management--Mathematical models , BUSINESS & ECONOMICS--Investments & Securities--General. , Electronic books.
• ISBN： 9780123749529 、 0123749522 、 9780080959207 (electronic bk.) 、 0080959202 (electronic bk.)
• FIND@SFXID: CGU
• 資料類型: 電子書
• 內容註: Includes bibliographical references and index. 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.
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• 摘要註: 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 m
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