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Optimizing Optimization - 1st Edition - ISBN: 9780123749529, 9780080959207

Optimizing Optimization

1st Edition

The Next Generation of Optimization Applications and Theory

Authors: Stephen Satchell
eBook ISBN: 9780080959207
Hardcover ISBN: 9780123749529
Imprint: Academic Press
Published Date: 8th October 2009
Page Count: 328
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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 2010
Published:
Imprint:
Academic Press
eBook ISBN:
9780080959207
Hardcover ISBN:
9780123749529

About the Authors

Stephen Satchell Author

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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