Image from Coce

Optimization methods in finance / Gerard Cornuejols, Reha Tütüncü.

By: Contributor(s): Material type: TextTextSeries: Mathematics, finance, and riskPublisher: Cambridge, UK ; New York : Cambridge University Press, 2007Description: xii, 345 pages : illustrations ; 26 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 0521861705
  • 9780521861700
Subject(s): DDC classification:
  • 332.015195 22
LOC classification:
  • HG106 .C67 2007
Contents:
1. Introduction -- 2. Linear programming : theory and algorithms -- 3. LP models : asset/liability cash-flow matching -- 4. LP models : asset pricing and arbitrage -- 5. Nonlinear programming : theory and algorithms -- 6. NLP models : volatility estimation -- 7. Quadratic programming : theory and algorithms -- 8. QP models : portfolio optimization -- 9. Conic optimization tools -- 10. Conic optimization models in finance -- 11. Integer programming : theory and algorithms -- 12. Integer programming models : constructing an index fund -- 13. Dynamic programming methods -- 14. DP models : option pricing -- 15. DP models : structuring asset-backed securities -- 16. Stochastic programming : theory and algorithms -- 17. Stochastic programming models : value-at-risk and conditional value-at-risk -- 18. Stochastic programming models : asset/liability management -- 19. Robust optimization : theory and tools -- 20. Robust optimization models in finance -- App. A. Convexity -- App. B. Cones -- App. C. A probability primer -- App. D. The revised simplex method.
Summary: "Optimization models play an increasingly important role in financial decisions. This is the first textbook devoted to explaining how recent advances in optimization models, methods and software can be applied to solve problems in computational finance more efficiently and accurately. Chapters discussing the theory and efficient solution methods for all major classes of optimization problems alternate with chapters illustrating their use in modeling problems of mathematical finance. The reader is guided through topics such as volatility estimation, portfolio optimization problems and constructing an index fund, using techniques such as nonlinear optimization models, quadratic programming formulations and integer programming models respectively. The book is based on Master's courses in financial engineering and comes with worked examples, exercises and case studies. It will be welcomed by applied mathematicians, operational researchers and others who work in mathematical and computational finance and who are seeking a text for self-learning or for use with courses."--Publisher description.
Tags from this library: No tags from this library for this title. Log in to add tags.
Holdings
Item type Current library Call number Copy number Status Date due Barcode
Book City Campus City Campus Main Collection 332.015195 COR (Browse shelf(Opens below)) 1 Available A374087B

Includes bibliographical references (pages 338-341) and index.

1. Introduction -- 2. Linear programming : theory and algorithms -- 3. LP models : asset/liability cash-flow matching -- 4. LP models : asset pricing and arbitrage -- 5. Nonlinear programming : theory and algorithms -- 6. NLP models : volatility estimation -- 7. Quadratic programming : theory and algorithms -- 8. QP models : portfolio optimization -- 9. Conic optimization tools -- 10. Conic optimization models in finance -- 11. Integer programming : theory and algorithms -- 12. Integer programming models : constructing an index fund -- 13. Dynamic programming methods -- 14. DP models : option pricing -- 15. DP models : structuring asset-backed securities -- 16. Stochastic programming : theory and algorithms -- 17. Stochastic programming models : value-at-risk and conditional value-at-risk -- 18. Stochastic programming models : asset/liability management -- 19. Robust optimization : theory and tools -- 20. Robust optimization models in finance -- App. A. Convexity -- App. B. Cones -- App. C. A probability primer -- App. D. The revised simplex method.

"Optimization models play an increasingly important role in financial decisions. This is the first textbook devoted to explaining how recent advances in optimization models, methods and software can be applied to solve problems in computational finance more efficiently and accurately. Chapters discussing the theory and efficient solution methods for all major classes of optimization problems alternate with chapters illustrating their use in modeling problems of mathematical finance. The reader is guided through topics such as volatility estimation, portfolio optimization problems and constructing an index fund, using techniques such as nonlinear optimization models, quadratic programming formulations and integer programming models respectively. The book is based on Master's courses in financial engineering and comes with worked examples, exercises and case studies. It will be welcomed by applied mathematicians, operational researchers and others who work in mathematical and computational finance and who are seeking a text for self-learning or for use with courses."--Publisher description.

Machine converted from AACR2 source record.

There are no comments on this title.

to post a comment.

Powered by Koha