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Optimization Methods in Finance

2nd Edition

$64.99 (P)

  • Date Published: September 2018
  • availability: In stock
  • format: Hardback
  • isbn: 9781107056749

$ 64.99 (P)
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  • Optimization methods play a central role in financial modeling. This textbook is devoted to explaining how state-of-the-art optimization theory, algorithms, and software can be used to efficiently solve problems in computational finance. It discusses some classical mean–variance portfolio optimization models as well as more modern developments such as models for optimal trade execution and dynamic portfolio allocation with transaction costs and taxes. Chapters discussing the theory and efficient solution methods for the main classes of optimization problems alternate with chapters discussing their use in the modeling and solution of central problems in mathematical finance. This book will be interesting and useful for students, academics, and practitioners with a background in mathematics, operations research, or financial engineering. The second edition includes new examples and exercises as well as a more detailed discussion of mean–variance optimization, multi-period models, and additional material to highlight the relevance to finance.

    • Numerous examples, exercises, and case studies allow the reader to easily test, experiment, and extend the concepts and models discussed in the main text
    • Links together the two important disciplines of optimization and finance, benefiting both
    • Technical content gives the reader a solid foundation in the main methods
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    Reviews & endorsements

    Review of first edition: 'This book will be useful as a textbook for students in financial engineering at the MS level. … The book will also be of interest to researchers and graduate students in optimization who are interested in applications of optimization to financial problems.' Brian Borchers, Journal of Online Mathematics and its Applications

    Review of first edition: 'This book would certainly appeal to someone with a mathematical background, perhaps in operations research, wishing to update and apply their knowledge to the financial world.' Mathematics TODAY

    Review of first edition: 'Until now, there has been no comprehensive optimization book aimed at quantitative analysts in the financial industry. The book by Cornuejols and Tutuncu fills this void … an excellent source for quantitative financial analysts and graduate students to learn about basic optimization theory, computational methods, and available software. At the same time, it can be used by academic researchers and students in optimization as an introduction to various interesting problems in financial applications.' International Review of Economics & Finance

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    Product details

    • Edition: 2nd Edition
    • Date Published: September 2018
    • format: Hardback
    • isbn: 9781107056749
    • length: 348 pages
    • dimensions: 253 x 178 x 21 mm
    • weight: 0.84kg
    • contains: 34 b/w illus. 125 exercises
    • availability: In stock
  • Table of Contents

    Part I. Introduction:
    1. Overview of optimization models
    2. Linear programming: theory and algorithms
    3. Linear programming models: asset-liability management
    4. Linear programming models: arbitrage and asset pricing
    Part II. Single-Period Models:
    5. Quadratic programming: theory and algorithms
    6. Quadratic programming models: mean-variance optimization
    7. Sensitivity of mean-variance models to input estimation
    8. Mixed integer programming: theory and algorithms
    9. Mixed integer programming models: portfolios with combinatorial constraints
    10. Stochastic programming: theory and algorithms
    11. Stochastic programming models: risk measures
    Part III. Multi-Period Models:
    12. Multi-period models: simple examples
    13. Dynamic programming: theory and algorithms
    14. Dynamic programming models: multi-period portfolio optimization
    15. Dynamic programming models: the binomial pricing model
    16. Multi-stage stochastic programming
    17. Stochastic programming models: asset-liability management
    Part IV. Other Optimization Techniques:
    18. Conic programming: theory and algorithms
    19. Robust optimization
    20. Nonlinear programming: theory and algorithms
    Appendix
    References
    Index.

  • Resources for

    Optimization Methods in Finance

    Gérard Cornuéjols, Javier Peña, Reha Tütüncü

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  • Authors

    Gérard Cornuéjols, Carnegie Mellon University, Pennsylvania
    Gérard Cornuéjols is a Professor of Operations Research at the Tepper School of Business, Carnegie Mellon University, Pennsylvania. He is a member of the National Academy of Engineering and has received numerous prizes for his research contributions in integer programming and combinatorial optimization, including the Lanchester Prize, the Fulkerson Prize, the Dantzig Prize, and the von Neumann Theory Prize.

    Javier Peña, Carnegie Mellon University, Pennsylvania
    Javier Peña is a Professor of Operations Research at the Tepper School of Business, Carnegie Mellon University, Pennsylvania. His research explores the myriad of challenges associated with large-scale optimization models and he has published numerous articles on optimization, machine learning, financial engineering, and computational game theory. His research has been supported by grants from the National Science Foundation, including a prestigious CAREER award.

    Reha Tütüncü, SECOR Asset Management
    Reha Tütüncü is the Chief Risk Officer at SECOR Asset Management and an adjunct professor at Carnegie Mellon University, Pennsylvania. He has previously held senior positions at Goldman Sachs Asset Management and AQR Capital Management focusing on quantitative portfolio construction, equity portfolio management, and risk management.

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