國立虎尾科技大學 |

Optimization methods in finance /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Optimization methods in finance // Gerard Cornuejols, Reha Tütüncü.
作者:	Cornuejols, Gerard,
其他作者:	Tütüncü, Reha,
面頁冊數:	1 online resource (xii, 345 pages) :digital, PDF file(s). :
附註:	Title from publisher's bibliographic system (viewed on 05 Oct 2015).
標題:	Mathematical optimization. -
電子資源:	https://doi.org/10.1017/CBO9780511753886
ISBN:	9780511753886 (ebook)

Optimization methods in finance /
Cornuejols, Gerard,1950-

Optimization methods in finance /Gerard Cornuejols, Reha Tütüncü. - 1 online resource (xii, 345 pages) :digital, PDF file(s). - Mathematics, finance, and risk ;5. - Mathematics, finance, and risk ;5..

Title from publisher's bibliographic system (viewed on 05 Oct 2015).

Introduction -- Linear programming : theory and algorithms -- LP models : asset/liability cash-flow matching -- LP models : asset pricing and arbitage -- Nonlinear programming : theory and algorithms -- NLP models : volatility estimation -- Quadratic programming : theory and algorithms -- QP models : portfolio optimization -- Conic optimization tools -- Conic optimization models in finance -- Integer programming : theory and algorithms -- Integer programming models : constructing an index fund -- Dynamic programming methods -- DP models : option pricing -- DP models : structuring asset-backed securities -- Stochastic programming : theory and algorithms -- Stochastic programming models : value-at-risk and conditional value-at-risk -- Stochastic programming models : asset/liability management -- Robust optimization : theory and tools -- Robust optimization models in finance.

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.

ISBN: 9780511753886 (ebook)Subjects--Topical Terms:

527675
Mathematical optimization.

LC Class. No.: HG106 / .C67 2007

Dewey Class. No.: 332.01/5195

Optimization methods in finance /
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505 0 $a Introduction -- Linear programming : theory and algorithms -- LP models : asset/liability cash-flow matching -- LP models : asset pricing and arbitage -- Nonlinear programming : theory and algorithms -- NLP models : volatility estimation -- Quadratic programming : theory and algorithms -- QP models : portfolio optimization -- Conic optimization tools -- Conic optimization models in finance -- Integer programming : theory and algorithms -- Integer programming models : constructing an index fund -- Dynamic programming methods -- DP models : option pricing -- DP models : structuring asset-backed securities -- Stochastic programming : theory and algorithms -- Stochastic programming models : value-at-risk and conditional value-at-risk -- Stochastic programming models : asset/liability management -- Robust optimization : theory and tools -- Robust optimization models in finance.
520 $a 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.
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