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Portfolio optimization with differen...

Hillairet, Caroline,

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Portfolio optimization with different information flow /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Portfolio optimization with different information flow // Caroline Hillairet, Ying Jiao.
作者:	Hillairet, Caroline,
其他作者:	Jiao, Ying,
面頁冊數:	1 online resource (192 pages).
內容註:	Front Cover ; Portfolio Optimization with Different Information Flow; Copyright; Contents; Introduction; Acknowledgments; 1. Optimization Problems; 1.1. Portfolio optimization problem; 1.2. Duality approach; 1.3. Dynamic programming principle; 1.4. Several explicit examples; 1.5. Brownian-Poisson filtration with general utility weights; 2. Enlargement of Filtration; 2.1. Conditional law and density hypothesis; 2.2. Initial enlargement of filtration; 2.3. Progressive enlargement of filtration; 3. Portfolio Optimization with Credit Risk; 3.1. Model setup.
內容註:	3.2. Direct method with the logarithmic utility3.3. Optimization for standard investor: power utility; 3.4. Decomposition method with the exponential utility; 3.5. Optimization with insider's information; 3.6. Numerical illustrations; 4. Portfolio Optimization with Information Asymmetry; 4.1. The market; 4.2. Optimal strategies in some examples of side-information; 4.3. Numerical illustrations; Bibliography; Index; Back Cover.
標題:	Portfolio management. -
電子資源:	https://www.sciencedirect.com/science/book/9781785480843
ISBN:	9780081011775

Portfolio optimization with different information flow /
Hillairet, Caroline,

Portfolio optimization with different information flow /Caroline Hillairet, Ying Jiao. - 1 online resource (192 pages). - Optimization in insurance and finance set. - Optimization in insurance and finance set..

Includes bibliographical references (pages 165-173) and index.

Front Cover ; Portfolio Optimization with Different Information Flow; Copyright; Contents; Introduction; Acknowledgments; 1. Optimization Problems; 1.1. Portfolio optimization problem; 1.2. Duality approach; 1.3. Dynamic programming principle; 1.4. Several explicit examples; 1.5. Brownian-Poisson filtration with general utility weights; 2. Enlargement of Filtration; 2.1. Conditional law and density hypothesis; 2.2. Initial enlargement of filtration; 2.3. Progressive enlargement of filtration; 3. Portfolio Optimization with Credit Risk; 3.1. Model setup.

Portfolio Optimization with Different Information Flow recalls the stochastic tools and results concerning the stochastic optimization theory and the enlargement filtration theory.The authors apply the theory of the enlargement of filtrations and solve the optimization problem. Two main types of enlargement of filtration are discussed: initial and progressive, using tools from various fields, such as from stochastic calculus and convex analysis, optimal stochastic control and backward stochastic differential equations. This theoretical and numerical analysis is applied in different market settings to provide a good basis for the understanding of portfolio optimization with different information flow.

ISBN: 9780081011775Subjects--Topical Terms:

646616
Portfolio management.
Index Terms--Genre/Form:

542853
Electronic books.

LC Class. No.: HG4529.5

Dewey Class. No.: 332.67/8

Portfolio optimization with different information flow /
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505 8 $a 3.2. Direct method with the logarithmic utility3.3. Optimization for standard investor: power utility; 3.4. Decomposition method with the exponential utility; 3.5. Optimization with insider's information; 3.6. Numerical illustrations; 4. Portfolio Optimization with Information Asymmetry; 4.1. The market; 4.2. Optimal strategies in some examples of side-information; 4.3. Numerical illustrations; Bibliography; Index; Back Cover.
520 $a Portfolio Optimization with Different Information Flow recalls the stochastic tools and results concerning the stochastic optimization theory and the enlargement filtration theory.The authors apply the theory of the enlargement of filtrations and solve the optimization problem. Two main types of enlargement of filtration are discussed: initial and progressive, using tools from various fields, such as from stochastic calculus and convex analysis, optimal stochastic control and backward stochastic differential equations. This theoretical and numerical analysis is applied in different market settings to provide a good basis for the understanding of portfolio optimization with different information flow.
650 0 $a Portfolio management. $3 646616
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