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.

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.

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.

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

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