Dynamic Portfolio Strategies: quantitative methods and empirical rules for incomplete information
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Dynamic Portfolio Strategies: quantitative methods and empirical rules for incomplete information

Quantitative Methods and Empirical Rules for Incomplete Information
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Nikolai Dokuchaev
522 g
242x165x19 mm
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List of Figures. List of Tables. Acknowledgments. Introduction.
Part I: Background. 1. Stochastic Market Model.
Part II: Model-free Empirical Strategies and Their Evaluation. 2. Two Empirical Model-Free `Winning' Strategies and Their Statistical Evaluation. 3. Strategies for Investment in Options. 4. Continuous-Time Analogs of `Winning' Strategies and Asymptotic Arbitrage.
Part III: Optimal Strategies for the Diffusion Market Model with Observable Parameters. 5. Optimal Strategies with Direct Observation of Parameters. 6. Optimal Portfolio Compression. 7. Maximin Criterion for Observable But Nonpredictable Parameters.
Part IV: Optimal Strategies Based on Historical Data for Markets with Nonobservable Parameters. 8. Strategies Based on Historical Prices and Volume: Existence Result. 9. Solution for Log and Power Utilities With Historical Prices and Volume. 10. Solution for General Utilities and Constraints Via Parabolic Equations. 11. Special Cases and Examples: Replicating with Gap and Goal Achieving. 12. Unknown Distribution: Maximin Criterion and Duality Approach. 13. On Replication of Claims.
References. Index.
Dynamic Portfolio Strategies: Quantitative Methods and Empirical Rules for Incomplete Information investigates optimal investment problems for stochastic financial market models. It is addressed to academics and students who are interested in the mathematics of finance, stochastic processes, and optimal control, and also to practitioners in risk management and quantitative analysis who are interested in new strategies and methods of stochastic analysis.While there are many works devoted to the solution of optimal investment problems for various models, the focus of this book is on analytical strategies based on "technical analysis" which are model-free. The technical analysis of these strategies has a number of characteristics. Two of the more important characteristics are: (1) they require only historical data, and (2) typically they are more widely used by traders than analysis based on stochastic models. Hence it is the objective of this book to reduce the gap between model-free strategies and strategies that are "optimal" for stochastic models. We hope that researchers, students and practitioners will be interested in some of the new empirically based methods of "technical analysis" strategies suggested in this book and evaluated via stochastic market models.