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. Presents recent progress of stochastic portfolio optimization with exotic filtrations Shows you how to apply the tools of the enlargement of filtrations to resolve the optimization problem Uses tools from various fields from enlargement of filtration theory, stochastic calculus, convex analysis, optimal stochastic control, and backward stochastic differential equations
In this thesis, we analyze various problems of dynamic portfolio optimization as well as green capital requirements under risk constraints and incomplete information. First, we examine the problem of optimal expected utility under the constraint of a utility-based shortfall risk measure in an incomplete market. The existence and uniqueness of an optimal solution to the problem are shown using a Lagrange multiplier and duality methods. Second, we consider the optimization problem under various levels of the investor’s information. By using martingale representation theorems, we demonstrate the existence and uniqueness of optimal solutions, which differ in their market dynamics. Third, we analyze the effects of green- and brownwashing on banks’ lending to firms, on the regulator’s deposit insurance subsidy, and on carbon emissions under different green capital requirement functions. Furthermore, we show that green capital requirements may compromise financial stability.
This volume is an original collection of articles by 44 leading mathematicians on the theme of the future of the discipline. The contributions range from musings on the future of specific fields, to analyses of the history of the discipline, to discussions of open problems and conjectures, including first solutions of unresolved problems. Interestingly, the topics do not cover all of mathematics, but only those deemed most worthy to reflect on for future generations. These topics encompass the most active parts of pure and applied mathematics, including algebraic geometry, probability, logic, optimization, finance, topology, partial differential equations, category theory, number theory, differential geometry, dynamical systems, artificial intelligence, theory of groups, mathematical physics and statistics.
This volume presents classical results of the theory of enlargement of filtration. The focus is on the behavior of martingales with respect to the enlarged filtration and related objects. The study is conducted in various contexts including immersion, progressive enlargement with a random time and initial enlargement with a random variable. The aim of this book is to collect the main mathematical results (with proofs) previously spread among numerous papers, great part of which is only available in French. Many examples and applications to finance, in particular to credit risk modelling and the study of asymmetric information, are provided to illustrate the theory. A detailed summary of further connections and applications is given in bibliographic notes which enables to deepen study of the topic. This book fills a gap in the literature and serves as a guide for graduate students and researchers interested in the role of information in financial mathematics and in econometric science. A basic knowledge of the general theory of stochastic processes is assumed as a prerequisite.
In Portfolio Management , Shan Rajegopal, a leading authority on innovation and project portfolio management, provides an integrated project portfolio management framework which links innovation, investment and implementation. A successful tried and tested method, this blueprint will be a hands-on guide for business executives.
Praise for IT Portfolio Management Step-by-Step "Bryan Maizlish and Robert Handler bring their deep experience in IT 'value realization' to one of the most absent of all IT management practices--portfolio management. They capture the essence of universally proven investment practices and apply them to the most difficult of challenges--returning high strategic and dollar payoffs from an enterprise's IT department. The reader will find many new and rewarding insights to making their IT investments finally return market leading results." --John C. Reece, Chairman and CEO, John C. Reece & Associates, LLC Former deputy commissioner for modernization and CIO of the IRS "IT Portfolio Management describes in great detail the critical aspects, know-how, practical examples, key insights, and best practices to improve operational efficiency, corporate agility, and business competitiveness. It eloquently illustrates the methods of building and integrating a portfolio of IT investments to ensure the realization of maximum value and benefit, and to fully leverage the value of all IT assets. Whether you are getting started or building on your initial success in IT portfolio management, this book will provide you information on how to build and implement an effective IT portfolio management strategy." --David Mitchell, President and CEO, webMethods, Inc. "I found IT Portfolio Management very easy to read, and it highlights many of the seminal aspects and best practices from financial portfolio management. It is an important book for executive, business, and IT managers." --Michael J. Montgomery, President, Montgomery & Co. "IT Portfolio Management details a comprehensive framework and process showing how to align business and IT for superior value. Maizlish and Handler have the depth of experience, knowledge, and insight needed to tackle the challenges and opportunities companies face in optimizing their IT investment portfolios. This is an exceptionally important book for executive leadership and IT business managers, especially those wanting to build a process-managed enterprise." --Peter Fingar, Executive Partner Greystone Group, coauthor of The Real-Time Enterprise and Business Process Management (BPM): The Third Wave "A must-read for the non-IT manager who needs to understand the complexity and challenges of managing an IT portfolio. The portfolio management techniques, analysis tools, and planning can be applied to any project or function." --Richard "Max" Maksimoski, Senior Director R&D, The Scotts Company "This book provides an excellent framework and real-world based approach for implementing IT portfolio management. It is a must-read for every CIO staff considering how to strategically and operationally impact their company's bottom line." --Donavan R. Hardenbrook, New Product Development Professional, Intel Corporation
Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and return forecasts and more complex constraints. Trading algorithms use AI to devise novel trading signals and execute trades with lower transaction costs. AI also improves risk modeling and forecasting by generating insights from new data sources. Finally, robo-advisors owe a large part of their success to AI techniques. Yet the use of AI can also create new risks and challenges, such as those resulting from model opacity, complexity, and reliance on data integrity.
Handbook of Statistical Analysis: AI and ML Applications, third edition, is a comprehensive introduction to all stages of data analysis, data preparation, model building, and model evaluation. This valuable resource is useful to students and professionals across a variety of fields and settings: business analysts, scientists, engineers, and researchers in academia and industry. General descriptions of algorithms together with case studies help readers understand technical and business problems, weigh the strengths and weaknesses of modern data analysis algorithms, and employ the right analytical methods for practical application. This resource is an ideal guide for users who want to address massive and complex datasets with many standard analytical approaches and be able to evaluate analyses and solutions objectively. It includes clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques; offers accessible tutorials; and discusses their application to real-world problems. - Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data analytics to build successful predictive analytic solutions - Provides in-depth descriptions and directions for performing many data preparation operations necessary to generate data sets in the proper form and format for submission to modeling algorithms - Features clear, intuitive explanations of standard analytical tools and techniques and their practical applications - Provides a number of case studies to guide practitioners in the design of analytical applications to solve real-world problems in their data domain - Offers valuable tutorials on the book webpage with step-by-step instructions on how to use suggested tools to build models - Provides predictive insights into the rapidly expanding "Intelligence Age" as it takes over from the "Information Age," enabling readers to easily transition the book's content into the tools of the future
This book presents solutions to the general problem of single period portfolio optimization. It introduces different linear models, arising from different performance measures, and the mixed integer linear models resulting from the introduction of real features. Other linear models, such as models for portfolio rebalancing and index tracking, are also covered. The book discusses computational issues and provides a theoretical framework, including the concepts of risk-averse preferences, stochastic dominance and coherent risk measures. The material is presented in a style that requires no background in finance or in portfolio optimization; some experience in linear and mixed integer models, however, is required. The book is thoroughly didactic, supplementing the concepts with comments and illustrative examples.
