Stochastic Optimization Models In Finance
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Stochastic Optimization Models In Finance
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Author : W. T. Ziemba
language : en
Publisher: Academic Press
Release Date : 2014-05-12
Stochastic Optimization Models In Finance written by W. T. Ziemba and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-05-12 with Business & Economics categories.
Stochastic Optimization Models in Finance focuses on the applications of stochastic optimization models in finance, with emphasis on results and methods that can and have been utilized in the analysis of real financial problems. The discussions are organized around five themes: mathematical tools; qualitative economic results; static portfolio selection models; dynamic models that are reducible to static models; and dynamic models. This volume consists of five parts and begins with an overview of expected utility theory, followed by an analysis of convexity and the Kuhn-Tucker conditions. The reader is then introduced to dynamic programming; stochastic dominance; and measures of risk aversion. Subsequent chapters deal with separation theorems; existence and diversification of optimal portfolio policies; effects of taxes on risk taking; and two-period consumption models and portfolio revision. The book also describes models of optimal capital accumulation and portfolio selection. This monograph will be of value to mathematicians and economists as well as to those interested in economic theory and mathematical economics.
Stochastic Optimization Models In Finance
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Author : W. T. Ziemba (Comp)
language : en
Publisher:
Release Date : 1975
Stochastic Optimization Models In Finance written by W. T. Ziemba (Comp) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1975 with Finance categories.
Stochastic Programming
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Author : Horand Gassmann
language : en
Publisher: World Scientific
Release Date : 2013
Stochastic Programming written by Horand Gassmann and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Business & Economics categories.
This book shows the breadth and depth of stochastic programming applications. All the papers presented here involve optimization over the scenarios that represent possible future outcomes of the uncertainty problems. The applications, which were presented at the 12th International Conference on Stochastic Programming held in Halifax, Nova Scotia in August 2010, span the rich field of uses of these models. The finance papers discuss such diverse problems as longevity risk management of individual investors, personal financial planning, intertemporal surplus management, asset management with benchmarks, dynamic portfolio management, fixed income immunization and racetrack betting. The production and logistics papers discuss natural gas infrastructure design, farming Atlantic salmon, prevention of nuclear smuggling and sawmill planning. The energy papers involve electricity production planning, hydroelectric reservoir operations and power generation planning for liquid natural gas plants. Finally, two telecommunication papers discuss mobile network design and frequency assignment problems.
Decision Making Under Uncertainty In Financial Markets
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Author : Jonas Ekblom
language : en
Publisher: Linköping University Electronic Press
Release Date : 2018-09-13
Decision Making Under Uncertainty In Financial Markets written by Jonas Ekblom and has been published by Linköping University Electronic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-13 with categories.
This thesis addresses the topic of decision making under uncertainty, with particular focus on financial markets. The aim of this research is to support improved decisions in practice, and related to this, to advance our understanding of financial markets. Stochastic optimization provides the tools to determine optimal decisions in uncertain environments, and the optimality conditions of these models produce insights into how financial markets work. To be more concrete, a great deal of financial theory is based on optimality conditions derived from stochastic optimization models. Therefore, an important part of the development of financial theory is to study stochastic optimization models that step-by-step better capture the essence of reality. This is the motivation behind the focus of this thesis, which is to study methods that in relation to prevailing models that underlie financial theory allow additional real-world complexities to be properly modeled. The overall purpose of this thesis is to develop and evaluate stochastic optimization models that support improved decisions under uncertainty on financial markets. The research into stochastic optimization in financial literature has traditionally focused on problem formulations that allow closed-form or `exact' numerical solutions; typically through the application of dynamic programming or optimal control. The focus in this thesis is on two other optimization methods, namely stochastic programming and approximate dynamic programming, which open up opportunities to study new classes of financial problems. More specifically, these optimization methods allow additional and important aspects of many real-world problems to be captured. This thesis contributes with several insights that are relevant for both financial and stochastic optimization literature. First, we show that the modeling of several real-world aspects traditionally not considered in the literature are important components in a model which supports corporate hedging decisions. Specifically, we document the importance of modeling term premia, a rich asset universe and transaction costs. Secondly, we provide two methodological contributions to the stochastic programming literature by: (i) highlighting the challenges of realizing improved decisions through more stages in stochastic programming models; and (ii) developing an importance sampling method that can be used to produce high solution quality with few scenarios. Finally, we design an approximate dynamic programming model that gives close to optimal solutions to the classic, and thus far unsolved, portfolio choice problem with constant relative risk aversion preferences and transaction costs, given many risky assets and a large number of time periods.
Stochastic Modeling And Optimization
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Author : David D. Yao
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Stochastic Modeling And Optimization written by David D. Yao and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Business & Economics categories.
This books covers the broad range of research in stochastic models and optimization. Applications presented include networks, financial engineering, production planning, and supply chain management. Each contribution is aimed at graduate students working in operations research, probability, and statistics.
Stochastic Optimization And Economic Models
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Author : Jati Sengupta
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-09
Stochastic Optimization And Economic Models written by Jati Sengupta and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-09 with Mathematics categories.
This book presents the main applied aspects of stochas tic optimization in economic models. Stochastic processes and control theory are used under optimization to illustrate the various economic implications of optimal decision rules. Unlike econometrics which deals with estimation, this book emphasizes the decision-theoretic basis of uncertainty specified by the stochastic point of view. Methods of ap plied stochastic control using stochastic processes have now reached an exciti~g phase, where several disciplines like systems engineering, operations research and natural reso- ces interact along with the conventional fields such as mathematical economics, finance and control systems. Our objective is to present a critical overview of this broad terrain from a multidisciplinary viewpoint. In this attempt we have at times stressed viewpoints other than the purely economic one. We believe that the economist would find it most profitable to learn from the other disciplines where stochastic optimization has been successfully applied. It is in this spirit that we have discussed in some detail the following major areas: A. Portfolio models in ·:finance, B. Differential games under uncertainty, c. Self-tuning regulators, D. Models of renewable resources under uncertainty, and ix x PREFACE E. Nonparametric methods of efficiency measurement. Stochastic processes are now increasingly used in economic models to understand the various adaptive behavior implicit in the formulation of expectation and its application in decision rules which are optimum in some sense.
Stochastic Modeling In Economics And Finance
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Author : Jitka Dupacova
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-04-18
Stochastic Modeling In Economics And Finance written by Jitka Dupacova and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-04-18 with Mathematics categories.
In Part I, the fundamentals of financial thinking and elementary mathematical methods of finance are presented. The method of presentation is simple enough to bridge the elements of financial arithmetic and complex models of financial math developed in the later parts. It covers characteristics of cash flows, yield curves, and valuation of securities. Part II is devoted to the allocation of funds and risk management: classics (Markowitz theory of portfolio), capital asset pricing model, arbitrage pricing theory, asset & liability management, value at risk. The method explanation takes into account the computational aspects. Part III explains modeling aspects of multistage stochastic programming on a relatively accessible level. It includes a survey of existing software, links to parametric, multiobjective and dynamic programming, and to probability and statistics. It focuses on scenario-based problems with the problems of scenario generation and output analysis discussed in detail and illustrated within a case study.
Problems In Portfolio Theory And The Fundamentals Of Financial Decision Making
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Author : Leonard C MacLean
language : en
Publisher: World Scientific Publishing Company
Release Date : 2016-09-29
Problems In Portfolio Theory And The Fundamentals Of Financial Decision Making written by Leonard C MacLean and has been published by World Scientific Publishing Company this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-09-29 with categories.
This book consists of invaluable introductions, tutorials and problems which are helpful for teaching purposes and have a very broad appeal and usage. The problems cover many aspects of static and dynamic portfolio theory as well as other important subjects such as arbitrage and asset pricing, utility theory, stochastic dominance, risk aversion and static portfolio theory, risk measures, dynamic portfolio theory and asset allocation. This material could be used with important books that cover these topics including MacLean-Ziemba's The Handbook of the Fundamentals of Financial Decision Making, and Ziemba-Vickson's Stochastic Optimization Models in Finance.
Optimization Methods In Finance
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Author : Gerard Cornuejols
language : en
Publisher: Cambridge University Press
Release Date : 2006-12-21
Optimization Methods In Finance written by Gerard Cornuejols and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-12-21 with Mathematics categories.
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.
Optimization Methods In Finance
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Author : Gérard Cornuéjols
language : en
Publisher: Cambridge University Press
Release Date : 2018-08-09
Optimization Methods In Finance written by Gérard Cornuéjols and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-09 with Business & Economics categories.
Full treatment, from model formulation to computational implementation, of optimization techniques that solve central problems in finance.