Stochastic Optimization Models In Finance

Author : W. T. Ziemba,R. G. Vickson
Publisher : Academic Press
Page : 736 pages
File Size : 41,5 Mb
Release : 2014-05-12
Category : Business & Economics
ISBN : 9781483273990

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Stochastic Optimization Models in Finance by W. T. Ziemba,R. G. Vickson Pdf

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.

Author : W. T. Ziemba (Comp)
Publisher : Unknown
Page : 719 pages
File Size : 54,7 Mb
Release : 1975
Category : Finance
ISBN : LCCN:10138499

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Stochastic Optimization Models in Finance by W. T. Ziemba (Comp) Pdf

Author : Horand Gassmann,W. T. Ziemba
Publisher : World Scientific
Page : 549 pages
File Size : 50,6 Mb
Release : 2013
Category : Business & Economics
ISBN : 9789814407502

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Stochastic Programming by Horand Gassmann,W. T. Ziemba Pdf

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.

Author : Gerard Cornuejols,Reha Tütüncü
Publisher : Cambridge University Press
Page : 358 pages
File Size : 50,6 Mb
Release : 2006-12-21
Category : Mathematics
ISBN : 0521861705

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Optimization Methods in Finance by Gerard Cornuejols,Reha Tütüncü Pdf

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.

Author : Jonas Ekblom
Publisher : Linköping University Electronic Press
Page : 36 pages
File Size : 52,8 Mb
Release : 2018-09-13
Category : Electronic
ISBN : 9789176852026

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Decision Making under Uncertainty in Financial Markets by Jonas Ekblom Pdf

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.

Author : Georg Ch. Pflug
Publisher : Springer Science & Business Media
Page : 384 pages
File Size : 54,6 Mb
Release : 2012-12-06
Category : Business & Economics
ISBN : 9781461314493

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Optimization of Stochastic Models by Georg Ch. Pflug Pdf

Stochastic models are everywhere. In manufacturing, queuing models are used for modeling production processes, realistic inventory models are stochastic in nature. Stochastic models are considered in transportation and communication. Marketing models use stochastic descriptions of the demands and buyer's behaviors. In finance, market prices and exchange rates are assumed to be certain stochastic processes, and insurance claims appear at random times with random amounts. To each decision problem, a cost function is associated. Costs may be direct or indirect, like loss of time, quality deterioration, loss in production or dissatisfaction of customers. In decision making under uncertainty, the goal is to minimize the expected costs. However, in practically all realistic models, the calculation of the expected costs is impossible due to the model complexity. Simulation is the only practicable way of getting insight into such models. Thus, the problem of optimal decisions can be seen as getting simulation and optimization effectively combined. The field is quite new and yet the number of publications is enormous. This book does not even try to touch all work done in this area. Instead, many concepts are presented and treated with mathematical rigor and necessary conditions for the correctness of various approaches are stated. Optimization of Stochastic Models: The Interface Between Simulation and Optimization is suitable as a text for a graduate level course on Stochastic Models or as a secondary text for a graduate level course in Operations Research.

Author : Huyên Pham
Publisher : Springer Science & Business Media
Page : 243 pages
File Size : 55,5 Mb
Release : 2009-05-28
Category : Mathematics
ISBN : 9783540895008

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Continuous-time Stochastic Control and Optimization with Financial Applications by Huyên Pham Pdf

Stochastic optimization problems arise in decision-making problems under uncertainty, and find various applications in economics and finance. On the other hand, problems in finance have recently led to new developments in the theory of stochastic control. This volume provides a systematic treatment of stochastic optimization problems applied to finance by presenting the different existing methods: dynamic programming, viscosity solutions, backward stochastic differential equations, and martingale duality methods. The theory is discussed in the context of recent developments in this field, with complete and detailed proofs, and is illustrated by means of concrete examples from the world of finance: portfolio allocation, option hedging, real options, optimal investment, etc. This book is directed towards graduate students and researchers in mathematical finance, and will also benefit applied mathematicians interested in financial applications and practitioners wishing to know more about the use of stochastic optimization methods in finance.

Author : David D. Yao,Hanqin Zhang,Xun Yu Zhou
Publisher : Springer Science & Business Media
Page : 472 pages
File Size : 49,5 Mb
Release : 2012-12-06
Category : Business & Economics
ISBN : 9780387217574

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Stochastic Modeling and Optimization by David D. Yao,Hanqin Zhang,Xun Yu Zhou Pdf

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.

Author : Jati Sengupta
Publisher : Springer Science & Business Media
Page : 381 pages
File Size : 43,7 Mb
Release : 2013-03-09
Category : Mathematics
ISBN : 9789401730853

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Stochastic Optimization and Economic Models by Jati Sengupta Pdf

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.

Author : Bruce D. Craven,Sardar M. N. Islam
Publisher : Springer Science & Business Media
Page : 161 pages
File Size : 41,5 Mb
Release : 2006-03-30
Category : Business & Economics
ISBN : 9780387242804

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Optimization in Economics and Finance by Bruce D. Craven,Sardar M. N. Islam Pdf

Some recent developments in the mathematics of optimization, including the concepts of invexity and quasimax, have not yet been applied to models of economic growth, and to finance and investment. Their applications to these areas are shown in this book.

Author : Jitka Dupacova,J. Hurt,J. Stepan
Publisher : Springer Science & Business Media
Page : 394 pages
File Size : 50,9 Mb
Release : 2006-04-18
Category : Mathematics
ISBN : 9780306481673

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Stochastic Modeling in Economics and Finance by Jitka Dupacova,J. Hurt,J. Stepan Pdf

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.

Author : Georg Ch. Pflug,Alois Pichler
Publisher : Springer
Page : 301 pages
File Size : 44,6 Mb
Release : 2014-11-12
Category : Business & Economics
ISBN : 9783319088433

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Multistage Stochastic Optimization by Georg Ch. Pflug,Alois Pichler Pdf

Multistage stochastic optimization problems appear in many ways in finance, insurance, energy production and trading, logistics and transportation, among other areas. They describe decision situations under uncertainty and with a longer planning horizon. This book contains a comprehensive treatment of today’s state of the art in multistage stochastic optimization. It covers the mathematical backgrounds of approximation theory as well as numerous practical algorithms and examples for the generation and handling of scenario trees. A special emphasis is put on estimation and bounding of the modeling error using novel distance concepts, on time consistency and the role of model ambiguity in the decision process. An extensive treatment of examples from electricity production, asset liability management and inventory control concludes the book.

Author : Warren B. Powell
Publisher : John Wiley & Sons
Page : 1090 pages
File Size : 45,5 Mb
Release : 2022-03-15
Category : Mathematics
ISBN : 9781119815037

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Reinforcement Learning and Stochastic Optimization by Warren B. Powell Pdf

REINFORCEMENT LEARNING AND STOCHASTIC OPTIMIZATION Clearing the jungle of stochastic optimization Sequential decision problems, which consist of “decision, information, decision, information,” are ubiquitous, spanning virtually every human activity ranging from business applications, health (personal and public health, and medical decision making), energy, the sciences, all fields of engineering, finance, and e-commerce. The diversity of applications attracted the attention of at least 15 distinct fields of research, using eight distinct notational systems which produced a vast array of analytical tools. A byproduct is that powerful tools developed in one community may be unknown to other communities. Reinforcement Learning and Stochastic Optimization offers a single canonical framework that can model any sequential decision problem using five core components: state variables, decision variables, exogenous information variables, transition function, and objective function. This book highlights twelve types of uncertainty that might enter any model and pulls together the diverse set of methods for making decisions, known as policies, into four fundamental classes that span every method suggested in the academic literature or used in practice. Reinforcement Learning and Stochastic Optimization is the first book to provide a balanced treatment of the different methods for modeling and solving sequential decision problems, following the style used by most books on machine learning, optimization, and simulation. The presentation is designed for readers with a course in probability and statistics, and an interest in modeling and applications. Linear programming is occasionally used for specific problem classes. The book is designed for readers who are new to the field, as well as those with some background in optimization under uncertainty. Throughout this book, readers will find references to over 100 different applications, spanning pure learning problems, dynamic resource allocation problems, general state-dependent problems, and hybrid learning/resource allocation problems such as those that arose in the COVID pandemic. There are 370 exercises, organized into seven groups, ranging from review questions, modeling, computation, problem solving, theory, programming exercises and a “diary problem” that a reader chooses at the beginning of the book, and which is used as a basis for questions throughout the rest of the book.

Author : Stavros A. Zenios
Publisher : Cambridge University Press
Page : 374 pages
File Size : 40,7 Mb
Release : 1993
Category : Business & Economics
ISBN : 0521577772

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Financial Optimization by Stavros A. Zenios Pdf

The use of formal mathematical models and optimization in finance has become common practice in the 1980s and 1990s. This book clearly presents the exciting symbiosis between the fields of finance and management science/operations research. Prominent researchers present the state of the art in financial optimization, while analysts from industry discuss the latest business techniques practised by financial firms in New York, London and Tokyo. The book covers a wide range of topics: portfolio management of equities and fixed income investments, the pricing of complex insurance, mortgage and other asset-backed products, and models for risk-management and diversification.

Author : Bruce D. Craven,Sardar M. N. Islam
Publisher : Springer Science & Business Media
Page : 184 pages
File Size : 42,8 Mb
Release : 2005
Category : Business & Economics
ISBN : 0387242791

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Optimization in Economics and Finance by Bruce D. Craven,Sardar M. N. Islam Pdf

Extends the optimization techniques, in a form that may be adopted for modeling social choice problems. The models in this book provide possible models for a society's social choice for an allocation that maximizes welfare and utilization of resources. A computer program SCOM is presented here for computing social choice models by optimal control.