Stochastic Optimization In Insurance

Author : Pablo Azcue,Nora Muler
Publisher : Springer
Page : 146 pages
File Size : 55,9 Mb
Release : 2014-06-19
Category : Mathematics
ISBN : 9781493909957

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Stochastic Optimization in Insurance by Pablo Azcue,Nora Muler Pdf

The main purpose of the book is to show how a viscosity approach can be used to tackle control problems in insurance. The problems covered are the maximization of survival probability as well as the maximization of dividends in the classical collective risk model. The authors consider the possibility of controlling the risk process by reinsurance as well as by investments. They show that optimal value functions are characterized as either the unique or the smallest viscosity solution of the associated Hamilton-Jacobi-Bellman equation; they also study the structure of the optimal strategies and show how to find them. The viscosity approach was widely used in control problems related to mathematical finance but until quite recently it was not used to solve control problems related to actuarial mathematical science. This book is designed to familiarize the reader on how to use this approach. The intended audience is graduate students as well as researchers in this area.

Author : Hanspeter Schmidli
Publisher : Springer Science & Business Media
Page : 263 pages
File Size : 41,5 Mb
Release : 2007-11-20
Category : Business & Economics
ISBN : 9781848000032

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Stochastic Control in Insurance by Hanspeter Schmidli Pdf

Yet again, here is a Springer volume that offers readers something completely new. Until now, solved examples of the application of stochastic control to actuarial problems could only be found in journals. Not any more: this is the first book to systematically present these methods in one volume. The author starts with a short introduction to stochastic control techniques, then applies the principles to several problems. These examples show how verification theorems and existence theorems may be proved, and that the non-diffusion case is simpler than the diffusion case. Schmidli’s brilliant text also includes a number of appendices, a vital resource for those in both academic and professional settings.

Author : Charles S. Tapiero
Publisher : Springer Science & Business Media
Page : 352 pages
File Size : 46,7 Mb
Release : 2012-12-06
Category : Business & Economics
ISBN : 9781461558231

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Applied Stochastic Models and Control for Finance and Insurance by Charles S. Tapiero Pdf

Applied Stochastic Models and Control for Finance and Insurance presents at an introductory level some essential stochastic models applied in economics, finance and insurance. Markov chains, random walks, stochastic differential equations and other stochastic processes are used throughout the book and systematically applied to economic and financial applications. In addition, a dynamic programming framework is used to deal with some basic optimization problems. The book begins by introducing problems of economics, finance and insurance which involve time, uncertainty and risk. A number of cases are treated in detail, spanning risk management, volatility, memory, the time structure of preferences, interest rates and yields, etc. The second and third chapters provide an introduction to stochastic models and their application. Stochastic differential equations and stochastic calculus are presented in an intuitive manner, and numerous applications and exercises are used to facilitate their understanding and their use in Chapter 3. A number of other processes which are increasingly used in finance and insurance are introduced in Chapter 4. In the fifth chapter, ARCH and GARCH models are presented and their application to modeling volatility is emphasized. An outline of decision-making procedures is presented in Chapter 6. Furthermore, we also introduce the essentials of stochastic dynamic programming and control, and provide first steps for the student who seeks to apply these techniques. Finally, in Chapter 7, numerical techniques and approximations to stochastic processes are examined. This book can be used in business, economics, financial engineering and decision sciences schools for second year Master's students, as well as in a number of courses widely given in departments of statistics, systems and decision sciences.

Author : Georg Ch. Pflug,Alois Pichler
Publisher : Springer
Page : 301 pages
File Size : 41,5 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 : Marida Bertocchi,Giorgio Consigli,Michael A. H. Dempster
Publisher : Springer Science & Business Media
Page : 476 pages
File Size : 55,5 Mb
Release : 2011-09-15
Category : Business & Economics
ISBN : 1441995862

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Stochastic Optimization Methods in Finance and Energy by Marida Bertocchi,Giorgio Consigli,Michael A. H. Dempster Pdf

This volume presents a collection of contributions dedicated to applied problems in the financial and energy sectors that have been formulated and solved in a stochastic optimization framework. The invited authors represent a group of scientists and practitioners, who cooperated in recent years to facilitate the growing penetration of stochastic programming techniques in real-world applications, inducing a significant advance over a large spectrum of complex decision problems. After the recent widespread liberalization of the energy sector in Europe and the unprecedented growth of energy prices in international commodity markets, we have witnessed a significant convergence of strategic decision problems in the energy and financial sectors. This has often resulted in common open issues and has induced a remarkable effort by the industrial and scientific communities to facilitate the adoption of advanced analytical and decision tools. The main concerns of the financial community over the last decade have suddenly penetrated the energy sector inducing a remarkable scientific and practical effort to address previously unforeseeable management problems. Stochastic Optimization Methods in Finance and Energy: New Financial Products and Energy Markets Strategies aims to include in a unified framework for the first time an extensive set of contributions related to real-world applied problems in finance and energy, leading to a common methodological approach and in many cases having similar underlying economic and financial implications. Part 1 of the book presents 6 chapters related to financial applications; Part 2 presents 7 chapters on energy applications; and Part 3 presents 5 chapters devoted to specific theoretical and computational issues.

Author : Kurt Marti,Yuri Ermoliev,Georg Ch. Pflug
Publisher : Springer Science & Business Media
Page : 337 pages
File Size : 46,9 Mb
Release : 2012-12-06
Category : Science
ISBN : 9783642558849

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Dynamic Stochastic Optimization by Kurt Marti,Yuri Ermoliev,Georg Ch. Pflug Pdf

Uncertainties and changes are pervasive characteristics of modern systems involving interactions between humans, economics, nature and technology. These systems are often too complex to allow for precise evaluations and, as a result, the lack of proper management (control) may create significant risks. In order to develop robust strategies we need approaches which explic itly deal with uncertainties, risks and changing conditions. One rather general approach is to characterize (explicitly or implicitly) uncertainties by objec tive or subjective probabilities (measures of confidence or belief). This leads us to stochastic optimization problems which can rarely be solved by using the standard deterministic optimization and optimal control methods. In the stochastic optimization the accent is on problems with a large number of deci sion and random variables, and consequently the focus ofattention is directed to efficient solution procedures rather than to (analytical) closed-form solu tions. Objective and constraint functions of dynamic stochastic optimization problems have the form of multidimensional integrals of rather involved in that may have a nonsmooth and even discontinuous character - the tegrands typical situation for "hit-or-miss" type of decision making problems involving irreversibility ofdecisions or/and abrupt changes ofthe system. In general, the exact evaluation of such functions (as is assumed in the standard optimization and control theory) is practically impossible. Also, the problem does not often possess the separability properties that allow to derive the standard in control theory recursive (Bellman) equations.

Author : Stanislav Uryasev,Panos M. Pardalos
Publisher : Springer Science & Business Media
Page : 438 pages
File Size : 51,5 Mb
Release : 2013-03-09
Category : Technology & Engineering
ISBN : 9781475765946

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Stochastic Optimization by Stanislav Uryasev,Panos M. Pardalos Pdf

Stochastic programming is the study of procedures for decision making under the presence of uncertainties and risks. Stochastic programming approaches have been successfully used in a number of areas such as energy and production planning, telecommunications, and transportation. Recently, the practical experience gained in stochastic programming has been expanded to a much larger spectrum of applications including financial modeling, risk management, and probabilistic risk analysis. Major topics in this volume include: (1) advances in theory and implementation of stochastic programming algorithms; (2) sensitivity analysis of stochastic systems; (3) stochastic programming applications and other related topics. Audience: Researchers and academies working in optimization, computer modeling, operations research and financial engineering. The book is appropriate as supplementary reading in courses on optimization and financial engineering.

Author : William T. Ziemba,Raymond G. Vickson
Publisher : World Scientific
Page : 756 pages
File Size : 55,6 Mb
Release : 2006
Category : Business & Economics
ISBN : 9789812568007

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Stochastic Optimization Models in Finance by William T. Ziemba,Raymond G. Vickson Pdf

A reprint of one of the classic volumes on portfolio theory and investment, this book has been used by the leading professors at universities such as Stanford, Berkeley, and Carnegie-Mellon. It contains five parts, each with a review of the literature and about 150 pages of computational and review exercises and further in-depth, challenging problems.Frequently referenced and highly usable, the material remains as fresh and relevant for a portfolio theory course as ever.

Author : Alexandra Sidorenko
Publisher : Unknown
Page : 27 pages
File Size : 46,7 Mb
Release : 2001
Category : Health insurance
ISBN : 0957976917

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Coinsurance Rate Elasticity of Demand for Medical Insurance in Stochastic Optimization Model by Alexandra Sidorenko Pdf

Author : Tomasz Rolski,Hanspeter Schmidli,V. Schmidt,Jozef L. Teugels
Publisher : John Wiley & Sons
Page : 680 pages
File Size : 44,9 Mb
Release : 2009-09-25
Category : Mathematics
ISBN : 9780470317884

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Stochastic Processes for Insurance and Finance by Tomasz Rolski,Hanspeter Schmidli,V. Schmidt,Jozef L. Teugels Pdf

Stochastic Processes for Insurance and Finance offers a thorough yet accessible reference for researchers and practitioners of insurance mathematics. Building on recent and rapid developments in applied probability, the authors describe in general terms models based on Markov processes, martingales and various types of point processes. Discussing frequently asked insurance questions, the authors present a coherent overview of the subject and specifically address: The principal concepts from insurance and finance Practical examples with real life data Numerical and algorithmic procedures essential for modern insurance practices Assuming competence in probability calculus, this book will provide a fairly rigorous treatment of insurance risk theory recommended for researchers and students interested in applied probability as well as practitioners of actuarial sciences. Wiley Series in Probability and Statistics

Author : Alexei A. Gaivoronski,Pavlo S. Knopov,Volodymyr A. Zaslavskyi
Publisher : CRC Press
Page : 388 pages
File Size : 52,8 Mb
Release : 2023-10-06
Category : Computers
ISBN : 9781000983920

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Modern Optimization Methods for Decision Making Under Risk and Uncertainty by Alexei A. Gaivoronski,Pavlo S. Knopov,Volodymyr A. Zaslavskyi Pdf

The book comprises original articles on topical issues of risk theory, rational decision making, statistical decisions, and control of stochastic systems. The articles are the outcome of a series international projects involving the leading scholars in the field of modern stochastic optimization and decision making. The structure of stochastic optimization solvers is described. The solvers in general implement stochastic quasi-gradient methods for optimization and identification of complex nonlinear models. These models constitute an important methodology for finding optimal decisions under risk and uncertainty. While a large part of current approaches towards optimization under uncertainty stems from linear programming (LP) and often results in large LPs of special structure, stochastic quasi-gradient methods confront nonlinearities directly without need of linearization. This makes them an appropriate tool for solving complex nonlinear problems, concurrent optimization and simulation models, and equilibrium situations of different types, for instance, Nash or Stackelberg equilibrium situations. The solver finds the equilibrium solution when the optimization model describes the system with several actors. The solver is parallelizable, performing several simulation threads in parallel. It is capable of solving stochastic optimization problems, finding stochastic Nash equilibria, and of composite stochastic bilevel problems where each level may require the solution of stochastic optimization problem or finding Nash equilibrium. Several complex examples with applications to water resources management, energy markets, pricing of services on social networks are provided. In the case of power system, regulator makes decision on the final expansion plan, considering the strategic behavior of regulated companies and coordinating the interests of different economic entities. Such a plan can be an equilibrium − a planned decision where a company cannot increase its expected gain unilaterally.

Author : Stein W. Wallace,William T. Ziemba
Publisher : SIAM
Page : 701 pages
File Size : 54,8 Mb
Release : 2005-06-01
Category : Mathematics
ISBN : 9780898715552

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Applications of Stochastic Programming by Stein W. Wallace,William T. Ziemba Pdf

Consisting of two parts, this book presents papers describing publicly available stochastic programming systems that are operational. It presents a diverse collection of application papers in areas such as production, supply chain and scheduling, gaming, environmental and pollution control, financial modeling, telecommunications, and electricity.

Author : Stavros A. Zenios,William T. Ziemba
Publisher : Elsevier
Page : 685 pages
File Size : 40,5 Mb
Release : 2007-08-08
Category : Business & Economics
ISBN : 9780080548562

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Handbook of Asset and Liability Management by Stavros A. Zenios,William T. Ziemba Pdf

The Handbooks in Finance are intended to be a definitive source for comprehensive and accessible information in the field of finance. Each individual volume in the series presents an accurate self-contained survey of a sub-field of finance, suitable for use by finance and economics professors and lecturers, professional researchers, graduate students and as a teaching supplement. It is fitting that the series Handbooks in Finance devotes a handbook to Asset and Liability Management. Volume 2 focuses on applications and case studies in asset and liability management.The growth in knowledge about practical asset and liability modeling has followed the popularity of these models in diverse business settings. This volume portrays ALM in practice, in contrast to Volume 1, which addresses the theories and methodologies behind these models. In original articles practitioners and scholars describe and analyze models used in banking, insurance, money management, individual investor financial planning, pension funds, and social security. They put the traditional purpose of ALM, to control interest rate and liquidity risks, into rich and broad-minded frameworks. Readers interested in other business settings will find their discussions of financial institutions both instructive and revealing. * Focuses on pragmatic applications * Relevant to a variety of risk-management industries* Analyzes models used in most financial sectors

Author : Georg Ch. Pflug
Publisher : Springer Science & Business Media
Page : 384 pages
File Size : 54,5 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 : W. T. Ziemba,R. G. Vickson
Publisher : Academic Press
Page : 736 pages
File Size : 48,7 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.