2 Stochastic optimal control model of short-term debt; 3 Stochastic intertemporal optimization: Long-term debt continuous time; 4 The NATREX model of the equilibrium real exchange rate; Part III Evaluating exchange rates A stochastic control framework for real options in strategic valuation. Modern real option theory may be used for the valuation of entire companies as well as for particular investment projects in the presence of uncertainty. This book is devoted to examining a new framework for classifying real options from a management and a valuation perspective, giving the advantages and disadvantages of the real option approach. }, abstractNote = {In this study, we address the problem of minimizing the long-run expected average cost of a complex system consisting of interactive subsystems. This bar-code number lets you verify that you're getting exactly the right version or edition of a book. Vollert's book contains many great insights, but most readers will find this very tough going, indeed. There's a problem loading this menu right now. A variety of contractual features are considered, as well as some alternative assumptions about policy holder behaviour. Previous. The model captures detailed network dynamics and operational constraints and uses a weighted risk-mean objective. However, the reader will need extensive mathematical background and already understand much of what is contained here. The basic framework is built on the assumption that quantum mechanics should be seen through the framework of stochastic optimal control theory; stochastic dynamic optimization in a coordinate-invariant manner on the Minkowski spacetime. Over the last decade the theory has rapidly expanded and become enriched thanks to increasing research activity. [Alexander Vollert] The aim of this chapter is to derive a modelling framework for valuing projects with multiple incorporated real options. Stochastic control is the appropriate mathematical framework for rigorously formulating the problem of optimally adapting IMRT plans. We perform a degrees-of-freedom analysis to assess operational flexibility and to determine conditions for model consistency. We begin by defining a special class of policies. Various examples are given to demonstrate the potential of this framework. The tools are stochastic impulse control systems leading to partial differential equations and variational inequalities. Various examples are given to demonstrate the potential of this framework. The book is particularly useful for readers interested in applications in management science.". 2003 edition (October 21, 2011), Reviewed in the United States on January 10, 2005. The main tool in stochastic control is the method of dynamic programming. Title:Operational Risk Management: A Stochastic Control Framework with Preventive and Corrective Controls, Manufacturing & Service Operations Management. A Stochastic Control Framework for Real Options in Strategic Evaluation A Stochastic Control Framework Series: Author: Alexander Vollert: Edition: illustrated: Publisher: Springer Science & Business Media, 2002: ISBN: 0817642587, 9780817642587: Length: 288 pages: Subjects Enter your email address below and we will send you your username, If the address matches an existing account you will receive an email with instructions to retrieve your username. Download it once and read it on your Kindle device, PC, phones or tablets. It provides a mathematical framework for modeling decision making in situations where outcomes are partly random and partly under the control of a decision maker. 1 Optimal debt and equilibrium exchange rates in a stochastic environment: an overview; Part II Theoretical framework. A Stochastic Control Framework for Real Options in Strategic Valuation - Kindle edition by Vollert, Alexander. Please try again. The theoretical foundation for real options goes back to the mid 1980s and the development of a model that forms the basis for many current applications of real option theory. MDPs are useful for studying optimization problems solved via dynamic programming and reinforcement learning. All Rights Reserved, INFORMS site uses cookies to store information on your computer. Uncertainty, Political Preferences, and the Performance of Stabilization 3 into stochastic CGE modeling would allow policymakers to use a wide range of policy experiments with careful treatment of uncertainty. [Alexander Vollert] -- The theoretical foundation for real options goes back to the mid 1980s and the development of a model that forms the basis for many current applications of … By using this site, you consent to the placement of these cookies. The strength of this book is its rigorous taxonomy of real options and stochastic processes, extensive bibliography, and criticism of naive DCF models. Some are essential to make our site work; Others help us improve the user experience. Top subscription boxes – right to your door, © 1996-2020, Amazon.com, Inc. or its affiliates. Such a family of problems can be seen as the locally linear-quadratic approximation to more general stochastic control problems whose state dynamics are linearized in the control variables and the reward functions are lo- cally approximated by quadratic control functions (Todorov and Li, 2005; Li and Todorov, 2007). Many graphics are provided in the United States on January 10, 2005 rating and percentage breakdown star... To demonstrate the potential of the original 1st ed problems for discrete stochastic... A general modeling framework for rigorously formulating the problem of optimally adapting IMRT plans essential to make site. 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