Stochastic Intertemporal Optimization in Discrete Time PDF Download
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Languages : en
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Author:
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Languages : en
Pages :
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Author: Masanao Aoki
Publisher: Elsevier
ISBN: 1483224058
Category : Mathematics
Languages : en
Pages : 373
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Book Description
Optimization of Stochastic Systems
Author: Wendell Helms Fleming
Publisher:
ISBN:
Category :
Languages : en
Pages : 22
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Author: Wendell H. Fleming
Publisher:
ISBN:
Category :
Languages : en
Pages : 23
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Book Description
The standard literature concerning intertemporal optimization in international finance is based upon certainty equivalence, and ignores risk and uncertainty. It therefore is not helpful concerning risk management and evaluation of the risk involved in the holding of international short-term debt. We solve a modification of the standard model of intertemporal optimization in discrete time, in an environment where the return to capital is stochastic. We impose the constraint that there be no default on the short-term debt. Thereby we derive benchmarks for optimal foreign debt, which will not be defaulted. We do not claim that the optimal debt is the same as the actual debt incurred. Witness the defaults and debt crises. Insofar as the actual debt exceeds the benchmark, the risk of default is increased. The main reasons for a deviation between the actual debt and the optimal debt is that the borrower is overly optimistic about the distribution function of the return to investment, and does not optimize subject to a quot;no defaultquot; constraint. We also consider an intertemporal optimization model involving extreme prudence. The lender, who may be an institutional investor, has infinite risk aversion and will only lend for projects where the profitability of the investment is almost sure. In this case also, we derive the optimal debt, which is our benchmark for risk management.
Author: C. Striebel
Publisher: Springer
ISBN: 3642454704
Category : Business & Economics
Languages : en
Pages : 215
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Author: Klaus Wälde
Publisher: Klaus Wälde
ISBN: 3000324283
Category :
Languages : en
Pages : 332
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Book Description
Author: Pierre Carpentier
Publisher: Springer
ISBN: 3319181386
Category : Mathematics
Languages : en
Pages : 370
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Book Description
The focus of the present volume is stochastic optimization of dynamical systems in discrete time where - by concentrating on the role of information regarding optimization problems - it discusses the related discretization issues. There is a growing need to tackle uncertainty in applications of optimization. For example the massive introduction of renewable energies in power systems challenges traditional ways to manage them. This book lays out basic and advanced tools to handle and numerically solve such problems and thereby is building a bridge between Stochastic Programming and Stochastic Control. It is intended for graduates readers and scholars in optimization or stochastic control, as well as engineers with a background in applied mathematics.
Author: Dimitri P. Bertsekas
Publisher:
ISBN: 9780120932603
Category : Dynamic programming
Languages : en
Pages : 323
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Book Description
Author: Charlotte Striebel
Publisher: Springer
ISBN: 9780387071817
Category : Commande, Théorie de la
Languages : en
Pages : 0
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Author: Xi-Ren Cao
Publisher: Springer Nature
ISBN: 3030418464
Category : Technology & Engineering
Languages : en
Pages : 376
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Book Description
This monograph applies the relative optimization approach to time nonhomogeneous continuous-time and continuous-state dynamic systems. The approach is intuitively clear and does not require deep knowledge of the mathematics of partial differential equations. The topics covered have the following distinguishing features: long-run average with no under-selectivity, non-smooth value functions with no viscosity solutions, diffusion processes with degenerate points, multi-class optimization with state classification, and optimization with no dynamic programming. The book begins with an introduction to relative optimization, including a comparison with the traditional approach of dynamic programming. The text then studies the Markov process, focusing on infinite-horizon optimization problems, and moves on to discuss optimal control of diffusion processes with semi-smooth value functions and degenerate points, and optimization of multi-dimensional diffusion processes. The book concludes with a brief overview of performance derivative-based optimization. Among the more important novel considerations presented are: the extension of the Hamilton–Jacobi–Bellman optimality condition from smooth to semi-smooth value functions by derivation of explicit optimality conditions at semi-smooth points and application of this result to degenerate and reflected processes; proof of semi-smoothness of the value function at degenerate points; attention to the under-selectivity issue for the long-run average and bias optimality; discussion of state classification for time nonhomogeneous continuous processes and multi-class optimization; and development of the multi-dimensional Tanaka formula for semi-smooth functions and application of this formula to stochastic control of multi-dimensional systems with degenerate points. The book will be of interest to researchers and students in the field of stochastic control and performance optimization alike.
