Multiple Stopping Problems

Multiple Stopping Problems
Author: Georgy Sofronov
Publisher: CRC Press
Total Pages: 376
Release: 2024-12-24
Genre: Mathematics
ISBN: 1040228925

This book presents the theory of rational decisions involving the selection of stopping times in observed discrete-time stochastic processes, both by single and multiple decision-makers. Readers will become acquainted with the models, strategies, and applications of these models. It begins with an examination of selected models framed as stochastic optimization challenges, emphasizing the critical role of optimal stopping times in sequential statistical procedures. The authors go on to explore models featuring multiple stopping and shares on leading applications, particularly focusing on change point detection, selection problems, and the nuances of behavioral ecology. In the following chapters, an array of perspectives on model strategies is presented, elucidating their interpretation and the methodologies underpinning their genesis. Essential notations and definitions are introduced, examining general theorems about solution existence and structure, with an intricate analysis of optimal stopping predicaments and addressing crucial multilateral models. The reader is presented with the practical application of models based on multiple stopping within stochastic processes. The coverage includes a diverse array of domains, including sequential statistics, finance, economics, and the broader generalization of the best-choice problem. Additionally, it delves into numerical and asymptotic solutions, offering a comprehensive exploration of optimal stopping quandaries. The book will be of interest to researchers and practitioners in fields such as economics, finance, and engineering. It could also be used by graduate students doing a research degree in insurance, economics or business analytics or an advanced undergraduate course in mathematical sciences.

Advanced Simulation-Based Methods for Optimal Stopping and Control

Advanced Simulation-Based Methods for Optimal Stopping and Control
Author: Denis Belomestny
Publisher: Springer
Total Pages: 366
Release: 2018-01-31
Genre: Business & Economics
ISBN: 1137033517

This is an advanced guide to optimal stopping and control, focusing on advanced Monte Carlo simulation and its application to finance. Written for quantitative finance practitioners and researchers in academia, the book looks at the classical simulation based algorithms before introducing some of the new, cutting edge approaches under development.

Optimization, Control, and Applications of Stochastic Systems

Optimization, Control, and Applications of Stochastic Systems
Author: Daniel Hernández-Hernández
Publisher: Springer Science & Business Media
Total Pages: 331
Release: 2012-08-15
Genre: Science
ISBN: 0817683372

This volume provides a general overview of discrete- and continuous-time Markov control processes and stochastic games, along with a look at the range of applications of stochastic control and some of its recent theoretical developments. These topics include various aspects of dynamic programming, approximation algorithms, and infinite-dimensional linear programming. In all, the work comprises 18 carefully selected papers written by experts in their respective fields. Optimization, Control, and Applications of Stochastic Systems will be a valuable resource for all practitioners, researchers, and professionals in applied mathematics and operations research who work in the areas of stochastic control, mathematical finance, queueing theory, and inventory systems. It may also serve as a supplemental text for graduate courses in optimal control and dynamic games.

Robust Libor Modelling and Pricing of Derivative Products

Robust Libor Modelling and Pricing of Derivative Products
Author: John Schoenmakers
Publisher: CRC Press
Total Pages: 224
Release: 2005-03-29
Genre: Business & Economics
ISBN: 0203499093

One of Riskbook.com's Best of 2005 - Top Ten Finance Books The Libor market model remains one of the most popular and advanced tools for modelling interest rates and interest rate derivatives, but finding a useful procedure for calibrating the model has been a perennial problem. Also the respective pricing of exotic derivative products such

Probabilistic Methods in Discrete Mathematics

Probabilistic Methods in Discrete Mathematics
Author: V. F. Kolchin
Publisher: Walter de Gruyter GmbH & Co KG
Total Pages: 400
Release: 2020-05-18
Genre: Mathematics
ISBN: 3112314107

No detailed description available for "Probabilistic Methods in Discrete Mathematics".

Quantitative Energy Finance

Quantitative Energy Finance
Author: Fred Espen Benth
Publisher: Springer Science & Business Media
Total Pages: 318
Release: 2013-08-28
Genre: Business & Economics
ISBN: 1461472482

Finance and energy markets have been an active scientific field for some time, even though the development and applications of sophisticated quantitative methods in these areas are relatively new—and referred to in a broader context as energy finance. Energy finance is often viewed as a branch of mathematical finance, yet this area continues to provide a rich source of issues that are fuelling new and exciting research developments. Based on a special thematic year at the Wolfgang Pauli Institute (WPI) in Vienna, Austria, this edited collection features cutting-edge research from leading scientists in the fields of energy and commodity finance. Topics discussed include modeling and analysis of energy and commodity markets, derivatives hedging and pricing, and optimal investment strategies and modeling of emerging markets, such as power and emissions. The book also confronts the challenges one faces in energy markets from a quantitative point of view, as well as the recent advances in solving these problems using advanced mathematical, statistical and numerical methods. By addressing the emerging area of quantitative energy finance, this volume will serve as a valuable resource for graduate-level students and researchers studying financial mathematics, risk management, or energy finance.

Algorithms to Live By

Algorithms to Live By
Author: Brian Christian
Publisher: Macmillan
Total Pages: 366
Release: 2016-04-19
Genre: Business & Economics
ISBN: 1627790365

'Algorithms to Live By' looks at the simple, precise algorithms that computers use to solve the complex 'human' problems that we face, and discovers what they can tell us about the nature and origin of the mind.

Recent Developments in Computational Finance

Recent Developments in Computational Finance
Author: Thomas Gerstner
Publisher: World Scientific
Total Pages: 481
Release: 2013
Genre: Business & Economics
ISBN: 9814436437

Computational finance is an interdisciplinary field which joins financial mathematics, stochastics, numerics and scientific computing. Its task is to estimate as accurately and efficiently as possible the risks that financial instruments generate. This volume consists of a series of cutting-edge surveys of recent developments in the field written by leading international experts. These make the subject accessible to a wide readership in academia and financial businesses. The book consists of 13 chapters divided into 3 parts: foundations, algorithms and applications. Besides surveys of existing results, the book contains many new previously unpublished results.

Optimal Stopping Problems in Operations Management

Optimal Stopping Problems in Operations Management
Author: SeChan Oh
Publisher: Stanford University
Total Pages: 159
Release: 2010
Genre:
ISBN:

Optimal stopping problems determine the time to terminate a process to maximize expected rewards. Such problems are pervasive in the areas of operations management, marketing, statistics, finance, and economics. This dissertation provides a method that characterizes the structure of the optimal stopping policy for a general class of optimal stopping problems. It also studies two important optimal stopping problems arising in Operations Management. In the first part of the dissertation, we provide a method to characterize the structure of the optimal stopping policy for the class of discrete-time optimal stopping problems. Our method characterizes the structure of the optimal policy for some stopping problems for which conventional methods fail. Our method also simplifies the analysis of some existing results. Using the method, we determine sufficient conditions that yield threshold or control-band type optimal stopping policies. The results also help characterize parametric monotonicity of optimal thresholds and provide bounds for them. In the second part of the dissertation, we first generalize the Martingale Model of Forecast Evolution to account for multiple forecasters who forecast demand for the same product. The result enables us to consistently model the evolution of forecasts generated by two forecasters who have asymmetric demand information. Using the forecast evolution model, we next study a supplier's problem of eliciting credible forecast information from a manufacturer when both parties obtain asymmetric demand information over multiple periods. For better capacity planning, the supplier designs and offers a screening contract that ensures the manufacturer's credible information sharing. By delaying to offer this incentive mechanism, the supplier can obtain more information. This delay, however, may increase (resp., or decrease) the degree of information asymmetry between the two firms, resulting in a higher (resp., or lower) cost of screening. The delay may also increase capacity costs. Considering all such trade-offs, the supplier has to determine how to design a mechanism to elicit credible forecast information from the manufacturer and when to offer this incentive mechanism. In the last part of the dissertation, we study a manufacturer's problem of determining the time to introduce a new product to the market. Conventionally, manufacturing firms determine the time to introduce a new product to the market long before launching the product. The timing decision involves considerable risk because manufacturing firms are uncertain about competing firms' market entry timing and the outcome of production process development activities at the time when they make the decision. As a solution for reducing such risk, we propose a dynamic market entry strategy under which the manufacturer makes decisions about market entry timing and process improvements in response to the evolution of uncertain factors. We show that the manufacturer can reduce profit variability and increase average profit by employing this dynamic strategy. Our study also characterizes the industry conditions under which the dynamic strategy is most effective.