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Stochastic Modeling and Optimization Methods for Critical Infrastructure Protection, Volume 2: Methods and Tools (ISTE Invoiced)
by Pavel S. Knopov Alexei A. Gaivoronski Volodymyr A. Zaslavskyi Vladimir I. NorkinStochastic Modeling and Optimization Methods for Critical Infrastructure Protection is a thorough exploration of mathematical models and tools that are designed to strengthen critical infrastructures against threats – both natural and adversarial. Divided into two volumes, this first volume examines stochastic modeling across key economic sectors and their interconnections, while the second volume focuses on advanced mathematical methods for enhancing infrastructure protection. The book covers a range of themes, including risk assessment techniques that account for systemic interdependencies within modern technospheres, the dynamics of uncertainty, instability and system vulnerabilities. The book also presents other topics such as cryptographic information protection and Shannon’s theory of secret systems, alongside solutions arising from optimization, game theory and machine learning approaches. Featuring research from international collaborations, this book covers both theory and applications, offering vital insights for advanced risk management curricula. It is intended not only for researchers, but also educators and professionals in infrastructure protection and stochastic optimization.
Stochastic Modeling and Optimization Methods for Critical Infrastructure Protection, Volume 2: Methods and Tools (ISTE Invoiced)
by Pavel S. Knopov Alexei A. Gaivoronski Volodymyr A. Zaslavskyi Vladimir I. NorkinStochastic Modeling and Optimization Methods for Critical Infrastructure Protection is a thorough exploration of mathematical models and tools that are designed to strengthen critical infrastructures against threats – both natural and adversarial. Divided into two volumes, this first volume examines stochastic modeling across key economic sectors and their interconnections, while the second volume focuses on advanced mathematical methods for enhancing infrastructure protection. The book covers a range of themes, including risk assessment techniques that account for systemic interdependencies within modern technospheres, the dynamics of uncertainty, instability and system vulnerabilities. The book also presents other topics such as cryptographic information protection and Shannon’s theory of secret systems, alongside solutions arising from optimization, game theory and machine learning approaches. Featuring research from international collaborations, this book covers both theory and applications, offering vital insights for advanced risk management curricula. It is intended not only for researchers, but also educators and professionals in infrastructure protection and stochastic optimization.
Stochastic Modelling of Big Data in Finance (Chapman and Hall/CRC Financial Mathematics Series)
by Anatoliy SwishchukStochastic Modelling of Big Data in Finance provides a rigorous overview and exploration of sto- chastic modelling of big data in finance (BDF). The book describes various stochastic models, including multivariate models, to deal with big data in finance. This includes data in high-frequency and algorithmic trading, specifically in limit order books (LOB), and shows how those models can be applied to different datasets to describe the dynamics of LOB, and to figure out which model is the best with respect to a specific data set. The results of the book may be used to also solve acquisition, liquidation and market making problems, and other optimization problems in finance.Features• Self-contained book suitable for graduate students and post-doctoral fellows in financial math- ematics and data science, as well as for practitioners working in the financial industry who deal with big data• All results are presented visually to aid in understanding of concepts Dr. Anatoliy Swishchuk is a Professor in Mathematical Finance at the Department of Mathematics and Statistics, University of Calgary, Calgary, AB, Canada. He got his B.Sc. and M.Sc. degrees from Kyiv State University, Kyiv, Ukraine. He earned two doctorate degrees in Mathematics and Physics (PhD and DSc) from the prestigious National Academy of Sciences of Ukraine (NASU), Kiev, Ukraine, and is a recipient of NASU award for young scientist with a gold medal for series of research publica- tions in random evolutions and their applications.Dr. Swishchuk is a chair and organizer of finance and energy finance seminar ‘Lunch at the Lab’ at the Department of Mathematics and Statistics. Dr. Swishchuk is a Director of Mathematical and Compu- tational Finance Laboratory at the University of Calgary. He was a steering committee member of the Professional Risk Managers International Association (PRMIA), Canada (2006-2015), and is a steer- ing committee member of Global Association of Risk Professionals (GARP), Canada (since 2015).Dr. Swishchuk is a creator of mathematical finance program at the Department of Mathematics & Sta- tistics. He is also a proponent for a new specialization “Financial and Energy Markets Data Modelling” in the Data Science and Analytics program. His research areas include financial mathematics, ran- dom evolutions and their applications, biomathematics, stochastic calculus, and he serves on editorial boards for four research journals. He is the author of more than 200 publications, including 15 books and more than 150 articles in peer-reviewed journals. In 2018 he received a Peak Scholar award.
Stochastic Models for Prices Dynamics in Energy and Commodity Markets: An Infinite-Dimensional Perspective (Springer Finance)
by Fred Espen Benth Paul KrühnerThis monograph presents a theory for random field models in time and space, viewed as stochastic processes with values in a Hilbert space, to model the stochastic dynamics of forward and futures prices in energy, power, and commodity markets. In this book, the well-known Heath–Jarrow–Morton approach from interest rate theory is adopted and extended into an infinite-dimensional framework, allowing for flexible modeling of price stochasticity across time and along the term structure curve. Various models are introduced based on stochastic partial differential equations with infinite-dimensional Lévy processes as noise drivers, emphasizing random fields described by low-dimensional parametric covariance functions instead of classical high-dimensional factor models. The Filipović space, a separable Hilbert space of Sobolev type, is found to be a convenient state space for the dynamics of forward and futures term structures. The monograph provides a classification of important operators in this space, covering covariance operators and the stochastic modeling of volatility term structures, including the Samuelson effect. Fourier methods are employed to price many derivatives of interest in energy, power, and commodity markets, and sensitivity 'delta' expressions can be derived. Additionally, the monograph covers forward curve smoothing, the connection between forwards with fixed delivery and delivery period, as well as the classical theory of forward and futures pricing. This monograph will appeal to researchers and graduate students interested in mathematical finance and stochastic analysis applied in the challenging markets of energy, power, and commodities. Practitioners seeking sophisticated yet flexible and analytically tractable risk models will also find it valuable.
Stochastic Models for Time Series (Mathématiques et Applications #80)
by Paul DoukhanThis book presents essential tools for modelling non-linear time series. The first part of the book describes the main standard tools of probability and statistics that directly apply to the time series context to obtain a wide range of modelling possibilities. Functional estimation and bootstrap are discussed, and stationarity is reviewed. The second part describes a number of tools from Gaussian chaos and proposes a tour of linear time series models. It goes on to address nonlinearity from polynomial or chaotic models for which explicit expansions are available, then turns to Markov and non-Markov linear models and discusses Bernoulli shifts time series models. Finally, the volume focuses on the limit theory, starting with the ergodic theorem, which is seen as the first step for statistics of time series. It defines the distributional range to obtain generic tools for limit theory under long or short-range dependences (LRD/SRD) and explains examples of LRD behaviours. More general techniques (central limit theorems) are described under SRD; mixing and weak dependence are also reviewed. In closing, it describes moment techniques together with their relations to cumulant sums as well as an application to kernel type estimation.The appendix reviews basic probability theory facts and discusses useful laws stemming from the Gaussian laws as well as the basic principles of probability, and is completed by R-scripts used for the figures. Richly illustrated with examples and simulations, the book is recommended for advanced master courses for mathematicians just entering the field of time series, and statisticians who want more mathematical insights into the background of non-linear time series.
Stochastic Models in Life Insurance
by Michael KollerThe book provides a sound mathematical base for life insurance mathematics and applies the underlying concepts to concrete examples. Moreover the models presented make it possible to model life insurance policies by means of Markov chains. Two chapters covering ALM and abstract valuation concepts on the background of Solvency II complete this volume. Numerous examples and a parallel treatment of discrete and continuous approaches help the reader to implement the theory directly in practice.
Stochastic Models in Reliability
by Terje Aven Uwe JensenThis book provides a comprehensive up-to-date presentation of some of the classical areas of reliability, based on a more advanced probabilistic framework using the modern theory of stochastic processes. This framework allows analysts to formulate general failure models, establish formulae for computing various performance measures, as well as determine how to identify optimal replacement policies in complex situations. In this second edition of the book, two major topics have been added to the original version: copula models which are used to study the effect of structural dependencies on the system reliability; and maintenance optimization which highlights delay time models under safety constraints. Terje Aven is Professor of Reliability and Risk Analysis at University of Stavanger, Norway. Uwe Jensen is working as a Professor at the Institute of Applied Mathematics and Statistics of the University of Hohenheim in Stuttgart, Germany. Review of first edition: "This is an excellent book on mathematical, statistical and stochastic models in reliability. The authors have done an excellent job of unifying some of the stochastic models in reliability. The book is a good reference book but may not be suitable as a textbook for students in professional fields such as engineering. This book may be used for graduate level seminar courses for students who have had at least the first course in stochastic processes and some knowledge of reliability mathematics. It should be a good reference book for researchers in reliability mathematics." --Mathematical Reviews (2000)
Stochastic Models, Statistics and Their Applications: Dresden, Germany, March 2019 (Springer Proceedings in Mathematics & Statistics #294)
by Ansgar Steland Ewaryst Rafajłowicz Ostap OkhrinThis volume presents selected and peer-reviewed contributions from the 14th Workshop on Stochastic Models, Statistics and Their Applications, held in Dresden, Germany, on March 6-8, 2019. Addressing the needs of theoretical and applied researchers alike, the contributions provide an overview of the latest advances and trends in the areas of mathematical statistics and applied probability, and their applications to high-dimensional statistics, econometrics and time series analysis, statistics for stochastic processes, statistical machine learning, big data and data science, random matrix theory, quality control, change-point analysis and detection, finance, copulas, survival analysis and reliability, sequential experiments, empirical processes, and microsimulations. As the book demonstrates, stochastic models and related statistical procedures and algorithms are essential to more comprehensively understanding and solving present-day problems arising in e.g. the natural sciences, machine learning, data science, engineering, image analysis, genetics, econometrics and finance.
Stochastic Optimal Control and the U.S. Financial Debt Crisis
by Jerome L. SteinStochastic Optimal Control (SOC)--a mathematical theory concerned with minimizing a cost (or maximizing a payout) pertaining to a controlled dynamic process under uncertainty--has proven incredibly helpful to understanding and predicting debt crises and evaluating proposed financial regulation and risk management. Stochastic Optimal Control and the U.S. Financial Debt Crisis analyzes SOC in relation to the 2008 U.S. financial crisis, and offers a detailed framework depicting why such a methodology is best suited for reducing financial risk and addressing key regulatory issues. Topics discussed include the inadequacies of the current approaches underlying financial regulations, the use of SOC to explain debt crises and superiority over existing approaches to regulation, and the domestic and international applications of SOC to financial crises. Principles in this book will appeal to economists, mathematicians, and researchers interested in the U.S. financial debt crisis and optimal risk management.
Stochastic Optimization Methods in Finance and Energy
by Michael A. Dempster Giorgio Consigli Marida BertocchiThis 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.
Stochastic Optimization Methods: Applications in Engineering and Operations Research
by Kurt MartiThis book examines optimization problems that in practice involve random model parameters. It outlines the computation of robust optimal solutions, i.e., optimal solutions that are insensitive to random parameter variations, where appropriate deterministic substitute problems are needed. Based on the probability distribution of the random data and using decision theoretical concepts, optimization problems under stochastic uncertainty are converted into corresponding deterministic problems.Due to the probabilities and expectations involved, the book also shows how to apply approximative solution techniques. Several deterministic and stochastic approximation methods are provided: Taylor expansion methods, regression and response surface methods (RSM), probability inequalities, multiple linearization of survival/failure domains, discretization methods, convex approximation/deterministic descent directions/efficient points, stochastic approximation and gradient procedures, and differentiation formulas for probabilities and expectations.The fourth edition of this classic text has been carefully and thoroughly revised. It includes new chapters on the solution of stochastic linear programs by discretization of the underlying probability distribution, and on solving deterministic optimization problems by means of controlled random search methods and multiple random search procedures. It also presents a new application of stochastic optimization methods to machine learning problems with different loss functions. For the computation of optimal feedback controls under stochastic uncertainty, besides the open-loop feedback procedures, a new method based on Taylor expansions with respect to the gain parameters is presented. The book is intended for researchers and graduate students who are interested in stochastics, stochastic optimization, and control. It will also benefit professionals and practitioners whose work involves technical, economicand/or operations research problems under stochastic uncertainty.
Stochastic Optimization in Insurance
by Pablo Azcue Nora MulerThe 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.
Stochastic Processes
by Toshio NakagawaReliability theory is of fundamental importance for engineers and managers involved in the manufacture of high-quality products and the design of reliable systems. In order to make sense of the theory, however, and to apply it to real systems, an understanding of the basic stochastic processes is indispensable. As well as providing readers with useful reliability studies and applications, Stochastic Processes also gives a basic treatment of such stochastic processes as: the Poisson process,the renewal process,the Markov chain,the Markov process, andthe Markov renewal process.Many examples are cited from reliability models to show the reader how to apply stochastic processes. Furthermore, Stochastic Processes gives a simple introduction to other stochastic processes such as the cumulative process, the Wiener process, the Brownian motion and reliability applications. Stochastic Processes is suitable for use as a reliability textbook by advanced undergraduate and graduate students. It is also of interest to researchers, engineers and managers who study or practise reliability and maintenance.
Stochastic Processes with Applications to Finance (Chapman And Hall/crc Financial Mathematics Ser.)
by Masaaki KijimaFinancial engineering has been proven to be a useful tool for risk management, but using the theory in practice requires a thorough understanding of the risks and ethical standards involved. Stochastic Processes with Applications to Finance, Second Edition presents the mathematical theory of financial engineering using only basic mathematical tools
Stochastic Processes: From Physics to Finance
by Wolfgang Paul Jörg BaschnagelThis book introduces the theory of stochastic processes with applications taken from physics and finance. Fundamental concepts like the random walk or Brownian motion but also Levy-stable distributions are discussed. Applications are selected to show the interdisciplinary character of the concepts and methods. In the second edition of the book a discussion of extreme events ranging from their mathematical definition to their importance for financial crashes was included. The exposition of basic notions of probability theory and the Brownian motion problem as well as the relation between conservative diffusion processes and quantum mechanics is expanded. The second edition also enlarges the treatment of financial markets. Beyond a presentation of geometric Brownian motion and the Black-Scholes approach to option pricing as well as the econophysics analysis of the stylized facts of financial markets, an introduction to agent based modeling approaches is given.
Stochastic Programming
by Gerd InfangerFrom the Preface... The preparation of this book started in 2004, when George B. Dantzig and I, following a long-standing invitation by Fred Hillier to contribute a volume to his International Series in Operations Research and Management Science, decided finally to go ahead with editing a volume on stochastic programming. The field of stochastic programming (also referred to as optimization under uncertainty or planning under uncertainty) had advanced significantly in the last two decades, both theoretically and in practice. George Dantzig and I felt that it would be valuable to showcase some of these advances and to present what one might call the state-of- the-art of the field to a broader audience. We invited researchers whom we considered to be leading experts in various specialties of the field, including a few representatives of promising developments in the making, to write a chapter for the volume. Unfortunately, to the great loss of all of us, George Dantzig passed away on May 13, 2005. Encouraged by many colleagues, I decided to continue with the book and edit it as a volume dedicated to George Dantzig. Management Science published in 2005 a special volume featuring the "Ten most Influential Papers of the first 50 Years of Management Science." George Dantzig's original 1955 stochastic programming paper, "Linear Programming under Uncertainty," was featured among these ten. Hearing about this, George Dantzig suggested that his 1955 paper be the first chapter of this book. The vision expressed in that paper gives an important scientific and historical perspective to the book. Gerd Infanger
Stochastic Programming in Supply Chain Risk Management: Resilience, Viability, and Cybersecurity (International Series in Operations Research & Management Science #359)
by Tadeusz SawikThis book offers a novel multi-portfolio approach and stochastic programming formulations for modeling and solving contemporary supply chain risk management problems. The focus of the book is on supply chain resilience under propagated disruptions, supply chain viability under severe crises, and supply chain cybersecurity under direct and indirect cyber risks. The content is illustrated with numerous computational examples, some of which are modeled on real-world supply chains subject to severe multi-regional or global crises, such as pandemics. In the computational examples, the proposed stochastic programming models are solved using an advanced algebraic modeling language AMPL and GUROBI solver. The book seamlessly continues the journey begun in the author’s previously published book “Supply Chain Disruption Management: Using Stochastic Mixed Integer Programming.” It equips readers with the knowledge, tools, and managerial insights needed to effectively model and address modern supply chain risk management challenges. As such, the book is designed for practitioners and researchers who are interested in supply chain risk management. Master’s and Ph.D. students in disciplines like supply chain management, operations research, industrial engineering, applied mathematics, and computer science will also find the book a valuable resource.
Stochastic Programming: Modeling Decision Problems Under Uncertainty (Graduate Texts in Operations Research #274)
by Ward Romeijnders Willem K. Klein Haneveld Maarten H. van der VlerkThis book provides an essential introduction to Stochastic Programming, especially intended for graduate students. The book begins by exploring a linear programming problem with random parameters, representing a decision problem under uncertainty. Several models for this problem are presented, including the main ones used in Stochastic Programming: recourse models and chance constraint models. The book not only discusses the theoretical properties of these models and algorithms for solving them, but also explains the intrinsic differences between the models. In the book’s closing section, several case studies are presented, helping students apply the theory covered to practical problems. The book is based on lecture notes developed for an Econometrics and Operations Research course for master students at the University of Groningen, the Netherlands - the longest-standing Stochastic Programming course worldwide.
Stochastic Volatility Modeling (Chapman and Hall/CRC Financial Mathematics Series)
by Lorenzo BergomiPacked with insights, Lorenzo Bergomi's Stochastic Volatility Modeling explains how stochastic volatility is used to address issues arising in the modeling of derivatives, including:Which trading issues do we tackle with stochastic volatility? How do we design models and assess their relevance? How do we tell which models are usable and when does c
Stochastic Volatility and Realized Stochastic Volatility Models (SpringerBriefs in Statistics)
by Makoto Takahashi Yasuhiro Omori Toshiaki WatanabeThis treatise delves into the latest advancements in stochastic volatility models, highlighting the utilization of Markov chain Monte Carlo simulations for estimating model parameters and forecasting the volatility and quantiles of financial asset returns. The modeling of financial time series volatility constitutes a crucial aspect of finance, as it plays a vital role in predicting return distributions and managing risks. Among the various econometric models available, the stochastic volatility model has been a popular choice, particularly in comparison to other models, such as GARCH models, as it has demonstrated superior performance in previous empirical studies in terms of fit, forecasting volatility, and evaluating tail risk measures such as Value-at-Risk and Expected Shortfall. The book also explores an extension of the basic stochastic volatility model, incorporating a skewed return error distribution and a realized volatility measurement equation. The concept of realized volatility, a newly established estimator of volatility using intraday returns data, is introduced, and a comprehensive description of the resulting realized stochastic volatility model is provided. The text contains a thorough explanation of several efficient sampling algorithms for latent log volatilities, as well as an illustration of parameter estimation and volatility prediction through empirical studies utilizing various asset return data, including the yen/US dollar exchange rate, the Dow Jones Industrial Average, and the Nikkei 225 stock index. This publication is highly recommended for readers with an interest in the latest developments in stochastic volatility models and realized stochastic volatility models, particularly in regards to financial risk management.
Stochastics of Environmental and Financial Economics
by Fred Espen Benth Giulia Di NunnoThese Proceedings offer a selection of peer-reviewed research and survey papers by some of the foremost international researchers in the fields of finance, energy, stochastics and risk, who present their latest findings on topical problems. The papers cover the areas of stochastic modeling in energy and financial markets; risk management with environmental factors from a stochastic control perspective; and valuation and hedging of derivatives in markets dominated by renewables, all of which further develop the theory of stochastic analysis and mathematical finance. The papers were presented at the first conference on "Stochastics of Environmental and Financial Economics (SEFE)", being part of the activity in the SEFE research group of the Centre of Advanced Study (CAS) at the Academy of Sciences in Oslo, Norway during the 2014/2015 academic year.
Stochastische Modelle der aktuariellen Risikotheorie: Eine mathematische Einführung (Masterclass)
by Riccardo GattoDieses Buch führt mathematisch präzise in die stochastischen Modelle ein, die bei der Bewertung von Schadensbeträgen für Versicherungen von besonderer Bedeutung sind. Abgedeckt werden Modelle für kleine und große Schadensbeträge, Modelle für extreme Ereignisse, Risikomaße, sowie die stochastischen Prozesse der aktuariellen Risikotheorie: Zählprozesse, zusammengesetzte Prozesse, Erneuerungsprozesse und Poisson-Prozesse. Zentrales Thema ist die Bestimmung der Ruinwahrscheinlichkeit des Versicherers. In diesem Zusammenhang werden analytische Lösungen, asymptotische Approximationen sowie numerische Algorithmen wie die Monte-Carlo-Simulation vorgestellt. Gute Grundkenntnisse in der Wahrscheinlichkeitstheorie werden vorausgesetzt, doch ein Anhang mit den wichtigsten Resultaten erleichtert die Lektüre dieses Buches. Das Buch ist geeignet für fortgeschrittene Bachelor- oder Masterstudierende der Mathematik oder Statistik mit entsprechender Vertiefungsrichtung. Darüber hinaus richtet es sich an Kandidaten, die das Diplom der Schweizerischen Aktuarvereinigung (SAV) erwerben oder sich auf das Diplom der Society of Actuaries (SOA) vorbereiten möchten. Auch praktizierende Versicherungsmathematiker, die ihre technischen Kenntnisse vertiefen wollen, werden angesprochen. Die vorliegende zweite Auflage enthält theoretische Ergänzungen, insbesondere Resultate über die Fluktuationen der Summe und der zusammengesetzten Summe, d.h. des Gesamtschadensbetrages einer Periode. Darüber hinaus erleichtern nun neue Aufgaben verschiedener Schwierigkeitsgrade und mit ausführlichen Lösungen das Selbststudium.
Stochastische Risikomodellierung und statistische Methoden: Angewandte Stochastik für die aktuarielle Praxis (Statistik und ihre Anwendungen)
by Richard Herrmann Torsten Becker Viktor Sandor Dominik Schäfer Ulrich Wellisch Christian Heumann Stefan PilzDieses Buch vereinigt Konzepte und Methoden der stochastischen Modellbildung, der statistischen Analyse und der aktuariellen Anwendung in einem Band. Dabei wird eine kompakte, aber dennoch für Theoretiker wie Praktiker verständliche und interessante Darstellung der Themen Risikobewertung, Datenanalyse, Parameterschätzung, verallgemeinerte lineare Regression, stochastische Prozesse und Differenzialrechnung, Zeitreihen, biometrische Modelle, Credibility sowie Simulation gegeben. Zahlreiche Beispiele illustrieren die Anwendung der Konzepte in der aktuariellen Praxis, wobei auf Modelle aus der Personen- und Sachversicherung und der Finanzmathematik eingegangen wird.
Stochastische Szenariosimulation in der Unternehmenspraxis: Risikomodellierung, Fallstudien, Umsetzung in R
by Frank Romeike Manfred StallingerDas Buch zeigt, wie Unternehmen durch die Anwendung der stochastischen Szenariosimulation ein wirksames und effizientes Risikomanagement umsetzen können. Die einfache Darstellung der Grundbegriffe und Methoden der Stochastik, ergänzt um Beispiele und Fallstudien aus der Praxis, geben dem Leser ein praxiserprobtes Toolkit an Instrumenten für die praktische Umsetzung mit auf den Weg.Die Autoren führen zunächst in die faszinierende Welt des Zufalls ein und erklären die Grundbegriffe der deskriptiven und auch für das Risikomanagement wichtigen Inferenzstatistik. Anschließend geben sie einen Einblick in erforderliche Wahrscheinlichkeitsverteilungen mit deren Risikomaße und Anwendung in der Praxis und beschreiben Verfahren der Risikoaggregation und der Effizienzbewertung von Risiko-Abmilderungsmaßnahmen. Diese Einführung wird begleitet durch konkrete Fallbeispiele, die in der Programmumgebung „R“ umgesetzt wurden.Ergänzend zur Einführung in die spannende Welt der Stochastik werden in einem separaten Kapitel typische Fallstudien aus der Praxis präsentiert. Die Beispiele werden als Sourcecode in der Programmiersprache „R“ für eine praktische Anwendung sowohl im Buch als auch in elektronischer Form von den Autoren zum Download bereitgestellt.
Stochastisches Bestandsmanagement: Grundmodelle für Betriebswirte
by Christian BrabänderDieses Buch erklärt grundlegend das betriebliche Gestaltungsfeld Bestandsmanagement und führt die relevanten Begriffe und Formeln ein. Es beschäftigt sich mit Antworten auf die Fragen, wann Produktbestellungen aufgegeben werden und wie viel auf einmal bestellt werden soll. Dabei werden die Unsicherheiten des zu versorgenden, konsumierenden Prozesses und des Nachschub-Prozesses berücksichtigt. Diese Aufgaben können mithilfe von Modellen optimal gelöst werden. Die wichtigsten Modelle zur Beantwortung der Fragen nach dem Wann und dem Wieviel werden einsteigerfreundlich erklärt und ihre Anwendung an einfachen Beispielen gezeigt. Nacheinander werden das klassische Bestellmengenmodel, das Newsvendor-Modell, das kontinuierliche und das periodische Bestandsmodell erläutert. Weiterführend werden die Anwendungsfälle Zentralisierung und Risikomanagement aus Sicht der Bestandsführung vertieft.