Risikoaggregation Und Monte Carlo Simulation
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Risikoaggregation Und Monte Carlo Simulation
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Author : Werner Gleißner
language : de
Publisher: Springer-Verlag
Release Date : 2018-12-13
Risikoaggregation Und Monte Carlo Simulation written by Werner Gleißner and has been published by Springer-Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-13 with Business & Economics categories.
Dieser Band stellt praxisorientiert die Monte-Carlo-Simulation (Risikosimulation) vor, die es ermöglicht, den Gesamtrisikoumfang eines Unternehmens oder Projektes zu berechnen (Risikoaggregation) und mögliche „bestandsgefährdende Entwicklungen“ aus Kombinationseffekten von Einzelrisiken zu erkennen (Kernanforderung an ein Risikomanagement nach §91 Abs. 2 AktG). Ausgehend von einer Einführung zu Risikoanalyse und Risikoquantifizierung wird gezeigt, wie durch die Verbindung von Unternehmensplanung und Risikoanalyse mittels Monte-Carlo-Simulation eine Risikoaggregation durchgeführt wird (mit Excel und der Simulationssoftware Crystal Ball).
Risikoanalyse In Industrieunternehmen
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Author : Diana Wieske
language : de
Publisher: diplom.de
Release Date : 2006-05-05
Risikoanalyse In Industrieunternehmen written by Diana Wieske and has been published by diplom.de this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-05-05 with Business & Economics categories.
Inhaltsangabe:Einleitung: Risk and uncertainty are key features of most business and government problems and need to be understood for rational decisions to be made. Das Betreiben eines Geschäftes oder einer Unternehmung ist untrennbar mit dem Eingehen von Risiken verbunden. Die zunehmende Umweltdynamik und -diskontinuität sowie die steigende Komplexität der Unternehmensstrukturen erhöhen das Risikopotenzial unternehmerischer Aktivitäten. Unternehmen müssen in dynamischen Märkten und in einer dynamischen Umwelt Entscheidungen auf Basis unvollkommener Informationen treffen darin besteht ihr unternehmerisches Risiko. Für Marktakteure ist es unerlässlich, sich kritisch mit der eigenen Unternehmensplanung und insbesondere mit ihren künftigen Entwicklungen auseinander zu setzen. Die damit verbundenen Fragen nach den Chancen und Risiken sind intensiv zu diskutieren. Welche Möglichkeiten besitzt die Unternehmensführung, um der wachsenden Ungewissheit und den daraus resultierenden Risiken zu begegnen? Gefahren und Chancen zu identifizieren, zu analysieren und zu bewerten sowie ihre Auswirkungen auf die Kapitalkosten und damit auf den Unternehmenswert zu zeigen, ist Aufgabe des Risikomanagements. Die Einbeziehung der Risiken in einer komplexen Umwelt von mehreren einander beeinflussenden Faktoren kann mit Methoden der quantitativen Risikoanalyse erfolgen. Die vorliegende Arbeit hat zum Ziel, durch Techniken der Risikoanalyse und insbesondere der Risikoquantifizierung nach Identifizierung der unterspezifischen Einzelrisiken eine Zusammenfassung der Einzelrisiken zu einer Gesamtrisikoposition zu bestimmen. Zur Risikomodellierung stehen mehrere Techniken zur Verfügung, wobei eine in der Literatur häufig propagierte Methode zur Risikobewertung die Monte Carlo Simulation darstellt. Diese Gesamtrisikobestimmung erweist sich u.a. als problematisch, weil der Aspekt der Sicherheit ein abstraktes, hoch aggregiertes und schwer fassbares Konstrukt darstellt. Darüber hinaus erweist sich dieses Vorhaben als methodisch relativ schwierig. Die Bestimmung der Gesamtrisikoposition wird in der Praxis des Risikomanagements oft vernachlässigt oder mit ungeeigneten Methoden bearbeitet. Simulationsansätze stellen einerseits geeignete Methoden zur Risikoquantifizierung dar und erfordern andererseits ein hohes Maß an Know-how. Sie geben dem Risikomanager ein geeignetes Instrument an die Hand unter Prüfung der gewählten Annahmen im Modell, ein qualitatives Ergebnis einer [...]
The Monte Carlo Simulation Method For System Reliability And Risk Analysis
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Author : Enrico Zio
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-11-02
The Monte Carlo Simulation Method For System Reliability And Risk Analysis written by Enrico Zio and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-11-02 with Technology & Engineering categories.
Monte Carlo simulation is one of the best tools for performing realistic analysis of complex systems as it allows most of the limiting assumptions on system behavior to be relaxed. The Monte Carlo Simulation Method for System Reliability and Risk Analysis comprehensively illustrates the Monte Carlo simulation method and its application to reliability and system engineering. Readers are given a sound understanding of the fundamentals of Monte Carlo sampling and simulation and its application for realistic system modeling. Whilst many of the topics rely on a high-level understanding of calculus, probability and statistics, simple academic examples will be provided in support to the explanation of the theoretical foundations to facilitate comprehension of the subject matter. Case studies will be introduced to provide the practical value of the most advanced techniques. This detailed approach makes The Monte Carlo Simulation Method for System Reliability and Risk Analysis a key reference for senior undergraduate and graduate students as well as researchers and practitioners. It provides a powerful tool for all those involved in system analysis for reliability, maintenance and risk evaluations.
Simulation And The Monte Carlo Method
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Author : Reuven Y. Rubinstein
language : en
Publisher: John Wiley & Sons
Release Date : 2016-11-07
Simulation And The Monte Carlo Method written by Reuven Y. Rubinstein and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-07 with Mathematics categories.
This accessible new edition explores the major topics in Monte Carlo simulation that have arisen over the past 30 years and presents a sound foundation for problem solving Simulation and the Monte Carlo Method, Third Edition reflects the latest developments in the field and presents a fully updated and comprehensive account of the state-of-the-art theory, methods and applications that have emerged in Monte Carlo simulation since the publication of the classic First Edition over more than a quarter of a century ago. While maintaining its accessible and intuitive approach, this revised edition features a wealth of up-to-date information that facilitates a deeper understanding of problem solving across a wide array of subject areas, such as engineering, statistics, computer science, mathematics, and the physical and life sciences. The book begins with a modernized introduction that addresses the basic concepts of probability, Markov processes, and convex optimization. Subsequent chapters discuss the dramatic changes that have occurred in the field of the Monte Carlo method, with coverage of many modern topics including: Markov Chain Monte Carlo, variance reduction techniques such as importance (re-)sampling, and the transform likelihood ratio method, the score function method for sensitivity analysis, the stochastic approximation method and the stochastic counter-part method for Monte Carlo optimization, the cross-entropy method for rare events estimation and combinatorial optimization, and application of Monte Carlo techniques for counting problems. An extensive range of exercises is provided at the end of each chapter, as well as a generous sampling of applied examples. The Third Edition features a new chapter on the highly versatile splitting method, with applications to rare-event estimation, counting, sampling, and optimization. A second new chapter introduces the stochastic enumeration method, which is a new fast sequential Monte Carlo method for tree search. In addition, the Third Edition features new material on: • Random number generation, including multiple-recursive generators and the Mersenne Twister • Simulation of Gaussian processes, Brownian motion, and diffusion processes • Multilevel Monte Carlo method • New enhancements of the cross-entropy (CE) method, including the “improved” CE method, which uses sampling from the zero-variance distribution to find the optimal importance sampling parameters • Over 100 algorithms in modern pseudo code with flow control • Over 25 new exercises Simulation and the Monte Carlo Method, Third Edition is an excellent text for upper-undergraduate and beginning graduate courses in stochastic simulation and Monte Carlo techniques. The book also serves as a valuable reference for professionals who would like to achieve a more formal understanding of the Monte Carlo method. Reuven Y. Rubinstein, DSc, was Professor Emeritus in the Faculty of Industrial Engineering and Management at Technion-Israel Institute of Technology. He served as a consultant at numerous large-scale organizations, such as IBM, Motorola, and NEC. The author of over 100 articles and six books, Dr. Rubinstein was also the inventor of the popular score-function method in simulation analysis and generic cross-entropy methods for combinatorial optimization and counting. Dirk P. Kroese, PhD, is a Professor of Mathematics and Statistics in the School of Mathematics and Physics of The University of Queensland, Australia. He has published over 100 articles and four books in a wide range of areas in applied probability and statistics, including Monte Carlo methods, cross-entropy, randomized algorithms, tele-traffic c theory, reliability, computational statistics, applied probability, and stochastic modeling.
Monte Carlo Simulation With Applications To Finance
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Author : Hui Wang
language : en
Publisher: CRC Press
Release Date : 2012-05-22
Monte Carlo Simulation With Applications To Finance written by Hui Wang and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-05-22 with Business & Economics categories.
Developed from the author’s course on Monte Carlo simulation at Brown University, Monte Carlo Simulation with Applications to Finance provides a self-contained introduction to Monte Carlo methods in financial engineering. It is suitable for advanced undergraduate and graduate students taking a one-semester course or for practitioners in the financial industry. The author first presents the necessary mathematical tools for simulation, arbitrary free option pricing, and the basic implementation of Monte Carlo schemes. He then describes variance reduction techniques, including control variates, stratification, conditioning, importance sampling, and cross-entropy. The text concludes with stochastic calculus and the simulation of diffusion processes. Only requiring some familiarity with probability and statistics, the book keeps much of the mathematics at an informal level and avoids technical measure-theoretic jargon to provide a practical understanding of the basics. It includes a large number of examples as well as MATLAB® coding exercises that are designed in a progressive manner so that no prior experience with MATLAB is needed.
The Monte Carlo Simulation Method For System Reliability And Risk Analysis
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Author : Springer
language : en
Publisher:
Release Date : 2012-11-01
The Monte Carlo Simulation Method For System Reliability And Risk Analysis written by Springer and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-11-01 with categories.
Handbook In Monte Carlo Simulation
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Author : Paolo Brandimarte
language : en
Publisher: John Wiley & Sons
Release Date : 2014-06-17
Handbook In Monte Carlo Simulation written by Paolo Brandimarte and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-06-17 with Business & Economics categories.
An accessible treatment of Monte Carlo methods, techniques, and applications in the field of finance and economics Providing readers with an in-depth and comprehensive guide, the Handbook in Monte Carlo Simulation: Applications in Financial Engineering, Risk Management, and Economics presents a timely account of the applicationsof Monte Carlo methods in financial engineering and economics. Written by an international leading expert in thefield, the handbook illustrates the challenges confronting present-day financial practitioners and provides various applicationsof Monte Carlo techniques to answer these issues. The book is organized into five parts: introduction andmotivation; input analysis, modeling, and estimation; random variate and sample path generation; output analysisand variance reduction; and applications ranging from option pricing and risk management to optimization. The Handbook in Monte Carlo Simulation features: An introductory section for basic material on stochastic modeling and estimation aimed at readers who may need a summary or review of the essentials Carefully crafted examples in order to spot potential pitfalls and drawbacks of each approach An accessible treatment of advanced topics such as low-discrepancy sequences, stochastic optimization, dynamic programming, risk measures, and Markov chain Monte Carlo methods Numerous pieces of R code used to illustrate fundamental ideas in concrete terms and encourage experimentation The Handbook in Monte Carlo Simulation: Applications in Financial Engineering, Risk Management, and Economics is a complete reference for practitioners in the fields of finance, business, applied statistics, econometrics, and engineering, as well as a supplement for MBA and graduate-level courses on Monte Carlo methods and simulation.
Monte Carlo Simulation And Finance
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Author : Don L. McLeish
language : en
Publisher: John Wiley & Sons
Release Date : 2011-09-13
Monte Carlo Simulation And Finance written by Don L. McLeish and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-09-13 with Business & Economics categories.
Monte Carlo methods have been used for decades in physics, engineering, statistics, and other fields. Monte Carlo Simulation and Finance explains the nuts and bolts of this essential technique used to value derivatives and other securities. Author and educator Don McLeish examines this fundamental process, and discusses important issues, including specialized problems in finance that Monte Carlo and Quasi-Monte Carlo methods can help solve and the different ways Monte Carlo methods can be improved upon. This state-of-the-art book on Monte Carlo simulation methods is ideal for finance professionals and students. Order your copy today.
Monte Carlo Methods In Finance
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Author : Peter Jäckel
language : en
Publisher: John Wiley & Sons
Release Date : 2002-04-03
Monte Carlo Methods In Finance written by Peter Jäckel and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002-04-03 with Business & Economics categories.
An invaluable resource for quantitative analysts who need to run models that assist in option pricing and risk management. This concise, practical hands on guide to Monte Carlo simulation introduces standard and advanced methods to the increasing complexity of derivatives portfolios. Ranging from pricing more complex derivatives, such as American and Asian options, to measuring Value at Risk, or modelling complex market dynamics, simulation is the only method general enough to capture the complexity and Monte Carlo simulation is the best pricing and risk management method available. The book is packed with numerous examples using real world data and is supplied with a CD to aid in the use of the examples.
Simulation And The Monte Carlo Method
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Author : Reuven Y. Rubinstein
language : en
Publisher: John Wiley & Sons
Release Date : 2009-09-25
Simulation And The Monte Carlo Method written by Reuven Y. Rubinstein and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-09-25 with Mathematics categories.
This book provides the first simultaneous coverage of the statistical aspects of simulation and Monte Carlo methods, their commonalities and their differences for the solution of a wide spectrum of engineering and scientific problems. It contains standard material usually considered in Monte Carlo simulation as well as new material such as variance reduction techniques, regenerative simulation, and Monte Carlo optimization.