School of Business‚ The George Washington University‚ Washington‚ DC 20052‚ USA. E-mail: kwak@gwu.edu A b stra ct Monte Carlo simulation is a useful technique for modeling and analyzing real-world systems and situations. This paper is a conceptual paper that explores the applications of Monte Carlo simulation for managing project risks and uncertainties. The benefits of Monte Carlo simulation are using quantified data‚ allowing project managers to better justify and communicate their arguments
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Monte Carlo Simulation Risk analysis is part of every decision we make. We are constantly faced with uncertainty‚ ambiguity‚ and variability. And even though we have unprecedented access to information‚ we can’t accurately predict the future. Monte Carlo simulation (also known as the Monte Carlo Method) lets you see all the possible outcomes of your decisions and assess the impact of risk‚ allowing for better decision making under uncertainty What is Monte Carlo simulation? Monte Carlo simulation
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| 1. Research one of the Monte Carlo analysis Products listed in the Topic Notes I reviewed the following products that developed Monte Carlo analysis package: Monte Carlo Simulation within Microsoft Excel Data Analysis and Business Palisade ’s @RiskModeling Oracle ’s Crystal Ball‚ RiskDecision ’s Predict! Risk Controller I really found two of the four solutions excellent. 1. Monte Carlo Simulation within Mocrosoft Excel I really was amazed by by Monte Carlo Simulation that is available
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Monte Carlo Simulation in Finance for Calculating European Options Value 1. Introduction An option is a financial instrument whose value depends on a value of underlying security. Options trade started in 1973 at the Chicago Board Options Exchange (Hull‚ Fundamentals of futures and options markets 2008). Nowadays‚ options have become a crucial tool in finance; they have become valuable both for financial institutions and investors. Options are attractive to investors since they have great effect
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Application of Monte Carlo Simulation in Capital Budgeting | | |by Prit‚ Aug 2‚ 2008 | |The usefulness of Monte carlo Simulation in Capital Budgeting and the processes involved in Monte Carlo Simulation. It also | |highlights the advantages in some situation compared to other deterministic models where uncertainty is the norm. | |[pic]
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REAL OPTIONS: STATE OF THE PRACTICE by Alex Triantis‚ University of Maryland‚ and Adam Borison‚ Applied Decision Analysis/ PricewaterhouseCoopers1 n an economic environment characterized by rapid change‚ great uncertainty‚ and the need for flexibility‚ it has become increasingly important for corporate managers to use investment evaluation tools and processes that properly account for both uncertainty and the company’s ability to react to new information. Real options has emerged as an approach
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the largest B2C online shopping website in China in the area of supply chain management. The main issue is under the assumption of using two logistic methods including self-run logistic mode and third-party logistic mode for the company to ship the products. Under this condition‚ we use the Monte-Carlo Model to calculate the total cost of two methods and then to analysis two models in specific to work out which mode cost less and has the high efficiency. At last‚ we offer our solutions and recommendations
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Analysis (Page 1 of 2) Course: QNT 5040 LEARNING OUTCOME/S:(see syllabus) Date:02/10/2014 PURPOSE: To facilitate effective decision making under uncertain conditions by quantifying risk. Name of Student: Stephanie Preston VALIDITY: Best practices in Monte Carlo simulation. Name of Faculty: Professor Yurova COMPANION DOCUMENTS: Assignment and format instructions‚ Case Earning maximum points in each box in ‘PROFICIENT’ column and / or points in columns to the right of ‘PROFICIENT’ meets standard. <<<<<<<<<<
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MOUNT. Computerworld‚ 36(6)‚ 1. Heck‚ M. (1993‚ February 1). High-end project managers: coordinate enterprisewide projects with desktop flexibility. InfoWorld‚ 15(5)‚ 59+. Lanza‚ R. B. (2003). Getting to realistic estimates and project plans: A Monte Carlo approach. Information Strategy The Executives Journal‚ 19(4)‚ 26. Larson‚ E. W.‚ & Gray‚ C. F. (2011). Project Management The Managerial Process. New York: McGraw-Hill/Irwin. Prince‚ J. (2007). Best Practices for LAN Security Projects. Business
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Address: surban1@jhu.edu Teaching Assistant Ben Brock: bbrock1@jhu.edu Office Hours Saturdays‚ 10:00am – 12:30pm at DC Center (room 201) or by appointment Required Text and Learning Materials: Stochastic Simulation and Applications in Finance with MATLAB Programs‚ (2008)‚ Huu Tue Huynh‚ Van Son Lai‚ Issouf Soumare (HLS) [this book may be available as an e-book for students] MATLAB: An Introduction with Applications 4th Edition (2010) or earlier Editions‚ Amos Gilat (AG) Software:
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