This is the third post about the Companion and Self-Testing Question List for my recent book Business Risk and Simulation Modelling in Practice: Using Excel, VBA and @RISK. As mentioned in earlier posts, the complete list of questions posted in this series will also shortly be available for free on the John Wiley web-site.
As a reminder, the full series of posts correspond to the Chapters of the book as follows:
- Post I: Risk Assessment Context and Processes (Chapters 1 and 2 of book)
- Post II: Risk Assessment, Quantification and Modelling: Approaches, Benefits and Challenges (Chapters 3, 4 and 5)
- Post III: Principles of Simulation Methods (Chapter 6)
- Post IV: Core Principles in the Design of Risk Models (Chapter 7)
- Post V: Measuring Risk using Statistics of Distributions (Chapter 8)
- Post VI: The Selection of Distributions for Use in Risk Models (Chapters 9 and 10)
- Post VII: Modelling Dependencies between Sources of Risk (Chapter 11)
- Post VIII: Using Excel/VBA for Simulation Modelling (Chapter 12)
- Post IX: Using @RISK for Simulation Modelling (Chapter 13)
This is Post III, so the Companion Question List concerns Principles of Simulation Methods (Chapter 6):
- Describe in one sentence what it meant by Monte Carlo Simulation.
- What are the origins of the term Monte Carlo Simulation?
- What are the main uses of simulation methods?
- To what extent are simulation modelling and risk modelling the same, and what are the differences?
- Describe how the simultaneous variation of multiple inputs in a model automatically creates a distribution of output values?
- Why are there typically more values in the central area of a possible range than in the tails at either end?
- What is the role and interpretation of using input distributions in risk models?
- What are the key questions about the range of outcomes that are addressable through risk models?
- What is the typical relationship between the number of recalculations (iterations) that are used when running a simulation model and the error of the results compared to the exact solution?
- What are some differences in the outputs of traditional sensitivity and scenario analysis, and the results of quantitative risk model, and how may these affect decisions that are taken?
- What forms of generic optimisation situations exist? What is the relationship between optimisation and simulation situations?
- What is meant by closed-form or analytic solutions?
- Describe how Monte Carlo Simulation can be used as a numerical method (even though an analytic solution is known) for the following cases: a) the valuation of a European option, b) the calculation of the value of π [these exercises may require a small amount of external research or referring to elsewhere in the text].
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