1) What is a stochastic model? Provide an example. 2) Risk management is a major

Question

1) What is a stochastic model? Provide an example.

2) Risk management is a major usage of Monte Carol simulation. True (T) or False (F)?

3) Risk management uses data to assist in decision making to avoid risks that lose money and assists in decision making for opportunities to be more profitable?

4) Taking risks is always bad. True (T) or False (F)?

5) Randomness (generating random values for input) is included in Monte Carlo analysis. True (T) or False (F)?

6) What is Montel Carlo simulation? Provide an example of how it might be used.

7) This problem assumes a normal bell shaped curve. Problem: With a mean of 1000 and a standard deviation of 100, what values of the population would tend to fall within 3 standard deviations of the mean? What percentage of the population would tend to fall within 3 standard deviations of the mean?

8) Monte Carlo sampling selects random values (variates) independently over a short, partial range of the possible values of the entire distribution. Provide your answer: True (T) or False (F) ?

9) What is a sensitivity chart? Provide an example of how it might be used.

10) The ‘Flaw of Averages’ tells us that the evaluation of a model output using the average value of the input is always equal to the average value of the outputs when evaluated with each of the input values. (T – True) or (F-False)?

Explanation / Answer

Q1) What is a stochastic model? - A stochastic model is a tool for estimating probability distributions of potential outcomes by allowing for random variation in one or more inputs over time. The random variation is usually based on fluctuations observed in historical data for a selected period using standard time-series techniques.

Example - The Monte Carlo simulation is one example of a stochastic model; when used for portfolio evaluation, various simulations of how a portfolio may perform are developed based on probability distributions of individual stock returns

Q2) Risk management is a major usage of Monte Carol simulation? - TRUE (Because, Monte Carlo simulation, or probability simulation, is a technique used to understand the impact of risk and uncertainty in financial, project management, cost, and other forecasting models. So Risk management is required for this)

Q3) Risk management uses data to assist in decision making to avoid risks that lose money and assists in decision making for opportunities to be more profitable? - TRUE

Q4) Taking risks is always bad? - FALSE (Sometimes we do need to take risks to achieve something BIG)

Q5) Randomness (generating random values for input) is included in Monte Carlo analysis - TRUE (Monte Carlo methods (or Monte Carlo experiments) are a broad class of computational algorithms that rely on repeated random sampling to obtain numerical results. Their essential idea is using randomness to solve problems that might be deterministic in principle)

Q6) What is Montel Carlo simulation - Monte Carlo methods (or Monte Carlo experiments) are a broad class of computational algorithms that rely on repeated random sampling to obtain numerical results. Their essential idea is using randomness to solve problems that might be deterministic in principle. They are often used in physical and mathematical problems and are most useful when it is difficult or impossible to use other approaches.

Example - In physics-related problems, Monte Carlo methods are useful for simulating systems with many coupled degrees of freedom, such as fluids, disordered materials, strongly coupled solids, and cellular structures.

Q8) Monte Carlo sampling selects random values (variates) independently over a short, partial range of the possible values of the entire distribution - TRUE

Q9) Sensitivity Chart - allows you to determine the influence that each uncertain model input has individually on an output variable based on its correlation with the output variables. Displays rankings of uncertain variables according to their impact on an output cell.

Provide an example of how it might be used. - It tells us which uncertain variables influence output variables the most and which would benefit from better estimates. Tells which uncertain variables influence output variables the least and can be ignored or discarded altogether. Provides understanding of how the uncertain variables affect your model, allows you to develop more realistic spreadsheet models and improve the accuracy of your results.

Q10) The ‘Flaw of Averages’ tells us that the evaluation of a model output using the average value of the input is always equal to the average value of the outputs when evaluated with each of the input values - FALSE (F)

Correct Definition:- the evaluation of a model output using the average value of the input is not necessarily equal to the average value of the outputs when evaluated with each of the input values.

Please let me know in case of any clarifications required. Thanks!

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