Financial Risk Manager Part 1
Get started today
Ultimate access to all questions.
Deep dive into the quiz with AI chat providers.
We prepare a focused prompt with your quiz and certificate details so each AI can offer a more tailored, in-depth explanation.
Tom Breitling, FRM, is working on building a model using a Monte Carlo Simulation. However, he is concern about the accuracy of the simulation. Which of the following is not a way of increasing the accuracy of the simulation?
Other
Community
NNikitesh
A
Increasing the number of generated scenarios
B
Variance reduction techniques
C
Decreasing the standard error
D
Controlling the standard deviation
Explanation:
Standard deviation cannot be controlled. For accuracy, we either reduce the standard error estimate by increasing the number of replication or we use variance reduction techniques.
Detailed Explanation:
In Monte Carlo simulations, accuracy can be improved through:
- Increasing the number of generated scenarios (Option A): More replications reduce sampling error and provide more precise estimates.
- Variance reduction techniques (Option B): Methods like antithetic variates, control variates, or importance sampling reduce the variance of estimates without increasing computational cost.
- Decreasing the standard error (Option C): Standard error = σ/√n, so decreasing it improves precision. This can be achieved by increasing sample size.
However, controlling the standard deviation (Option D) is not a valid method because:
- Standard deviation is a property of the underlying distribution being simulated
- It represents the inherent risk or volatility of the process
- Attempting to 'control' it would change the fundamental characteristics being modeled
- In financial risk modeling, standard deviation (volatility) is an input parameter, not something we control to improve simulation accuracy
Comments
Loading comments...