Financial Risk Manager Part 1

Explanation

Option A is correct because:

• Monte Carlo Simulation generates variables and shocks from a particular theoretical distribution (e.g., normal distribution, lognormal distribution)
• Bootstrapping generates variables from observed data through random sampling with replacement from the historical dataset

Option B is incorrect because it reverses the correct relationship - Monte Carlo uses theoretical distributions while bootstrapping uses observed data.

Option C is incorrect because drawing indices when selecting data is characteristic of bootstrapping, not Monte Carlo Simulation.

Option D is incorrect because:

• Bootstrapping does not alter the underlying distribution; it uses random sampling from observed data to create new datasets with similar features
• Monte Carlo Simulation can involve generating variables from theoretical distributions, not strictly historical data

Key Differences:

• Monte Carlo: Uses theoretical probability distributions to generate random variables
• Bootstrapping: Uses resampling from actual historical data to create new datasets
• Monte Carlo is model-based (requires assumptions about distributions)
• Bootstrapping is data-driven (uses actual observed data)