Financial Risk Manager Part 1

Explanation:

Monte Carlo Simulation and bootstrapping are both simulation techniques used in quantitative analysis, but they differ fundamentally in their data generation approach:

1. Monte Carlo Simulation:

   • Generates random variables and shocks from a specified theoretical distribution (e.g., normal distribution, uniform distribution)
   • Requires assumptions about the underlying distribution of the data
   • Useful when the theoretical distribution is known or assumed

2. Bootstrapping:

   • Generates variables by random sampling from observed historical data
   • Does not require assumptions about the underlying distribution
   • Creates new datasets by resampling from the original dataset with replacement
   • Preserves the empirical distribution of the observed data

Analysis of Options:

• Option A: Correctly describes the difference - Monte Carlo uses theoretical distributions while bootstrapping uses observed data.
• Option B: Incorrect - Reverses the definitions of the two methods.
• Option C: Incorrect - This describes bootstrapping, not Monte Carlo simulation. In bootstrapping, random indices are drawn to create resampled datasets.
• Option D: Incorrect - Since Option A is correct, this cannot be true.

Key Distinction: Monte Carlo is parametric (requires distribution assumptions) while bootstrapping is non-parametric (uses empirical data).