Jun 3, 2024

**Monte Carlo simulation**is a widely applied finance technique.- Used to deal with uncertainty in complex situations.
- Generates many random observations for uncertain variables to create a joint distribution of possible outcomes.

**Determine Variables**- Identify variables representing sources of uncertainty.

**Assume Distributions**- Choose a distribution for each variable.
- Choice of distribution affects model predictions and is at the analyst’s discretion.

**Execute Iterations**- Perform iterations with possible realizations for these variables.

**Repeat Iterations**- Repeat the 3rd step many times (hundreds to thousands).
- Observe a large number of possible realization paths.
- Determine the mean of the distribution.

**Options and Derivatives Pricing Models**- Useful when complex features are involved.

**Company Valuation**- Cross-check results obtained through a Discounted Cash Flow (DCF) method.

**Risk Management**- Used for Value at Risk (VaR) estimation and volatility modeling.

- Leveraged by advancements in computing power.
- Allows analysts to gain insights into possible realizations from a given distribution function.
- Helps estimate possible prices of an asset by testing different variable values.

**Model Dependency**- Results depend on the assumed distribution model.

**Statistical Nature**- It's a statistical approach rather than an analytical one.
- Provides aggregate results, not detailed impacts of separate variables.