Exercise

# Monte Carlo simulations

**Monte-Carlo simulations** are used to model a wide range of possibilities.

Monte-Carlos can be constructed in many different ways, but all of them involve generating a large number of random variants of a given model, allowing a wide distribution of possible paths to be analyzed. This can allow you to build a comprehensive forecast of possibilities to sample from without a large amount of historical data.

Generate 100 Monte-Carlo simulations for the USO oil ETF.

The parameters `mu`

, `vol`

, `T`

, and `S0`

are available from the previous exercise.

Instructions

**100 XP**

- Loop from 0 to 100 (not including 100) using the
`range()`

function. - Call the plotting function for each iteration using the
`plt.plot()`

function, passing the range of values T (`range(T)`

) as the first argument and the`forecasted_values`

as the second argument.