Python for Finance Cookbook

How it works...

In Steps 2 to 4, we downloaded Microsoft's stock prices and calculated simple returns. In the next step, we divided the data into the training and test sets. We calculated the average and standard deviation of the returns from the training set to obtain the drift (mu) and diffusion (sigma) coefficients, which we used later for simulations. Additionally, in Step 6, we defined the following parameters:

• T: Forecasting horizon; in this case, the number of days in the test set.
• N: Number of time increments in the forecasting horizon.
• S_0: Initial price. For this simulation, we take the last observation from the training set.
• N_SIM: Number of simulated paths.

Monte Carlo simulations use a process called discretization. The idea ...