Monte Carlo Simulation utilized in three cases;
Objectives;
Explain the main concepts of Monte Carlo simulation; Use historical observations to estimate the probability distributions of data; Simulate many possible outcomes for independent variables using Python; Summarize the distribution of scenarios using confidence intervals; Interpret the output of Monte Carlo simulation results and use it to guide business decisions
Outcomes;
Calculating daily returns from our observed data, we need to generate plausible scenarios to watch the price evolve over a 250 day period; Since simple returns are not additive, we use log returns which later we reverse into simple returns after plotting graphs.
Conclusion;
Observations: 20 years of historical data; Distributions: Daily Returns Probability Distribution (Mean, std dev, variance, drift); Simulations: Result of each simulation: 250 day stock price movement; Quantifications: Best, worst, and average stock price scenarios