Usage of Monte Carlo random process to simulate unpredictability of stock trends
Basic MCS applied to a stock price. We need a model to specify the behavior of the stock price, and we'll use one of the most common models in finance: geometric Brownian motion (GBM). GBM assumes that a constant drift is accompanied by random shocks. Therefore, while Monte Carlo simulation can refer to a universe of different approaches to simulation, we will start here with the most basic.
One of the most common ways to estimate risk is the use of a Monte Carlo simulation (MCS). For example, to calculate the value at risk (VaR) of a portfolio, we can run a Monte Carlo simulation that attempts to predict the worst likely loss for a portfolio given a confidence interval over a specified time horizon.
Where to Start A Monte Carlo simulation is an attempt to predict the future many times over. At the end of the simulation, thousands or millions of "random trials" produce a distribution of outcomes that can be analyzed. The key steps are:
- Specify a model (e.g. geometric Brownian motion)
- Generate random trials
- Process the output