- The main goal of Monte Carlo option pricing techniques is to generate thousands of random price paths for the underlying asset, i.e. stock. This is done through a simulation which then generates an average value based on the random price paths for the value of the options. Each path is averaged and discounted to today. The value in this approach is in its ability to model complexity which is often a key variable in demand and supply markets.
- Monte Carlo option models can be modeled with one source of uncertainty, e.g. the price of the underlying stock in question. They can also be modeled by understanding the primary driver of uncertainty. In the case of many valuations this number is the rate of interest as it is often determined by external factors; it is a dynamic variable. Monte Carlo option pricing methodology provides a solution set for ubiquitous variables. This allows investors to better model real-life situations.
- Monte Carlo methods also allow investors to model out compounding factors of uncertainty like interest rates and the underlying price of a stock. Both of these scenarios can be modeled out and averaged or added together. Other real-life factors which are hard to model due to their complexity are inflation, volatility, commodity prices, and even correlation which is used as a measure of risk.
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