Stochastic Modeling Definition
Stochastic modeling is a form of statistical modeling, primarily used in financial analysis. It forecasts the probability of various outcomes under different conditions, using random variables, based upon or accounting for certain levels of unpredictability or randomness. It is useful when it is necessary to view a variety of outcomes under multiple factors and conditions.
For example, in financial analysis, it allows an asset inventor to make investment decisions based upon statistical data. But, it can be used in many industries to predict probability y outcomes based upon random variables.
A Little More on What is Stochastic Modeling
Stochastic modeling is the opposite or deterministic modeling. Deterministic modeling produces constant results – that give you the same exact results for a particular set of inputs, no matter how many times you re-calculate the model. All variables are certain, thus any uncertain factors are left outside or external to the model. Stochastic modeling is inherently random, and the uncertain factors are built into the model. As such, the results can vary with each calculation. It requires adding variables to the calculation to determine their effects on the solution.
Stochastic modeling is used in many industries, such as insurance, stock investing, statistics, linguistics, biology, and quantum physics.
An example of a stochastic model in finance is the Monte Carlo simulation. It attempts to forecast the variations of prices, returns on assets (ROA), and asset classes (such as bonds and stocks) over time. It can simulate how a portfolio may perform based on the probability distributions of individual stock returns.