Stochastic Modelling is a financial model that helps make financial decisions.

- It incorporates random variables to produce many different outcomes under diverse conditions to predict the probability of results.
- Unpredictability –
Stochastic Modelling accounts for certain levels of unpredictability or randomness.

- Getting various projections of assets allocation outcomes based on various conditions.
- Management of Liabilities. Insurance companies heavily rely on Stochastic Modeling to forecast and hence to calculate premium. Here is more detailed example of one of such model called Cramer -Lungberg Insurance Model.
- Insurance companies also have to build reserve for their future payments which is usually done by deterministic methods; so there are many models which are Semi-Stochastic. You can checkout here.

- Produces Constant Results
- Example – f(x) = x +1
- Example – Moving Average [Or, Any indicator in Stock Market that is calculated based on past data]

- Produces Variable Results
- Inherently random
- Example – Monte Carlo Simulation, Markov Models [Any models or simulations in Stock Market that does probabilistic forecasting.]