Stochastic Modeling in Stock Market

Stochastic Modeling

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.

Deterministic modeling gives you the same exact results for a particular set of inputs, no matter how many times you re-calculate the model.

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

Stochastic modeling, on the other hand, is inherently random, and the uncertain factors are built into the model.

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