A stochastic process is a random process that changes over time. In probability they are represented by a random variable. They are widely used as mathematical models to study and understand random processes. Some examples Stochastic processes have been used to explain random fluctuations in financial markets. Stochastic process can be classified as either continuous-time or discrete-time based on the state space within which the process is defined in.
Stochastic modelling is used to predict outcomes that account for certain levels of randomness. The models attempt to forecast variations in prices, returns etc. over time. Based on this, they are used to help make investment decisions. Monte Carlo Simulations are an example of a stochastic model.
Markov process is a random process in which the future in independent of the past given the present. This is another example of a stochastic process. And a chain of such events for a Markov Chain. Any game that is entirely determined by a dice is an example of a Markov Chain.
