What is Stochastic Modeling
Stochastic modeling is a form of financial modeling that includes one or more random variables. The purpose of such modeling is to estimate how probable outcomes are within a forecast to predict conditions for different situations. The Monte Carlo simulation is one example of a stochastic model; when used for portfolio evaluation, various simulations of how a portfolio may perform are developed based on probability distributions of individual stock returns.
BREAKING DOWN Stochastic Modeling
Stochastic modeling presents data, or predicts outcomes, all of which account for certain degrees of unpredictability or randomness. Stochastic modeling is used in a variety of industries around the world, many of which are dependent on such models for improving business practices or increasing profitability. For example, the insurance industry relies heavily on stochastic modeling to predict the future of company balance sheets. Other industries and fields of study that depend on stochastic modeling include stock investing, statistics, linguistics, biology, and even quantum physics.
Understanding the Concept of Stochastic Modeling
To understand the sometimes confusing concept of stochastic modeling, it is helpful to compare it to deterministic modeling. While the former produces a variety of answers, estimations or outcomes, deterministic modeling is the opposite. Under deterministic modeling, there is typically only one solution, or answer, to a problem in most elementary mathematics. Deterministic modeling also typically dictates there is only one set of specific values. Alternatively, stochastic modeling can be likened to adding variations to a complex math problem to see its effect on the solution. This process is then repeated in a number of different ways to produce a number of solutions.
Stochastic Modeling in the Investment World
Stochastic investment models attempt to forecast the variations of prices and returns on assets and asset classes, such as bonds and stocks, over time. In the investment world, stochastic models can be classified differently, having different models for single assets and multiple assets. Such modeling is, much of the time, used for financial planning and actuarial work that allows investors and traders to optimize asset allocation as well as asset-liability management.
The significance of stochastic modeling is extensive and far-reaching. The importance of being able to view a variety of outcomes and factor in a variety of variables is unparalleled, and in some industries, it may mean the success or bankruptcy of a company. Because new variables may come into play at any time, and because the number of variables that may have an effect could be high, stochastic models are sometimes run hundreds or even thousands of times, offering potential outcomes for nearly every situation a business, industry, portfolio or agency may face.