Stochastic modeling

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Stochastic Modeling

Any of several methods for measuring the probability of distribution of a random variable. That is, a stochastic model measures the likelihood that a variable will equal any of a universe of amounts. It is used in technical analysis to predict market movements. Insurance companies also use stochastic modeling to estimate their assets and liabilities because, due to the nature of the insurance business, these are not known quantities.
Farlex Financial Dictionary. © 2012 Farlex, Inc. All Rights Reserved

Stochastic modeling.

Stochastic modeling is a statistical process that uses probability and random variables to predict a range of probable investment performances.

The mathematical principles behind stochastic modeling are complex, so it's not something you can do on your own.

But based on information you provide about your age, investments, and risk tolerance, financial analysts may use stochastic modeling to help you evaluate the probability that your current investment portfolio will allow you to meet your financial goals.

Appropriately enough, the term stochastic comes from the Greek word meaning "skillful in aiming."

Dictionary of Financial Terms. Copyright © 2008 Lightbulb Press, Inc. All Rights Reserved.
References in periodicals archive ?
Given that the climate, topography and initial soil characteristics were the same for each plot, the PDF of the relative soil water content simulated by the stochastic model for each plot was only affected by vegetation type.
Laurenson, "Novel 3D geometry-based stochastic models for non-isotropic MIMO vehicle-to-vehicle channels," IEEE Transactions on Wireless Communications, vol.
Using the extended model, we will formulate the basic reproductive number [R.sub.0] and use it to compare the disease dynamics of the deterministic and stochastic models in order to determine the effect of randomness in malaria transmission dynamics.
When passenger walking speed v is a distributed variable, the stochastic model is integrated with respect to walking speed as well.
Caption: Figure 2: The solution (S(t), [I.sub.1](t), [I.sub.2](t)) = (0.3,0.25,0.25) to the nonautonomous stochastic model (3).
Caption: FIGURE 1: The path of lit) for the stochastic model (6) with parameters in Example 1, compared to the corresponding deterministic model.
In section 2 we first improve the flocking estimates for general symmetric communication rates in [11] and then introduce an asymmetric stochastic model. For the asymmetric communication rate between agents, we explicitly obtain all statistical quantities about the random dynamical system which leads to time-asymptotic strong flocking.
The number of seeds remaining in the sample at any time t can be measured using the following stochastic model for a two-state germination process: E[M1 (t )] M 0 exp (1t ) (Tseng and Hsu, 1989).
1-4, for all resins investigated in this study as the photoinitiator loading concentration increases, the stochastic model's predictions agree with the trend of the experimental measurements.
To verify the validity of the stochastic model, we have applied PRISM to implement the experiment and present the result.

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