Stochastic models

Stochastic models

Liability-matching models that assume that the liability payments and the asset cash flows are uncertain. Related: Deterministic models.

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.
References in periodicals archive ?
1-4, the stochastic models do capture the nonlinear character of the photopolymerization process in every case.
The researchers create stochastic models to analyze viral dynamics and to understand how protective or preventative drug treatment prior to or immediately following exposure can act to reduce risk of infection under various scenarios.
On the probabilistic side we discuss applications of Fourier analysis of Boolean functions to the study of threshold behaviour of random graphs and other stochastic models, and propose ten directions for this emerging theory.
Dentcheva is broadening the scope of the journal to include more work on stochastic optimization and control, as well as research that incorporates probability and stochastic models.
s model (1995) along with a budgetary constraint where the demand follows several stochastic models.
of North Carolina at Chapel Hill) presents a text designed for use in a two-course sequence in stochastic models, with an emphasis on modeling and analysis.
Watch For: Insurers to use stochastic models that can generate the interconnections among identified risks and their impact.
4 adds significant usability features to the statistical enhancements included in previous releases, thereby making the most sophisticated stochastic models even more accessible.
Modeling of such phenomena can be achieved by stochastic models.
In our stochastic models certain features like the position, the size or the orientation of the particles are supposed independent of the shape and need therefore not to be taken into consideration.
Factor-based models take a quantitative, formulaic approach to determining capital adequacy, while stochastic models are more probabilistic.
For example, the quest for more reserve information through the application of stochastic models, such as bootstrapping, can help the corporate decision maker frame and address important risk management questions that are influenced by loss distribution information: What are the benefits and costs of strengthening reserves?

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