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  2. Stochastic investment model - Wikipedia

    en.wikipedia.org/wiki/Stochastic_investment_model

    A stochastic investment model tries to forecast how returns and prices on different assets or asset classes, (e. g. equities or bonds) vary over time. Stochastic models are not applied for making point estimation rather interval estimation and they use different stochastic processes .

  3. Stochastic volatility - Wikipedia

    en.wikipedia.org/wiki/Stochastic_volatility

    By assuming that the volatility of the underlying price is a stochastic process rather than a constant, it becomes possible to model derivatives more accurately. A middle ground between the bare Black-Scholes model and stochastic volatility models is covered by local volatility models. In these models the underlying volatility does not feature ...

  4. Stochastic modelling (insurance) - Wikipedia

    en.wikipedia.org/wiki/Stochastic_modelling...

    A stochastic model would be to set up a projection model which looks at a single policy, an entire portfolio or an entire company. But rather than setting investment returns according to their most likely estimate, for example, the model uses random variations to look at what investment conditions might be like.

  5. Stochastic optimization - Wikipedia

    en.wikipedia.org/wiki/Stochastic_optimization

    Stochastic optimization (SO) are optimization methods that generate and use random variables. For stochastic optimization problems, the objective functions or constraints are random. Stochastic optimization also include methods with random iterates .

  6. Stochastic simulation - Wikipedia

    en.wikipedia.org/wiki/Stochastic_simulation

    A stochastic simulation is a simulation of a system that has variables that can change stochastically (randomly) with individual probabilities. [ 1 ] Realizations of these random variables are generated and inserted into a model of the system.

  7. Stochastic approximation - Wikipedia

    en.wikipedia.org/wiki/Stochastic_approximation

    Stochastic approximation methods are a family of iterative methods typically used for root-finding problems or for optimization problems. The recursive update rules of stochastic approximation methods can be used, among other things, for solving linear systems when the collected data is corrupted by noise, or for approximating extreme values of functions which cannot be computed directly, but ...

  8. Category:Stochastic optimization - Wikipedia

    en.wikipedia.org/wiki/Category:Stochastic...

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  9. Stochastic portfolio theory - Wikipedia

    en.wikipedia.org/wiki/Stochastic_portfolio_theory

    Stochastic portfolio theory (SPT) is a mathematical theory for analyzing stock market structure and portfolio behavior introduced by E. Robert Fernholz in 2002.It is descriptive as opposed to normative, and is consistent with the observed behavior of actual markets.