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  2. Local volatility - Wikipedia

    en.wikipedia.org/wiki/Local_volatility

    A local volatility model, in mathematical finance and financial engineering, is an option pricing model that treats volatility as a function of both the current asset level and of time . As such, it is a generalisation of the Black–Scholes model , where the volatility is a constant (i.e. a trivial function of S t {\displaystyle S_{t}} and t ...

  3. Vasicek model - Wikipedia

    en.wikipedia.org/wiki/Vasicek_model

    The model specifies that the instantaneous interest rate follows the stochastic differential equation: d r t = a ( b − r t ) d t + σ d W t {\displaystyle dr_{t}=a(b-r_{t})\,dt+\sigma \,dW_{t}} where W t is a Wiener process under the risk neutral framework modelling the random market risk factor, in that it models the continuous inflow of ...

  4. Predictor–corrector method - Wikipedia

    en.wikipedia.org/wiki/Predictor–corrector_method

    The initial, "prediction" step, starts from a function fitted to the function-values and derivative-values at a preceding set of points to extrapolate ("anticipate") this function's value at a subsequent, new point.

  5. Prognostic equation - Wikipedia

    en.wikipedia.org/wiki/Prognostic_equation

    Prognostic equation - in the context of physical (and especially geophysical) simulation, a prognostic equation predicts the value of variables for some time in the future on the basis of the values at the current or previous times.

  6. Plug flow reactor model - Wikipedia

    en.wikipedia.org/wiki/Plug_flow_reactor_model

    The residence time distribution function is therefore a Dirac delta function at . A real plug flow reactor has a residence time distribution that is a narrow pulse around the mean residence time distribution. A typical plug flow reactor could be a tube packed with some solid material (frequently a catalyst). Typically these types of reactors ...

  7. Linear predictor function - Wikipedia

    en.wikipedia.org/wiki/Linear_predictor_function

    The basic form of a linear predictor function () for data point i (consisting of p explanatory variables), for i = 1, ..., n, is = + + +,where , for k = 1, ..., p, is the value of the k-th explanatory variable for data point i, and , …, are the coefficients (regression coefficients, weights, etc.) indicating the relative effect of a particular explanatory variable on the outcome.

  8. Dynkin's formula - Wikipedia

    en.wikipedia.org/wiki/Dynkin's_formula

    In mathematics — specifically, in stochastic analysis — Dynkin's formula is a theorem giving the expected value of any suitably smooth function applied to a Feller process at a stopping time. It may be seen as a stochastic generalization of the (second) fundamental theorem of calculus. It is named after the Russian mathematician Eugene Dynkin.

  9. Chapman–Enskog theory - Wikipedia

    en.wikipedia.org/wiki/Chapman–Enskog_theory

    An important prediction of Chapman–Enskog theory is that viscosity, , is independent of density (this can be seen for each molecular model in table 1, but is actually model-independent). This counterintuitive result traces back to James Clerk Maxwell , who inferred it in 1860 on the basis of more elementary kinetic arguments. [ 11 ]