Generalized Wiener Process

Short description: In statistics, a continuous time random walk with drift and random jumps at every point in time

In statistics, a generalized Wiener process^[1] (named after Norbert Wiener) is a continuous time random walk with drift and random jumps at every point in time. Formally:

a (x, t) d t + b (x, t) η \sqrt{d t}

where a and b are deterministic functions, t is a continuous index for time, x is a set of exogenous variables that may change with time, dt is a differential in time, and η is a random draw from a standard normal distribution at each instant.

References

↑ "Stochastic Processes and Monte Carlo Method | Introduction To Options on QuantConnect" (in en). https://www.quantconnect.com/learning/articles/introduction-to-options/stochastic-processes-and-monte-carlo-method.

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[1] "Stochastic Processes and Monte Carlo Method | Introduction To Options on QuantConnect" (in en). https://www.quantconnect.com/learning/articles/introduction-to-options/stochastic-processes-and-monte-carlo-method.

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Generalized Wiener Process

See also

References