process is used for exploration in the continuous domain, and it is a stochastic process which has mean-reverting properties.
Alexandridis, "Weather derivatives pricing: modeling the seasonal residual variance of an Ornstein-Uhlenbeck
temperature process with neural networks," Neurocomputing, vol.
Recent experimental results have questioned the archetypical Ornstein-Uhlenbeck
model, as they have shown some departures from standard predictions of persistent random motion paradigm based on a free Brownian particle driven by a drag Stokes's force, as well as by a random force due to molecule thermal motion.
Section 3 discusses some applications to diffusion, which include Brownian motion with drift, geometric Brownian motion, and the Ornstein-Uhlenbeck
Wylomanska, "Subordinated [alpha][alpha]- stable Ornstein-Uhlenbeck
process as a tool for financial data description," Physica A: Statistical Mechanics and its Applications, vol.
References [17, 18] discussed the first three moments of homogeneous portfolios of life insurance and endowment policies by modeling the force of interest directly based on the Wiener process or the Ornstein-Uhlenbeck
process and  also generalized these results to heterogeneous portfolios.
Under the assumption that volatility of the underlying asset obeys a fast mean-reverting Ornstein-Uhlenbeck
process, we study the pricing problem of the collar option by singular perturbation analysis.
The study was conducting by starting with reviewing the literature regarding the Brownian motion, Wiener process, Ito process, Ornstein-Uhlenbeck
process and reaching the random walk theory.
Ramaswany and Nelson (1990) propose a method that approximates the discrete continuous Ornstein-Uhlenbeck
model using a recombinant binomial tree.
As we aim for a model of wind infeed over time, the results above suggest to model deseasonalized logit wind power efficiency as an Ornstein-Uhlenbeck
process, which is stationary normally distributed.
At first we simulated an Ornstein-Uhlenbeck
process solution of the stochastic differential equation
This paper contributes to the expositions on theoretical and practical aspects of parameter estimation for long-memory stochastic volatility which follows a fractional Ornstein-Uhlenbeck