Struct FractionalOrnsteinUhlenbeck 

pub struct FractionalOrnsteinUhlenbeck {
    pub mu: ModelParameter,
    pub sigma: ModelParameter,
    pub theta: ModelParameter,
    pub hurst: f64,
    pub method: FractionalProcessGeneratorMethod,
}

Struct containing the Ornstein-Uhlenbeck process parameters.

Fields

mu: ModelParameter

The long-run mean ($\mu$).

sigma: ModelParameter

The diffusion, or instantaneous volatility ($\sigma$).

theta: ModelParameter

Mean reversion parameter ($\theta$). Defines the speed at which the process reverts to the long-run mean.

hurst: f64

Hurst parameter of the process. The Hurst parameter is a measure of the long-term memory of the process.

method: FractionalProcessGeneratorMethod

Method to generate Fractional Gaussian Noise.

Implementations

impl FractionalOrnsteinUhlenbeck

pub fn new( mu: impl Into<ModelParameter>, sigma: impl Into<ModelParameter>, theta: impl Into<ModelParameter>, hurst: f64, method: FractionalProcessGeneratorMethod, ) -> FractionalOrnsteinUhlenbeck

Create a new Ornstein-Uhlenbeck process.

Trait Implementations

impl StochasticProcess for FractionalOrnsteinUhlenbeck

fn drift(&self, x: f64, t: f64) -> f64

Base method for the process’ drift.

fn diffusion(&self, _x: f64, t: f64) -> f64

Base method for the process’ diffusion.

fn jump(&self, _x: f64, _t: f64) -> Option<f64>

Base method for the process’ jump term (if applicable).

fn parameters(&self) -> Vec<f64>

Return the model’s parameters as a Vec<f64>.

fn euler_maruyama(&self, config: &StochasticProcessConfig) -> Trajectories

Euler-Maruyama discretisation scheme.

fn seedable_euler_maruyama( &self, x_0: f64, t_0: f64, t_n: f64, n_steps: usize, m_paths: usize, parallel: bool, seed: u64, ) -> Trajectories

Euler-Maruyama discretisation scheme with a choice of random seed.