Implementation:Avhz RustQuant CIR Process
| Knowledge Sources | |
|---|---|
| Domains | Stochastic_Processes, Quantitative_Finance |
| Last Updated | 2026-02-07 19:00 GMT |
Overview
Concrete implementation of the Cox-Ingersoll-Ross (CIR) stochastic process provided by the RustQuant library.
Description
The Cox-Ingersoll-Ross model is a mean-reverting, square-root diffusion process defined by the SDE:
dX(t) = theta * (mu - X(t)) dt + sigma * sqrt(X(t)) dW(t)
where theta is the speed of mean reversion, mu is the long-run mean level, and sigma is the volatility. The square-root diffusion term ensures that the process remains non-negative (under the Feller condition 2*theta*mu >= sigma^2).
Key parameters:
- mu (ModelParameter) -- The long-run mean level
- sigma (ModelParameter) -- The volatility (must be non-negative)
- theta (ModelParameter) -- Mean reversion speed
Usage
Use this process for modeling interest rates, default intensities, or any non-negative mean-reverting quantity. The CIR model is widely used in fixed income for short-rate modeling (e.g., bond pricing) and in credit risk for intensity-based default models.
Code Reference
Source Location
- Repository: RustQuant
- File: crates/RustQuant_stochastics/src/cox_ingersoll_ross.rs
- Lines: 1-110
Signature
#[derive(Debug)]
pub struct CoxIngersollRoss {
pub mu: ModelParameter,
pub sigma: ModelParameter,
pub theta: ModelParameter,
}
impl CoxIngersollRoss {
pub fn new(
mu: impl Into<ModelParameter>,
sigma: impl Into<ModelParameter>,
theta: impl Into<ModelParameter>,
) -> Self
}
impl StochasticProcess for CoxIngersollRoss {
fn drift(&self, x: f64, t: f64) -> f64
fn diffusion(&self, x: f64, t: f64) -> f64
fn jump(&self, _x: f64, _t: f64) -> Option<f64>
fn parameters(&self) -> Vec<f64>
}
Import
use RustQuant::stochastics::CoxIngersollRoss;
I/O Contract
Inputs
| Name | Type | Required | Description |
|---|---|---|---|
| mu | impl Into<ModelParameter> | Yes | The long-run mean level |
| sigma | impl Into<ModelParameter> | Yes | The volatility (must be non-negative) |
| theta | impl Into<ModelParameter> | Yes | Mean reversion speed |
Outputs
| Name | Type | Description |
|---|---|---|
| drift() | f64 | Returns theta(t) * (mu(t) - x) -- mean-reverting drift |
| diffusion() | f64 | Returns sigma(t) * sqrt(x) -- square-root diffusion |
| jump() | Option<f64> | Always returns None (no jump component) |
| parameters() | Vec<f64> | Returns [mu(0), sigma(0), theta(0)] |
Usage Examples
use RustQuant::stochastics::CoxIngersollRoss;
use RustQuant::stochastics::{StochasticProcessConfig, StochasticScheme};
// Create a CIR process with mu=0.15, sigma=0.45, theta=0.01
let cir = CoxIngersollRoss::new(0.15, 0.45, 0.01);
// Configure simulation: x0=10.0, t_start=0.0, t_end=0.5, n_steps=100, 100 paths
let config = StochasticProcessConfig::new(
10.0, 0.0, 0.5, 100, StochasticScheme::EulerMaruyama, 100, false, None
);
let output = cir.generate(&config);
// Access simulated paths
let paths = &output.paths;