From 58ae1086e9ed7537a28d3c2a11b1092bf6edd8bd Mon Sep 17 00:00:00 2001
From: Daniel Boros <dancixx@gmail.com>
Date: Thu, 12 Sep 2024 22:14:26 +0200
Subject: [PATCH] feat: add rbergomi model

---
 stochastic-rs-core/src/volatility.rs          | 33 +--------
 stochastic-rs-core/src/volatility/rbergomi.rs | 71 +++++++++++++++++++
 2 files changed, 73 insertions(+), 31 deletions(-)
 create mode 100644 stochastic-rs-core/src/volatility/rbergomi.rs

diff --git a/stochastic-rs-core/src/volatility.rs b/stochastic-rs-core/src/volatility.rs
index 55131f3..09dfe61 100644
--- a/stochastic-rs-core/src/volatility.rs
+++ b/stochastic-rs-core/src/volatility.rs
@@ -1,33 +1,4 @@
-//! This module contains the implementations of various diffusion processes.
-//!
-//! The following diffusion processes are implemented:
-//!
-//! - Duffie-Kan Model
-//!   - The Duffie-Kan model is a multifactor interest rate model incorporating correlated Brownian motions.
-//!   - SDE: `dr(t) = (a1 * r(t) + b1 * x(t) + c1) * dt + sigma1 * (alpha * r(t) + beta * x(t) + gamma) * dW_r(t)`
-//!   - SDE: `dx(t) = (a2 * r(t) + b2 * x(t) + c2) * dt + sigma2 * (alpha * r(t) + beta * x(t) + gamma) * dW_x(t)`
-//!   - where Corr(W_r(t), W_x(t)) = rho
-//!
-//! - **Heston Model**
-//!   - A stochastic volatility model used to describe the evolution of the volatility of an underlying asset.
-//!   - SDE: `dS(t) = mu * S(t) * dt + S(t) * sqrt(V(t)) * dW_1(t)`
-//!   - SDE: `dV(t) = kappa * (theta - V(t)) * dt + eta * sqrt(V(t)) * dW_2(t)`
-//!
-//! - SABR (Stochastic Alpha, Beta, Rho) Model
-//! - Widely used in financial mathematics for modeling stochastic volatility.
-//!   - SDE: `dF(t) = V(t) * F(t)^beta * dW_F(t)`
-//!   - SDE: `dV(t) = alpha * V(t) * dW_V(t)`
-//!   - where Corr(W_F(t), W_V(t)) = rho
-//!
-//! - **Vasicek Model**
-//!   - An Ornstein-Uhlenbeck process used to model interest rates.
-//!   - SDE: `dX(t) = theta * (mu - X(t)) * dt + sigma * dW(t)`
-//!
-//! - **Fractional Vasicek (fVasicek) Model**
-//!   - Incorporates fractional Brownian motion into the Vasicek model.
-//!   - SDE: `dX(t) = theta * (mu - X(t)) * dt + sigma * dW^H(t)`
-//!
-//! Each process has its own module and functions to generate sample paths.
-
+pub mod fheston;
 pub mod heston;
+pub mod rbergomi;
 pub mod sabr;
diff --git a/stochastic-rs-core/src/volatility/rbergomi.rs b/stochastic-rs-core/src/volatility/rbergomi.rs
new file mode 100644
index 0000000..330533f
--- /dev/null
+++ b/stochastic-rs-core/src/volatility/rbergomi.rs
@@ -0,0 +1,71 @@
+use ndarray::{s, Array1};
+
+use crate::{noise::cgns::Cgns, Sampling2D};
+
+#[derive(Default)]
+pub struct RoughBergomi {
+  pub hurst: f64,
+  pub nu: f64,
+  pub sigma_0: f64,
+  pub r: f64,
+  pub rho: f64,
+  pub n: usize,
+  pub t: Option<f64>,
+  pub m: Option<usize>,
+  pub cgns: Cgns,
+}
+
+impl RoughBergomi {
+  #[must_use]
+  pub fn new(params: &Self) -> Self {
+    let cgns = Cgns::new(&Cgns {
+      rho: params.rho,
+      n: params.n,
+      t: params.t,
+      m: params.m,
+    });
+
+    Self {
+      hurst: params.hurst,
+      nu: params.nu,
+      sigma_0: params.sigma_0,
+      r: params.r,
+      rho: params.rho,
+      n: params.n,
+      t: params.t,
+      m: params.m,
+      cgns,
+    }
+  }
+}
+
+impl Sampling2D<f64> for RoughBergomi {
+  fn sample(&self) -> [Array1<f64>; 2] {
+    let dt = self.t.unwrap_or(1.0) / self.n as f64;
+    let [cgn1, z] = self.cgns.sample();
+
+    let mut s = Array1::<f64>::zeros(self.n + 1);
+    let mut sigma2 = Array1::<f64>::zeros(self.n + 1);
+
+    for i in 1..(self.n + 1) {
+      s[i] = s[i - 1] + self.r * s[i - 1] + sigma2[i - 1] * cgn1[i - 1];
+
+      let sum_z = z.slice(s![..i]).sum();
+      let t = i as f64 * dt;
+      sigma2[i] = self.sigma_0.powi(2)
+        * (self.nu * (2.0 * self.hurst).sqrt() * t.powf(self.hurst - 0.5) * sum_z
+          - 0.5 * self.nu.powi(2) * t.powf(2.0 * self.hurst))
+        .exp();
+    }
+
+    [s, sigma2]
+  }
+
+  fn n(&self) -> usize {
+    self.n
+  }
+
+  fn m(&self) -> Option<usize> {
+    self.m
+  }
+}