feat: hjm model

Author: dancixx

src/stochastic/interest.rs

@@ -1,12 +1,13 @@
 pub mod adg;
-pub mod bgm;
+// pub mod bgm;
 pub mod cir;
 pub mod cir_2f;
 pub mod duffie_kan;
 pub mod fvasicek;
+pub mod hjm;
 pub mod ho_lee;
 pub mod hull_white;
 pub mod hull_white_2f;
-pub mod mod_duffie_kan;
+// pub mod mod_duffie_kan;
 pub mod vasicek;
-pub mod wu_zhang;
+// pub mod wu_zhang;

src/stochastic/interest/hjm.rs

@@ -0,0 +1,56 @@
+use impl_new_derive::ImplNew;
+use ndarray::Array1;
+use ndarray_rand::RandomExt;
+use rand_distr::Normal;
+
+use crate::stochastic::Sampling3D;
+
+#[derive(ImplNew)]
+pub struct HJM {
+  pub a: fn(f64) -> f64,
+  pub b: fn(f64) -> f64,
+  pub p: fn(f64, f64) -> f64,
+  pub q: fn(f64, f64) -> f64,
+  pub v: fn(f64, f64) -> f64,
+  pub alpha: fn(f64, f64) -> f64,
+  pub sigma: fn(f64, f64) -> f64,
+  pub n: usize,
+  pub r0: Option<f64>,
+  pub p0: Option<f64>,
+  pub f0: Option<f64>,
+  pub t: Option<f64>,
+  pub m: Option<usize>,
+}
+
+impl Sampling3D<f64> for HJM {
+  fn sample(&self) -> [Array1<f64>; 3] {
+    let t_max = self.t.unwrap_or(1.0);
+    let dt = self.t.unwrap_or(1.0) / (self.n - 1) as f64;
+    let mut r = Array1::<f64>::zeros(self.n);
+    let mut p = Array1::<f64>::zeros(self.n);
+    let mut f = Array1::<f64>::zeros(self.n);
+
+    let gn1 = Array1::random(self.n, Normal::new(0.0, dt.sqrt()).unwrap());
+    let gn2 = Array1::random(self.n, Normal::new(0.0, dt.sqrt()).unwrap());
+    let gn3 = Array1::random(self.n, Normal::new(0.0, dt.sqrt()).unwrap());
+
+    for i in 1..self.n {
+      let t = i as f64 * dt;
+
+      r[i] = r[i - 1] + (self.a)(t) * dt + (self.b)(t) * gn1[i - 1];
+      p[i] =
+        p[i - 1] + (self.p)(t, t_max) * ((self.q)(t, t_max) * dt + (self.v)(t, t_max) * gn2[i - 1]);
+      f[i] = f[i - 1] + (self.alpha)(t, t_max) * dt + (self.sigma)(t, t_max) * gn3[i - 1];
+    }
+
+    [r, p, f]
+  }
+
+  fn n(&self) -> usize {
+    self.n
+  }
+
+  fn m(&self) -> Option<usize> {
+    self.m
+  }
+}