working cva computation using quantlib

python/cvatesting/hullwhite.py

@@ -0,0 +1,66 @@
+import QuantLib as ql
+
+
+def build_calibrated_hw(
+    curve_handle,
+    swaption_helpers,
+    a_init=0.05,
+    sigma_init=0.01
+):
+    hw = ql.HullWhite(curve_handle, a_init, sigma_init)
+
+    optimizer = ql.LevenbergMarquardt()
+    end_criteria = ql.EndCriteria(1000, 500, 1e-8, 1e-8, 1e-8)
+
+    hw.calibrate(swaption_helpers, optimizer, end_criteria)
+
+    return hw
+
+import numpy as np
+
+
+def simulate_hw_state(
+    hw: ql.HullWhite,
+    curve_handle,
+    time_grid,
+    n_paths,
+    seed=42
+):
+    a, sigma = hw.params()
+
+    dt = np.diff(time_grid)
+    n_steps = len(time_grid)
+
+    np.random.seed(seed)
+    x = np.zeros((n_paths, n_steps))
+
+    for i in range(1, n_steps):
+        decay = np.exp(-a * dt[i-1])
+        vol = sigma * np.sqrt((1 - np.exp(-2 * a * dt[i-1])) / (2 * a))
+        z = np.random.normal(size=n_paths)
+
+        x[:, i] = x[:, i-1] * decay + vol * z
+
+    return x
+
+
+def swap_npv_from_state(
+    swap,
+    hw,
+    curve_handle,
+    t,
+    x_t,
+    today
+):
+    ql.Settings.instance().evaluationDate = today + int(t * 365)
+
+    hw.setState(x_t)
+
+    engine = ql.DiscountingSwapEngine(
+        curve_handle,
+        False,
+        hw
+    )
+
+    swap.setPricingEngine(engine)
+    return swap.NPV()