working cva computation using quantlib

2026-02-08 13:40:35 +00:00
parent d484f9c236
commit 8b5eb8797f
6 changed files with 1337 additions and 0 deletions
--- a/python/cvatesting/main.py
+++ b/python/cvatesting/main.py
@@ -0,0 +1,109 @@
+import QuantLib as ql
+import numpy as np
+import matplotlib.pyplot as plt
+
+# --- UNIT 1: Instrument Setup ---
+def create_30y_swap(today, rate_quote):
+    ql.Settings.instance().evaluationDate = today
+    
+    # We use a Relinkable handle to swap curves per path/time-step
+    yield_handle = ql.RelinkableYieldTermStructureHandle()
+    yield_handle.linkTo(ql.FlatForward(today, rate_quote, ql.Actual365Fixed()))
+    
+    calendar = ql.TARGET()
+    settle_date = calendar.advance(today, 2, ql.Days)
+    maturity_date = calendar.advance(settle_date, 30, ql.Years)
+    
+    index = ql.Euribor6M(yield_handle)
+    
+    fixed_schedule = ql.Schedule(settle_date, maturity_date, ql.Period(ql.Annual), 
+                                 calendar, ql.ModifiedFollowing, ql.ModifiedFollowing, 
+                                 ql.DateGeneration.Forward, False)
+    float_schedule = ql.Schedule(settle_date, maturity_date, ql.Period(ql.Semiannual), 
+                                 calendar, ql.ModifiedFollowing, ql.ModifiedFollowing, 
+                                 ql.DateGeneration.Forward, False)
+    
+    swap = ql.VanillaSwap(ql.VanillaSwap.Payer, 1e6, fixed_schedule, rate_quote, 
+                          ql.Thirty360(ql.Thirty360.BondBasis), float_schedule, 
+                          index, 0.0, ql.Actual360())
+    
+    # Pre-calculate all fixing dates needed for the life of the swap
+    fixing_dates = [index.fixingDate(d) for d in float_schedule]
+    
+    return swap, yield_handle, index, fixing_dates
+
+# --- UNIT 2: The Simulation Loop ---
+def run_simulation(n_paths=50):
+    today = ql.Date(27, 1, 2026)
+    swap, yield_handle, index, fixing_dates = create_30y_swap(today, 0.03)
+    
+    # HW Parameters: a=mean reversion, sigma=vol
+    model = ql.HullWhite(yield_handle, 0.01, 0.01)
+    process = ql.HullWhiteProcess(yield_handle, 0.01, 0.01)
+    
+    times = np.arange(0, 31, 1.0) # Annual buckets
+    grid = ql.TimeGrid(times)
+    
+    rng = ql.GaussianRandomSequenceGenerator(ql.UniformRandomSequenceGenerator(
+        len(grid)-1, ql.UniformRandomGenerator()))
+    seq = ql.GaussianMultiPathGenerator(process, grid, rng, False)
+    
+    npv_matrix = np.zeros((n_paths, len(times)))
+
+    for i in range(n_paths):
+        path = seq.next().value()[0]
+        # 1. Clear previous path's fixings to avoid data pollution
+        ql.IndexManager.instance().clearHistories()
+        
+        for j, t in enumerate(times):
+            if t >= 30: continue
+            
+            eval_date = ql.TARGET().advance(today, int(t), ql.Years)
+            ql.Settings.instance().evaluationDate = eval_date
+            
+            # 2. Update the curve with simulated short rate rt
+            rt = path[j]
+            # Use pillars up to 35Y to avoid extrapolation crashes
+            tenors = [0, 1, 2, 5, 10, 20, 30, 35]
+            dates = [ql.TARGET().advance(eval_date, y, ql.Years) for y in tenors]
+            discounts = [model.discountBond(t, t + y, rt) for y in tenors]
+            
+            sim_curve = ql.DiscountCurve(dates, discounts, ql.Actual365Fixed())
+            sim_curve.enableExtrapolation()
+            yield_handle.linkTo(sim_curve)
+            
+            # 3. MANUAL FIXING INJECTION
+            # For every fixing date that has passed or is 'today'
+            for fd in fixing_dates:
+                if fd <= eval_date:
+                    # Direct manual calculation to avoid index.fixing() error
+                    # We calculate the forward rate for the 6M period starting at fd
+                    start_date = fd
+                    end_date = ql.TARGET().advance(start_date, 6, ql.Months)
+                    
+                    # Manual Euribor rate formula: (P1/P2 - 1) / dt
+                    p1 = sim_curve.discount(start_date)
+                    p2 = sim_curve.discount(end_date)
+                    dt = ql.Actual360().yearFraction(start_date, end_date)
+                    fwd_rate = (p1 / p2 - 1.0) / dt
+                    
+                    index.addFixing(fd, fwd_rate, True) # Force overwrite
+            
+            # 4. Valuation
+            swap.setPricingEngine(ql.DiscountingSwapEngine(yield_handle))
+            npv_matrix[i, j] = max(swap.NPV(), 0)
+            
+        ql.Settings.instance().evaluationDate = today
+
+    return times, npv_matrix
+
+# --- UNIT 3: Visualization ---
+times, npv_matrix = run_simulation(n_paths=100)
+ee = np.mean(npv_matrix, axis=0)
+
+plt.figure(figsize=(10, 5))
+plt.plot(times, ee, lw=2, label="Expected Exposure (EE)")
+plt.fill_between(times, ee, alpha=0.3)
+plt.title("30Y Swap EE Profile - Hull White")
+plt.xlabel("Years"); plt.ylabel("Exposure"); plt.grid(True); plt.legend()
+plt.show()