Add property tests for VaR/ES loss conventions and monotonicity

Author: sensor-aae

tests/test_market_risk_properties.py

@@ -0,0 +1,28 @@
+import numpy as np
+import pandas as pd
+from risklib.market.market_risk_model import MarketRiskModel, MarketRiskConfig
+
+
+def test_es_ge_var():
+    rng = np.random.default_rng(0)
+    returns = pd.DataFrame(rng.normal(0, 0.01, size=(1000, 3)), columns=["A", "B", "C"])
+    w = np.array([0.4, 0.3, 0.3])
+
+    m = MarketRiskModel(returns, w, MarketRiskConfig(alpha=0.99, method="historical"))
+    m.fit()
+
+    assert m.compute_var() >= 0
+    assert m.compute_es() >= m.compute_var()
+
+
+def test_var_monotonicity():
+    rng = np.random.default_rng(1)
+    returns = pd.DataFrame(rng.normal(0, 0.01, size=(1000, 3)), columns=["A", "B", "C"])
+    w = np.array([0.5, 0.2, 0.3])
+
+    m95 = MarketRiskModel(returns, w, MarketRiskConfig(alpha=0.95, method="historical"))
+    m99 = MarketRiskModel(returns, w, MarketRiskConfig(alpha=0.99, method="historical"))
+    m95.fit()
+    m99.fit()
+
+    assert m99.compute_var() >= m95.compute_var()