1 files changed, 45 insertions, 0 deletions
diff --git a/python/exploration/dispersion.py b/python/exploration/dispersion.py
index fac383c5..b33f2796 100644
--- a/python/exploration/dispersion.py
+++ b/python/exploration/dispersion.py
@@ -104,9 +104,54 @@ def create_models(conn, df) -> (pd.DataFrame, float):
         return temp
 
     df["predict"] = df.groupby(["index", "series", "date"])["predict"].transform(aux)
+    df = df.assign(
+        mispricing=(df.exp_percentage - df.predict)
+        * df.index_expected_loss
+        / (df.detach_adj - df.attach_adj)
+    )
     return (df, model)
 
 
+def create_models_separate(df):
+    # Takes the output of get_tranche_data
+    model, calc = {}, {}
+    df = df.assign(
+        tranche_loss_per=(df.exp_percentage * df.index_expected_loss)
+        / (df.detach_adj - df.attach_adj)
+    )
+    df = df.groupby(["date", "index", "series", "tenor", "attach"]).nth(-1)
+    for attach in df.index.get_level_values("attach").unique():
+        calc[attach] = df.loc(axis=0)[:, :, :, "5yr", attach]
+        model[attach] = smf.ols(
+            "logit(tranche_loss_per) ~ "
+            "np.log(moneyness)* logit(gini) + "
+            "np.log(index_expected_loss)* logit(gini) + "
+            "np.log(index_duration) + "
+            "I(np.log(moneyness)**2) + I(np.log(moneyness)**3)",
+            data=calc[attach],
+        ).fit()
+
+        calc[attach] = calc[attach].assign(
+            predict=expit(model[attach].predict(calc[attach]))
+            * (df.detach_adj - df.attach_adj)
+            / df.index_expected_loss
+        )
+
+    calc = pd.concat(calc, sort=False).reset_index(level=0, drop=True)
+    normalization = calc.groupby(["date", "index", "series", "tenor"])["predict"].sum()
+    calc = calc.merge(
+        normalization, left_index=True, right_index=True, suffixes=["_preN", "_sum"]
+    )
+    calc["predict_N"] = calc["predict_preN"] / calc["predict_sum"]
+    calc["mispricing"] = (
+        (calc["exp_percentage"] - calc["predict_N"])
+        * calc["index_expected_loss"]
+        / (calc["detach_adj"] - calc["attach_adj"])
+        * 100
+    )
+    return model, calc
+
+
 if __name__ == "__main__":
     index_type = "HY"
     series = 29