add ibrd based premium calculation

climada_petals/engine/cat_bonds/premium_class.py

@@ -1,7 +1,14 @@
 import pandas as pd
 import numpy as np
+from pathlib import Path
+from scipy.optimize import curve_fit
+
 import logging
 
+# path to data folder
+DATA_DIR = (Path(__file__).parent.parent).joinpath('data/cat_bonds')
+
+# setup logger 
 LOGGER = logging.getLogger(__name__)
 
 # regression coefficients for chatoro premium calculation (extracted from Chatoro et al., 2022)
@@ -19,6 +26,7 @@ class premium_calculations:
     def __init__(self, bond_simulation_class):
         self.bond_simulation_class = bond_simulation_class
 
+    ### CHATORO-PRICING ###
     def chatoro_premium(self, peak_multi, investment_graded, hybrid_trigger, GCIndex=None, BBSpread=None):
         '''Linear regression formula to calculate the premium based on the regression model presented in Chatoro et al., 2022'''
 
@@ -33,3 +41,43 @@ def chatoro_premium(self, peak_multi, investment_graded, hybrid_trigger, GCIndex
 
         self.chatoro_prem_rate = b_0 + b_1 * self.bond_simulation_class.loss_metrics['EL_ann'] + b_2 * peak_multi + b_3 * GCIndex + b_4 * BBSpread + b_5 * self.bond_simulation_class.term * 12 + b_6 * investment_graded + b_7 * hybrid_trigger
 
+
+    ### IBRD-PRICING ###
+    def monoExp(self, x, a, k, b):
+        '''Exponential function to fit the risk multiple curve'''
+        return a * np.exp(-k * x) + b
+
+    def init_prem_ibrd(self, peril=None, year=None):
+        """
+        Fits a monotonic exponential curve to catastrophe bond data for bonds issued by the World Bank to estimate premium parameters.
+        This function loads IBRD bond data from an Excel file, optionally filters the data by peril type or issuing year,
+        and fits a monotonic exponential function to the relationship between expected loss and risk multiple.
+        The fitted parameters are returned. Optionally, the function can generate and save a plot of the fit.
+        Parameters
+        ----------
+        peril : str, optional
+            Peril type to filter the bonds (e.g., 'Earthquake', 'Flood'). If None, no filtering by peril is applied.
+        year : list or int, optional
+            Issuing year(s) to filter the bonds. If None, no filtering by year is applied.
+        Returns
+        -------
+        ibrd_prem_rate : float
+            The estimated premium rate based on the fitted curve and the bond's expected annual loss.
+        """
+        ibrd_bonds = pd.read_excel(DATA_DIR.joinpath('IBRD_bonds.xlsx'))
+        if peril is not None: 
+            flt_ibrd_bonds = ibrd_bonds[ibrd_bonds['Peril'] == peril]
+            flt_ibrd_bonds = flt_ibrd_bonds.reset_index(drop=True)
+
+        elif year is not None:
+            flt_ibrd_bonds = ibrd_bonds[ibrd_bonds['Issuing date'].isin(year)]
+            flt_ibrd_bonds = flt_ibrd_bonds.reset_index(drop=True)
+
+        else:
+            flt_ibrd_bonds = ibrd_bonds.copy()
+        #perform the fit
+        params_prem_ibrd, cv = curve_fit(self.monoExp, flt_ibrd_bonds['Expected Loss'], flt_ibrd_bonds['Risk Multiple'])
+
+        a, k, b = params_prem_ibrd
+        LOGGER.info(f'Fitted IBRD premium parameters: a={a}, k={k}, b={b}')
+        self.ibrd_prem_rate = self.monoExp(self.bond_simulation_class.loss_metrics['EL_ann']*100, a, k, b) * self.bond_simulation_class.loss_metrics['EL_ann']