Source code for frds.measures._systemic_expected_shortfall

import numpy as np

[docs] class SystemicExpectedShortfall: """:doc:`/measures/systemic_expected_shortfall`"""
[docs] def __init__( self, mes_training_sample: np.ndarray, lvg_training_sample: np.ndarray, ses_training_sample: np.ndarray, mes_firm: float, lvg_firm: float, ) -> None: """__init__ Args: mes_training_sample (np.ndarray): ``(n_firms,)`` array of firm ex ante MES. lvg_training_sample (np.ndarray): ``(n_firms,)`` array of firm ex ante LVG (say, on the last day of the period of training data) ses_training_sample (np.ndarray): ``(n_firms,)`` array of firm ex post cumulative return for date range after `lvg_training_sample`. mes_firm (float): The current firm MES used to calculate the firm (fitted) SES value. lvg_firm (float): The current firm leverage used to calculate the firm (fitted) SES value. """ assert mes_training_sample.shape == lvg_training_sample.shape assert mes_training_sample.shape == ses_training_sample.shape self.mes = mes_training_sample self.lvg = lvg_training_sample = ses_training_sample self.mes_firm = mes_firm self.lvg_firm = lvg_firm
[docs] def estimate(self, version="BFLV2012") -> float: """estimate Args: version (str, optional): version of methods. Any of ["BFVL2012", "APPR2017"]. Defaults to "BFLV2012". Returns: float: The systemic risk that firm :math:`i` poses to the system at a future time. """ assert version in [ "BFLV2012", "APPR2017", ] if version == "BFLV2012": return self._bflv2012() if version == "APPR2017": raise NotImplementedError
def _bflv2012(self) -> float: n_firms = self.mes.shape data = np.vstack([np.ones(n_firms), self.mes, self.lvg]).T betas = np.linalg.lstsq(data,, rcond=None)[0] _, b, c = betas ses = (b * self.mes_firm + c * self.lvg_firm) / (b + c) return ses