MunichChainladder.py 文件源码

def __get_MCL_model(self): 
        modelsI=[]
        modelsP=[]
        for i in range(len(self.Incurred.data.columns)):
                modelsI.append(cl.WRTO(self.Incurred.data.iloc[:,i].dropna(), self.Paid.data.iloc[:,i].dropna(), w=1/self.Incurred.data.iloc[:,i].dropna()))
                modelsP.append(cl.WRTO(self.Paid.data.iloc[:,i].dropna(), self.Incurred.data.iloc[:,i].dropna(), w=1/self.Paid.data.iloc[:,i].dropna()))       
        q_f = np.array([item.coefficient for item in modelsI])
        qinverse_f = np.array([item.coefficient for item in modelsP])
        rhoI_sigma = np.array([item.sigma for item in modelsI])
        rhoP_sigma = np.array([item.sigma for item in modelsP])
        #y = np.log(rhoI_sigma[:-1])
        #x = np.array([i + 1 for i in range(len(y))])
        #x = sm.add_constant(x)
        #OLS = sm.OLS(y,x).fit()
        #tailsigma = np.exp((x[:,1][-1]+ 1) * OLS.params[1] + OLS.params[0])
        return rhoI_sigma, rhoP_sigma, q_f, qinverse_f