import pandas as pd import statsmodels.api as sm import numpy as np from scipy.stats import f def FF3factorsmulti_and_GRStest(data_matrix,assetnum):#FF多资产联合检验;data_matrix格式:date mkt smb hml rfreturn ……资产…… T=len(data_matrix)##时间序列长度 K=3##三因子 N=assetnum##资产数量 y=data_matrix.iloc[:,5:5+N].values-data_matrix.loc[:,['rfreturn']].values##各个资产的超额收益.reshape((107,1) x = data_matrix.loc[:, ['mkt', 'smb', 'hml']].values x=sm.add_constant(x)##加一列全1 xTx=np.dot(np.transpose(x),x) yTx=np.dot(np.transpose(y),x) AB_hat=np.dot(yTx,np.linalg.inv(xTx)) AB_hat=np.transpose(AB_hat)##从N*4变成了4*N ALPHA=AB_hat[0] RESD = y - np.dot(x, AB_hat)##用实际值减去系数计算出来的值，得到差值 COV = np.dot(np.transpose(RESD), RESD) / T invCOV = np.linalg.inv(COV) fs = x[:, [1, 2, 3]]##x加在第一列上 x=sm.add_constant(x)加在第一列上 muhat = np.mean(fs, axis=0)##axis=0,axis=1分别对应按列求平均和按行求平均 fs = fs - np.mean(fs, axis=0) omegahat = np.dot(np.transpose(fs), fs) / T invOMG = np.linalg.inv(omegahat) xxx = np.dot(np.dot(np.transpose(muhat), invOMG), muhat) yyy = np.dot(np.dot(ALPHA, invCOV), np.transpose(ALPHA)) GRS = (T - N - K) / N * (1 / (1 + xxx)) * yyy print(GRS) pvalue = 1 - f.cdf(GRS, N, T - N - K) print('三因子模型的多资产检验结果') print( '{:>7s},{:>7s},{:>7s},{:>7s},{:>7s},{:>7s},{:>7s},{:>7s},{:>7s}'.format('alpha1', 'alpha2', 'alpha3', 'alpha4', 'alpha5', 'alpha6', 'alpha7', 'GRS', 'pvalue')) print('{:7.4f},{:7.4f},{:7.4f},{:7.4f},{:7.4f},{:7.4f},{:7.4f},{:7.4f},{:7.4f}'.format(ALPHA[0], ALPHA[1], ALPHA[2], ALPHA[3], ALPHA[4], ALPHA[5], ALPHA[6], GRS, pvalue)) #FF多资产联合检验;data_matrix格式:date mkt smb hml rfreturn ……资产…… ##assetnum:资产个数 def FF3factors_readcsv():## data_factors = pd.read_csv('E:\大三下study\蒋志强\実験3\学习通资料\Data_FFFactors.csv', encoding='GB2312', usecols=[2, 6, 7, 8]) data_factors.columns = ['date', 'mkt', 'smb', 'hml'] data_factors['date'] = pd.to_datetime(data_factors['date']) data_factors['yearmonth'] = data_factors['date'].dt.strftime('%Y%m') data_index = pd.read_csv('E:\大三下study\蒋志强\実験3\学习通资料\Data_Index.csv', encoding='GB2312', usecols=[1, 2, 4]) data_index.columns = ['idxname', 'date', 'return'] data_index['date'] = pd.to_datetime(data_index['date']) data_index['yearmonth'] = data_index['date'].dt.strftime('%Y%m') idxname = np.unique(data_index['idxname'].values) data_xinxi = data_index[data_index['idxname'] == idxname[0]] data_gongyong = data_index[data_index['idxname'] == idxname[1]] data_yiyao = data_index[data_index['idxname'] == idxname[2]] data_kexuan = data_index[data_index['idxname'] == idxname[3]] data_gongye = data_index[data_index['idxname'] == idxname[4]] data_cailiao = data_index[data_index['idxname'] == idxname[6]] data_xiaofei = data_index[data_index['idxname'] == idxname[7]] data_dianxin = data_index[data_index['idxname'] == idxname[8]] data_nengyuan = data_index[data_index['idxname'] == idxname[9]] data_jinrong = data_index[data_index['idxname'] == idxname[10]] data_rf = pd.read_csv('E:\大三下study\蒋志强\実験3\学习通资料\Data_RiskFreeReturn.csv', encoding='GB2312', usecols=[0, 3]) data_rf.columns = ['date', 'rfreturn'] data_rf['date'] = pd.to_datetime(data_rf['date']) data_rf['yearmonth'] = data_rf['date'].dt.strftime('%Y%m') data_matrix = pd.merge(left=data_factors[['yearmonth', 'date', 'mkt', 'smb', 'hml']], right=data_xinxi[['yearmonth', 'return']], on=['yearmonth'], how='inner') data_matrix = pd.merge(left=data_matrix, right=data_gongyong[['yearmonth', 'return']], on=['yearmonth'], how='inner') data_matrix = pd.merge(left=data_matrix, right=data_yiyao[['yearmonth', 'return']], on=['yearmonth'], how='inner') data_matrix = pd.merge(left=data_matrix, right=data_kexuan[['yearmonth', 'return']], on=['yearmonth'], how='inner') data_matrix = pd.merge(left=data_matrix, right=data_gongye[['yearmonth', 'return']], on=['yearmonth'], how='inner') data_matrix = pd.merge(left=data_matrix, right=data_cailiao[['yearmonth', 'return']], on=['yearmonth'], how='inner') data_matrix = pd.merge(left=data_matrix, right=data_xiaofei[['yearmonth', 'return']], on=['yearmonth'], how='inner') data_matrix = pd.merge(left=data_matrix, right=data_dianxin[['yearmonth', 'return']], on=['yearmonth'], how='inner') data_matrix = pd.merge(left=data_matrix, right=data_nengyuan[['yearmonth', 'return']], on=['yearmonth'], how='inner') data_matrix = pd.merge(left=data_matrix, right=data_jinrong[['yearmonth', 'return']], on=['yearmonth'], how='inner') data_matrix = pd.merge(left=data_matrix, right=data_rf[['yearmonth', 'rfreturn']], on=['yearmonth'], how='inner') data_matrix.columns = ['yearmonth', 'date', 'mkt', 'smb', 'hml', 'xinxi', 'gongyong', 'yiyao', 'kexuan', 'gongye', 'cailiao', 'xiaofei', 'dianxin', 'nengyuan', 'jinrong', 'rfreturn'] data_matrix.dropna(inplace=True) data_matrix.sort_values(by='date', inplace=True) return data_matrix## ##三因子文件格式交易所标识_Exchflg 市场标识_Mktflg 日期_Date 市场溢酬因子__流通市值加权_Rmrf_tmv 市值因子__流通市值加权_Smb_tmv 账面市值比因子__流通市值加权_Hml_tmv 市场溢酬因子__总市值加权_Rmrf_mc 市值因子__总市值加权_Smb_mc 账面市值比因子__总市值加权_Hml_mc ##资产文件格式指数代码_IdxCd 指数名称_IdxNm 交易日期_TrdDt 收盘价()_ClPr 指数月收益率_IdxMonRet ##无风险收益率文件格式日期_Date 年份_Year 月份_Month 月无风险收益率_MonRFRet # def GRStest(): if __name__ == '__main__': data_factors = pd.read_csv('E:\大三下study\蒋志强\実験3\学习通资料\Data_FFFactors.csv', encoding='GB2312', usecols=[2, 6, 7, 8]) FF3factorsmulti_and_GRStest(data_matrix,10) # See PyCharm help at https://www.jetbrains.com/help/pycharm/