信息不对称下的互联网信贷利率风险研究
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摘要
目的构建信息不对称下的互联网信贷利率风险模型。方法根据我国互联网信贷市场的实际情况,构建了信息不对称程度的指标,将其与条件分位数自回归模型(CAViaR模型)相结合,提出了度量互联网信贷利率风险的新方法,即信息不对称下的CAViaR模型。结果与结论该模型能够很好地刻画我国互联网信贷市场信息不对称下的利率风险特征,并能直接计算出信息缺失下的互联网信贷利率风险价值(VaR)的大小,可以有效地反映信息不对称程度对未来风险的影响。
Purposes—To construct an internet credit interest rate risk model under asymmetric information.Methods—According to the actual situation of China's internet credit market,the index of information asymmetry degree is constructed.When it is combined with the conditional quantile autoregressive model(CAViaR model),a new method for measuring internet credit interest rate risk,namely,the CAViaR model under information asymmetry,is put forward.Result and Conclusion—The constructed model can not only well describe the interest rate risk characteristics of information asymmetry in China's internet credit market but also directly calculate the internet credit interest rate risk VaR under the lack of information,so it can effectively reflect the impact of information asymmetry on future risks.
Purposes—To construct an internet credit interest rate risk model under asymmetric information.Methods—According to the actual situation of China's internet credit market,the index of information asymmetry degree is constructed.When it is combined with the conditional quantile autoregressive model(CAViaR model),a new method for measuring internet credit interest rate risk,namely,the CAViaR model under information asymmetry,is put forward.Result and Conclusion—The constructed model can not only well describe the interest rate risk characteristics of information asymmetry in China's internet credit market but also directly calculate the internet credit interest rate risk VaR under the lack of information,so it can effectively reflect the impact of information asymmetry on future risks.
引文
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[14]陈鹿婧,杨青骥,孙超凡,等.基于logit模型的P2P公司的个人信贷风险评估[J].新经济,2016(20):50-51.宝鸡文理学院学报(自然科学版),第39卷,第1期,第21-25,38页,2019年3月Journal of Baoji University of Arts and Sciences(Natural Science),Vol.39,No.1,pp.21-25,38,Mar.2019
[2]张清菁.信息不对称视角下网络金融风险的分析——以P2P网贷为实证[J].现代经济信息,2016(5):284-285.
[3]钟肖英.互联网消费金融中的逆向选择和道德风险研究——基于信号传递和重复博弈的视角[J].金融理论与实践,2016(12):59-63.
[4] MARKOWITZ H M.Portfolio selection[J].Journal of Finance,1952,7(1):77-91.
[5]MORGAN J P.Risk Metrics Technical Document[M].New York:Morgan Guaranty Trust Company,1996.
[6]龚锐,陈仲常,杨栋瑞.GARCH族模型计算中国股市在险价值(VaR)风险的比较研究与评述[J].数量经济技术经济研究,2005(7):67-81,133.
[7]欧阳志刚,龚金萍.我国货币市场基金的风险度量及绩效评价[J].财会月刊,2011(12):51-54.
[8] ENGLE R F,MANGANELLI S.CAViaR:Conditional autoregressive value at risk by regression quantiles[J].Journal of Business and Economic Statistics,2004,22(4):367-381.
[9]张颖,张富祥.分位数回归的金融风险度量理论及实证[J].数量经济技术经济研究,2012,29(4):95-109.
[10]彭承亮,何启志.P2P网络借贷市场利率风险测度研究[J].兰州财经大学学报,2016,32(6):62-69.
[11]张俊,王晓莹.基于CAViaR模型的银行间质押回购利率风险研究[J].理论探索,2016(2):18-22.
[12] LIANG J,CAI S.Quantificational study on information asymmetry based information cognition[C].IEEE International Conference on Industrial Informatics,2006:405-410.
[13]张维迎.博弈论与信息经济学[M].上海:上海人民出版社,2012.
[14]陈鹿婧,杨青骥,孙超凡,等.基于logit模型的P2P公司的个人信贷风险评估[J].新经济,2016(20):50-51.宝鸡文理学院学报(自然科学版),第39卷,第1期,第21-25,38页,2019年3月Journal of Baoji University of Arts and Sciences(Natural Science),Vol.39,No.1,pp.21-25,38,Mar.2019