基于GARCH-Vine-Copula模型的P2P网贷市场区域利率相依性研究
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摘要
文章从区域角度出发,考虑"宽监管"与"严监管"两个时段,引入藤Copula模型,对P2P利率市场的相依结构进行刻画。研究结果表明:(1)R藤Copula相比C藤Copula更适合用来刻画P2P区域间的相依结构;(2)由"宽监管"进入"严监管"时期,P2P市场利率区域间相依性整体减弱,表明趋严的监管对于降低区域间风险传递是有效的;(3)发达区域的利率引导作用较弱。本文对于P2P监管方向以及监管侧重点的选择具有一定的参考价值。
Under the coverage of Internet technology, P2 P lending has broken through geographical restrictions to a certain extent. Is there some relationship between regions in terms of P2 P interest rate? In view of the shortcomings of previous research, this paper takes the "loose supervision" and "strict supervision" into account,introducing the vine Copula model to describes the dependent structure of the P2 P markets based on regional data.The results show that:(1) R-Vine Copula is more suitable for depicting the interdependent structure between P2 P regions than C-Vine Copula.(2) From the period of "loose supervision" to "strict supervision", the inter-regional dependence of P2 P interest rates is weakened overall, indicating that strict supervision is effective for reducing inter-regional risk infection.(3) the developed regions is weak in interest rate guidance. This paper has certain reference value for the direction of P2 P supervision and the choice of supervision focus.
Under the coverage of Internet technology, P2 P lending has broken through geographical restrictions to a certain extent. Is there some relationship between regions in terms of P2 P interest rate? In view of the shortcomings of previous research, this paper takes the "loose supervision" and "strict supervision" into account,introducing the vine Copula model to describes the dependent structure of the P2 P markets based on regional data.The results show that:(1) R-Vine Copula is more suitable for depicting the interdependent structure between P2 P regions than C-Vine Copula.(2) From the period of "loose supervision" to "strict supervision", the inter-regional dependence of P2 P interest rates is weakened overall, indicating that strict supervision is effective for reducing inter-regional risk infection.(3) the developed regions is weak in interest rate guidance. This paper has certain reference value for the direction of P2 P supervision and the choice of supervision focus.
引文
[1]廖理,李梦然,王正位.聪明的投资者:非完全市场化利率与风险识别——来自P2P网络借贷的证据[J].经济研究, 2014(7):125-137.
[2]李闻一,王宇,谌仁俊.基于城市视角的P2P网贷地域差异研究[J].国际金融研究, 2018(5):87-96.
[3]陈虹,马永健. P2P网贷行业利率定价模式研究[J].当代财经, 2016(5):45-56.
[4]向虹宇,廖理,王正位.注意力与P2P投资者投资决策——来自人人贷的证据[J].经济学报, 2017(3):84-107.
[5]陈霄,叶德珠.中国P2P网络借贷利率波动研究[J].国际金融研究, 2016(1):83-96.
[6]阮素梅,何浩然.中国P2P网贷收益率波动及其溢出效应研究[J].经济问题, 2016(12):42-46.
[7]何启志,彭明生.基于互联网金融的网贷利率特征研究[J].金融研究, 2016(10):95-110.
[8]韦起,魏云捷.基于Markov-vine copula的我国网贷平台对传统金融机构风险传染效应研究[J].系统工程理论与实践, 2018(2).
[9]唐勇,赵涤非,陈江城.互联网金融概论[M].北京:清华大学出版社, 2017:93.
[10]王宗润.金融风险测度与集成研究:基于Copula理论与方法[M].北京:科学出版社, 2014.
[11]王雨薇.基于藤结构Copula模型的中美欧股指联动性计量检验[D].吉林大学, 2015.
[12]马贝贝.中国P2P网络借贷区域差异性研究[D].浙江工商大学, 2016.
[13]郭文伟.国内外股市相依结构演化及其危机传染效应研究[J].国际金融研究,2016(10):63-73.
[14]赵玉荣,张艳华.股市联动的理论机理研究综述[J].时代金融, 2014(30):116+119.
[15]Li S, Lin Z, Qiu J, Safi R, Xiao Z. How friendship networks work in online P2P lending markets[J]. Nankai Business Review International.2015, 6(1):42-67.
[16] Chen D, Li X, Lai F. Gender discrimination in online peer-to-peer credit lending:evidence from a lending platform in China[J]. ElectronicCommerce Research. 2017, 17(4):553-583.
[17]Tao Q, Dong Y, Lin Z. Who can get money? Evidence from the Chinese peer-to-peer lending platform[J]. Information SystemsFrontiers. 2017, 19(3):425-441.
[18]Chen J, Zhang Y, Yin Z. EDUCATION PREMIUM IN THE ONLINE PEER-TO-PEER LENDING MARKETPLACE:EVIDENCE FROM THE BIG DATA IN CHINA[J]. Singapore Economic Review. 2018, 63(1):45-64.
[19]Schweizer B, Wolff E F. On nonparametric measures of dependence for random variables. The Annals of Statistics, 1981, 9(4):879-885.
[20]Joe H, Xu J J. The estimation method of inference functions for margins for multivariate models[R]. Vancouver:Department of Statistics,University of British Columbia. 1996.
1限于篇幅,此处仅列出严监管时期的数据描述性统计表格,宽监管期省略,如有兴趣的读者可向作者索要,邮箱:ctdaiyimin@163.com。
2限于篇幅,此处仅列出严监管时期的GARCH估计结果表格,宽监管期则省略,如有兴趣的读者可向作者索要,邮箱:ctdaiyimin@163.com。
[2]李闻一,王宇,谌仁俊.基于城市视角的P2P网贷地域差异研究[J].国际金融研究, 2018(5):87-96.
[3]陈虹,马永健. P2P网贷行业利率定价模式研究[J].当代财经, 2016(5):45-56.
[4]向虹宇,廖理,王正位.注意力与P2P投资者投资决策——来自人人贷的证据[J].经济学报, 2017(3):84-107.
[5]陈霄,叶德珠.中国P2P网络借贷利率波动研究[J].国际金融研究, 2016(1):83-96.
[6]阮素梅,何浩然.中国P2P网贷收益率波动及其溢出效应研究[J].经济问题, 2016(12):42-46.
[7]何启志,彭明生.基于互联网金融的网贷利率特征研究[J].金融研究, 2016(10):95-110.
[8]韦起,魏云捷.基于Markov-vine copula的我国网贷平台对传统金融机构风险传染效应研究[J].系统工程理论与实践, 2018(2).
[9]唐勇,赵涤非,陈江城.互联网金融概论[M].北京:清华大学出版社, 2017:93.
[10]王宗润.金融风险测度与集成研究:基于Copula理论与方法[M].北京:科学出版社, 2014.
[11]王雨薇.基于藤结构Copula模型的中美欧股指联动性计量检验[D].吉林大学, 2015.
[12]马贝贝.中国P2P网络借贷区域差异性研究[D].浙江工商大学, 2016.
[13]郭文伟.国内外股市相依结构演化及其危机传染效应研究[J].国际金融研究,2016(10):63-73.
[14]赵玉荣,张艳华.股市联动的理论机理研究综述[J].时代金融, 2014(30):116+119.
[15]Li S, Lin Z, Qiu J, Safi R, Xiao Z. How friendship networks work in online P2P lending markets[J]. Nankai Business Review International.2015, 6(1):42-67.
[16] Chen D, Li X, Lai F. Gender discrimination in online peer-to-peer credit lending:evidence from a lending platform in China[J]. ElectronicCommerce Research. 2017, 17(4):553-583.
[17]Tao Q, Dong Y, Lin Z. Who can get money? Evidence from the Chinese peer-to-peer lending platform[J]. Information SystemsFrontiers. 2017, 19(3):425-441.
[18]Chen J, Zhang Y, Yin Z. EDUCATION PREMIUM IN THE ONLINE PEER-TO-PEER LENDING MARKETPLACE:EVIDENCE FROM THE BIG DATA IN CHINA[J]. Singapore Economic Review. 2018, 63(1):45-64.
[19]Schweizer B, Wolff E F. On nonparametric measures of dependence for random variables. The Annals of Statistics, 1981, 9(4):879-885.
[20]Joe H, Xu J J. The estimation method of inference functions for margins for multivariate models[R]. Vancouver:Department of Statistics,University of British Columbia. 1996.
1限于篇幅,此处仅列出严监管时期的数据描述性统计表格,宽监管期省略,如有兴趣的读者可向作者索要,邮箱:ctdaiyimin@163.com。
2限于篇幅,此处仅列出严监管时期的GARCH估计结果表格,宽监管期则省略,如有兴趣的读者可向作者索要,邮箱:ctdaiyimin@163.com。