| 1,839 | 22 | 285 |
| 下载次数 | 被引频次 | 阅读次数 |
防范化解系统性金融风险是维护金融安全和经济安全的内在要求，关键要对风险进行准确度量和实时监测。本文运用混频动态因子模型，构建基于传统金融统计数据和网络搜索大数据的日度频率中国金融压力指数(FSI),度量并监测中国的系统性金融风险。研究表明：所构建的FSI能度量并监测中国系统性金融风险，指数的阶段性变化特征和区制状态识别结果与系统性金融风险的现实演进情况高度吻合；经济主体的预期不确定性和风险感知是金融压力的主要来源，并且金融压力有较强的持续性；引入大数据指标对FSI模型的估计具有明显改善作用，并能提高FSI对产出、通胀等宏观经济变量的预测效果；“高压力”区制下的FSI对月同比CPI增速的预测效果较好，“低压力”区制下的FSI对GDP增速的预测效果较好。监管部门未来需继续优化金融风险监测预警体系，提升监测能力，合理引导市场预期。
Abstract：Preventing and resolving systemic financial risk is of great significance to maintaining financial security. The key issue is to accurately describe and monitor systemic financial risk in real time. Based on the mixed frequency dynamic factor model, this paper constructs a daily frequency China's Financial Stress Index(FSI) by comprehensively using traditional financial statistics and Internet search big data to monitor China's systemic financial risks in real time. The research shows that the constructed FSI can accurately measure and monitor domestic systemic financial risk, and the phased change characteristics of the index and the identification results of regime state are highly consistent with the actual evolution of systemic financial risk. The uncertainty of expectation and risk perception of economic subjects are the main sources of financial stress, and China's financial stress has strong sustainability. The introduction of big data indicator can significantly improve the estimation of FSI model and promote the prediction effect of FSI on macroeconomic variables. The FSI on “high level” regime state is better in predicting month-on-month CPI growth, while the FSI on “low level” regime state is better in predicting GDP growth. In the future, regulatory authorities need to continue to optimize the financial risk monitoring and early warning system, improve monitoring capabilities, and reasonably guide market expectations.
陈磊，王艺枞，孟勇刚．2019．基于混频数据的中国服务业景气指数构建与周期波动分析[J]．财贸研究，30(2):17-26.Chen L,Wang Y C,Meng Y G.2019.Construction of China's service sector's coincident index and cycle fluctuation analysis based on mixed-frequency data[J].Finance and Trade Research,30(2):17-26.(in Chinese)
邓创，赵珂．2018．中国的金融压力及其对宏观经济景气的影响动态[J]．财经研究，44(7):86-98,113.Deng C,Zhao K.2018.Financial stress and its impact on the business cycle in China[J].Journal of Finance and Economics,44(7):86-98,113.(in Chinese)
丁慧，陈颖，卞志村．2020．中国金融市场压力指数构建及其宏观经济非线性效应[J]．现代财经，40(8):18-30.Ding H,Chen Y,Bian Z C.2020.The construction of China's financial market stress index and its macroeconomic nonlinear effects[J].Modern Finance and Economics,40(8):18-30.(in Chinese)
赖娟，吕江林．2010．基于金融压力指数的金融系统性风险的测度[J]．统计与决策，(19):128-131.Lai J,Lyv J L.2010.Measurement of financial systemic risk based on financial stress index[J].Statistics and Decision,(19):128-131.(in Chinese)
马勇．2011．系统性金融风险:一个经典注释[J]．金融评论，(4):1-17,123.Ma Y.2011.Systemic financial risk:A classical note[J].Chinese Review of Financial Studies,(4):1-17,123.(in Chinese)
马勇，黄科．2019．金融压力指数及其政策应用:基于中国的实证分析[J]．金融监管研究，(7):1-17.Ma Y,Huang K.2019.Financial stress index and its policy applications:An empirical analysis based on the Chinese economy[J].Financial Regulation Research,(7):1-17.(in Chinese)
苗子清．2020．中国金融压力的测度和时变特征研究---基于MIMIC和MS-AR模型的分析[J]．价格理论与实践，(9):102-106.Miao Z Q.2020.Research on the measurement and time-varying characteristics of China's financial pressure-Based on the analysis of MIMIC and MS-AR models[J].Price:Theory&Practice,(9):102-106.(in Chinese)
苗子清，张卓群．2020．基于大数据方法的中国系统性金融风险预警研究[J]．经济论坛，(12):5-17.Miao Z Q,Zhang Z Q.2020.Research on China's systematic financial risk earlywarning based on big data methods[J].Economic Tribune,(12):5-17.(in Chinese)
苗子清，张涛，党印．2021．中国银行体系系统性金融风险传染研究---基于24家A股银行的大数据与机器学习分析[J]．金融评论，13(5):58-74,124-125.Miao Z Q,Zhang T,Dang Y.2021.Systemic risk and contagion in China's banking system-Big data and machine learning analysis with 24 A-share listed banks[J].Chinese Review of Financial Studies,13(5):58-74,124-125.(in Chinese)
清华大学国家金融研究院金融与发展研究中心课题组．2019．中国系统性金融压力的监测[J]．国际金融研究，(12):3-12.Center for Finance and Development,Tsinghua National Institute of Financial Research.2019.Research on monitoring systemic financial stress in China[J].Studies of International Finance,(12):3-12.(in Chinese)
尚玉皇，郑挺国．2018．中国金融形势指数混频测度及其预警行为研究[J]．金融研究，(3):21-35.Shang Y H,Zheng T G.2018.The mixed frequency measuring of Chinese FCI and its risk warning behavior[J].Journal of Financial Research,(3):21-35.(in Chinese)
王艺枞，陈磊，孟勇刚．2019．数据发布下的服务业景气实时预测[J]．统计与决策，35(21):30-34.Wang Y C,Chen L,Meng Y G.2019.Real-time forecast of service industry boom under data release[J].Statistics and Decision,35(21):30-34.(in Chinese)
肖争艳，任梦瑶．2021．媒体风险感知与系统性金融风险预警[J]．财经问题研究，(9):63-74.Xiao Z Y,Ren M Y.2021.Media risk perception and early warning of systemic financial risk[J].Research on Financial and Economic Issues,(9):63-74.(in Chinese)
许涤龙，陈双莲．2015．基于金融压力指数的系统性金融风险测度研究[J]．经济学动态，(4):69-78.Xu D L,Chen S L.2015.Research on the measurement of systemic financial risk:Based on financial stress index[J].Economic Perspectives,(4):69-78.(in Chinese)
徐国祥，李波．2017．中国金融压力指数的构建及动态传导效应研究[J]．统计研究，34(4):59-71.Xu G X,Li B.2017.Study on the construction of China's financial stress index and its dynamic transmission effect[J].Statistical Research,34 (4):59-71.(in Chinese)
张勇，彭礼杰，莫嘉浩．2017．中国金融压力的度量及其宏观经济的非线性效应[J]．统计研究，34(1):67-79.Zhang Y,Peng L J,Mo J H.2017.The measurement of finance stress and nonlinear effect on macroeconomic activity in China[J].Statistical Research,34(1):67-79.(in Chinese)
郑金麟，张业圳，谢八妹．2019．我国系统性金融风险的衡量与识别[J]．金融监管研究，(12):54-65.Zheng J L,Zhang Y Z,Xie B M.2019.The measurement and prediction of China's systemic risk[J].Financial Regulation Research,(12):54-65.(in Chinese)
Aruoba S B,Diebold F X,Scotti C.2009.Real-time measurement of business conditions[J].Journal of Business&Economic Statistics,27(4):417-427．
Balakrishnan R,Danninger S,Tytell I,et al.2009.The transmission of financial stress from advanced to emerging economies[R].International Monetary Fund Working Paper No.09/133．
Bloom N.2009.The impact of uncertainty shocks[J].Econometrica,77(3):623-685．
Brochado A.2020.Google search based sentiment indexes[J].IIMB Management Review,32(3):325-335．
Da Z,Engelberg J,Gao P J.2015.The sum of all FEARS investor sentiment and asset prices[J].The Review of Financial Studies,28(1):1-32．
Gauthier C,Graham C,Liu Y.2004.Financial conditions indexes for Canada[R].Bank of Canada Working Paper 04-22．
Goodhart C A E,Hofmann B.2001.Asset prices,financial conditions and the transmission of monetary policy[R].Federal Reserve Bank of San Francisco Working Paper．
Hakkio C S,Keeton W R.2009.Financial stress:What is it,how can it be measured,and why does it matter?[J].Economic Review,Federal Reserve Bank of Kansas City,94(2):5-50．
HollóD,Kremer M,Lo Duca M.2012.CISS-A composite indicator of systemic stress in the financial system[R].European Central Bank Working Paper．
Illing M,Liu Y.2006.Measuring financial stress in a developed country:An application to Canada[J].Journal of Financial Stability,2(3):243-265．
International Monetary Fund.2002.Financial soundness indicators:Analytical aspects and country practices[R].International Monetary Fund Occasional Paper．
Irresberger F,K?nig F E,Wei?G N F.2017.Crisis sentiment in the U.S.insurance sector[J].Journal of Risk and Insurance,84(4):1295-1330．
Kaufman G G.1996.Bank failures,systemic risk,and bank regulation[R].Federal Reserve Bank of Chicago Working Paper．
Kibritcioglu A.2002.Excessive risk-taking,banking sector fragility,and banking crises[R].University of Illinois at Urbana-Champaign Working Paper,No 02-0114．
Kliesen K L,Smith D C.2010.Measuring financial market stress[J].Economic Synopses,(2):1-2．
Kostopoulos D,Meyer S,Uhr C.2020.Google search volume and individual investor trading[J].Journal of Financial Markets,49:100544．
Nyman R,Kapadia S,Tuckett D.2021.News and narratives in financial systems:Exploiting big data for systemic risk assessment[J].Journal of Economic Dynamics&Control,127:104119．
Van den End J W.2006.Indicator and boundaries of financial stability[R].Nederlandsche Bank Working Paper．
Van Roye B.2011.Financial stress and economic activity in Germany and the Euro area[R].Kiel Working Paper.
(1)货币市场、债券市场、资本市场、金融中介和外汇市场。
(2)经济主体对预期损失的风险感知提高,在金融行为上会表现为追逐高质量和高流动性资产,将导致资产利差上升;经济主体对经济前景和资产基本价值预期的不确定性提高,将导致资产价格的波动率上升(Hakkio and Keeton,2009;Bloom,2009)。
(3)主观选词法指根据研究者的经验进行主观判断确定关键词的方法;模型选词法包括百度搜索引擎的长尾关键词拓展法、需求图谱拓展法和网页链接搜索拓展法;参考文献法指参考借鉴经典文献确定关键词的方法。
基本信息:
DOI：10.16513/j.cnki.cje.20220923.001
中图分类号:F832;F224
引用信息:
[1]党印,苗子清,张涛.中国金融压力实时监测研究——基于混频大数据动态因子模型的分析[J].经济学报,2022,9(04):65-87.DOI:10.16513/j.cnki.cje.20220923.001.
基金信息:
研究阐释党的十九届五中全会精神国家社科基金重大项目“中央银行的逻辑与现代中央银行制度的建设”(项目编号:21ZDA045)的阶段性成果
2022-09-27
2022-09-27
2022-09-27