Research on the Transmission Effect of Systematic Financial Risk on Price Based Monetary Policy in China: —An Empirical Analysis Based on DMA-TVP-FAVAR Model and MS-VAR Model

Abstract

Abstract: Monitoring systemic financial risk and identifying the impact of this risk on the operational effects of monetary policy have significant practical value in balancing growth stabilization and risk prevention. The study innovatively applies the DMA-TVP-FAVAR model to build China's systemic financial risk index from a dynamic perspective and evaluates the nonlinear impact of financial risk on the output and price effects of price-based monetary policy with the help of the MS-VAR model. The study finds that:the money market has an important position in China's financial system, and the prevention of financial risks cannot be separated from the smooth operation of the money market, and the banking industry, stock market, real estate industry, and foreign exchange market account for a limited proportion of China's systemic financial risk index, but their relative importance has risen. China's systemic financial risk is characterized by a clear two-zone system and maintains a higher persistence of high-risk zones. Under the role of systemic financial risk, the operational effect of price-based tools shows asymmetry and the existence of financial risk substantially weakens the effectiveness of monetary policy, so it is necessary for the central bank to increase the intensity of monetary policy operations in order to realize the macroeconomic control objectives. The research helps to establish a systemic financial risk response mechanism with Chinese characteristics.

Key words: systemic financial risk, monetary policy, asymmetric, DMA-TVP-FAVAR, MS-VAR

CLC Number:

F832.5

LÜ Zheng, LIU Liping. Research on the Transmission Effect of Systematic Financial Risk on Price Based Monetary Policy in China: —An Empirical Analysis Based on DMA-TVP-FAVAR Model and MS-VAR Model[J]. Journal of Guizhou University of Finance and Economics, 2023(05): 12-21.

References

[1] Benhabib J, Liu X, Wang P. Sentiments, Financial Markets, and Macroeconomic Fluctuations[J]. Journal of Financial Economics, 2016, 120(2):420~443.
[2] Illing M, Liu Y. Measuring Financial Stress in a Developed Country:An Application to Canada[J]. Journal of Financial Stability, 2006, 2(3):243~265.
[3] Hakkio C S, Keeton W R. Financial Stress:What is It, How Can It Be Measured, and Why Does It Matter[J]. Economic Review, 2009, 94(2):5~50.
[4] Cardarelli R, Elekdag S, Lall S. Financial Stress and Economic Contractions[J]. Journal of Financial Stability, 2011, 7(2):78~97.
[5] Girardi G, Ergün A T. Systemic Risk Measurement:Multivariate GARCH Estimation of CoVaR[J]. Journal of Banking & Finance, 2013, 37(8):3169~3180.
[6] 杨子晖, 陈雨恬, 张平淼. 重大突发公共事件下的宏观经济冲击、金融风险传导与治理应对[J]. 管理世界, 2020, 36(5):13~35;7.
[7] 何青, 钱宗鑫, 刘伟. 中国系统性金融风险的度量——基于实体经济的视角[J]. 金融研究, 2018(4):53~70.
[8] 周上尧, 王胜. 中国影子银行的成因、结构及系统性风险[J]. 经济研究, 2021, 56(7):78~95.
[9] Holló D, Kremer M, Lo Duca M. CISS-A Composite Indicator of Systemic Stress in the Financial System[J]. ECB Working Paper, No.1426, 2012.
[10] Davig T, Hakkio C. What is the Effect of Financial Stress on Economic Activity[J]. Federal Reserve Bank of Kansas City, Economic Review, 2010, 95(2):35~62.
[11] Hubrich K, Tetlow R J. Financial Stress and Economic Dynamics:The Transmission of Crises[J]. Journal of Monetary Economics, 2015, 70:100~115.
[12] 崔百胜, 高崧耀. G20国家差异化金融条件下货币政策的非对称性传导研究[J]. 国际贸易问题, 2019(8):138~156.
[13] 杨子晖, 陈雨恬, 陈里璇. 极端金融风险的有效测度与非线性传染[J]. 经济研究, 2019, 54(5):63~80.
[14] 杨子晖, 陈里璇, 陈雨恬. 经济政策不确定性与系统性金融风险的跨市场传染——基于非线性网络关联的研究[J]. 经济研究, 2020, 55(1):65~81.
[15] 王立勇, 吕政. 利率走廊熨平市场流动性缺口的作用机制研究[J]. 系统工程理论与实践, 2022, 42(3):617~636.
[16] Lü Z, Xu J, Liu L. The Interest Rate Transmission Mechanism and Transmission Efficiency of Monetary Policy in China:Empirical Analysis Based on TVP-SV-VAR Model[J]. Journal of the Asia Pacific Economy, 2023:1~35.
[17] 王立勇, 吕政. 资产证券化与货币政策利率传导效率:证据与机制[J]. 中央财经大学学报, 2023(8):42~58.
[18] 吕政, 刘丽萍. 金融数字化与货币政策有效性[J]. 现代经济探讨, 2023(9):36~48.
[19] Koop G, Korobilis D. A New Index of Financial Conditions[J]. European Economic Review, 2014, 71:101~116.
[20] Raftery A E, Kárný M, Ettler P. Online Prediction Under Model Uncertainty via Dynamic Model Averaging:Application to a Cold Rolling Mill[J]. Technometrics, 2010, 52(1):52~66.
[21] 徐国祥, 李波. 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, 2017, 34(4):59~71.
[22] 李敏波, 梁爽. 监测系统性金融风险——中国金融市场压力指数构建和状态识别[J]. 金融研究, 2021(6):21~38.
[23] 吕政, 胡晨沛. 结构突变下国际油价波动与中国石油行业发展的多重联动关系[J]. 统计与信息论坛, 2021, 36(6):73~83.
[24] Hamilton J D. A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle[J]. Econometrica, 1989, 57(2):357~384.
[25] Krolzig H M. Markov-Switching Vector Autoregressions:Modelling, Statistical Inference, and Application to Business Cycle Analysis[M]. Springer Science & Business Media, 2013.