Summary:
In 2017, Moody's and Standard & Poor's downgraded the credit rating of China's sovereign debt. The Chinese government responded by claiming that these rating agencies had exaggerated China's economic difficulty and underestimated its reform efforts. It is thus a matter of dispute whether the firms' rating methods are applicable to China. For a rapidly developing economy like China, a forward-looking and dynamic judgment of its financial condition is needed, in addition to traditional economic models based on historical data. An index rating system constructed with fixed coefficient weights cannot capture the dynamic development of China's financial markets. In this paper, we implement a new method based on dynamic model selection with time-varying parameters and factor-augmented vector autoregression (DMS-TVP-FAVAR) to calculate an index of China's financial condition. This method is based on the FAVAR model with dynamic coefficient parameters. Using this TVP-FAVAR model, we have Mj=(2^k-1)different models to construct an indicator system with different combinations of financial variables. According to Raftery et al. (2010), the probability of a given indicator system and model at time t is calculated using the BIS information principle. The model corresponding to the maximum use probability is taken as the dynamically selected model at time t. Without altering its basic structure, this method can dynamically incorporate new factors based on changes in the financial system and structure. Using the new method, we first calculate the time-varying patterns of the Chinese financial market on a monthly basis from 1996 to 2016, and analyze the effects of different financial markets on China's overall financial condition through the dynamic changes in factor weights. China's financial condition index is composed of eight primary indicators: its monetary policy, foreign exchange market, money market, banking, stock market, bond market, non-traditional financial market, and foreign financial market. We use two macroeconomic variables (output and inflation) as tracking variables to determine the dynamic model selection and the time variance of the coefficients. The monthly output growth rate is represented by the change in industrial value-added, and the inflation rate is measured by the change in CPI. According to our calculation, the China Financial Conditions Index (CFCI) has since 1996 shown cyclical fluctuations. The CFCI was relatively low during the financial crisis, but generally increased during the boom period. Using the breakpoint segmentation method proposed by Bai and Perron (1998), we identify three structural breaks in China's financial condition based on our index: 2000M11, 2007M1, and 2011M8. In terms of the dynamics of factor weights, we conclude that during the sample period, money supply was the most important factor affecting China's financial condition. As the level of financial development increased, other key variables affecting China's financial condition changed, shifting from traditional banking and stock market factors to factors associated with the non-traditional financial market and foreign exchange market. Note that before and after the 2008 international financial crisis, foreign exchange market and foreign financial market factors had a strong impact on China's financial condition. The main contribution of this article lies in its use of the DMS-TVP-FAVAR model to construct China's financial condition index and capture the ongoing changes in China's financial status from the 1990s to the present. Our model also explains the various factors leading to these dynamic changes. Compared with other financial condition indices, ours performs better in tracking and characterizing the key structural changes in China's financial development. Unlike the previous literature, we use high-frequency data (i.e., monthly instead of quarterly) to provide a more accurate picture of China's financial condition. We believe that by understanding the dynamic nature of China's financial condition, we can identify potential financial risks in a timely manner. Our findings will also help regulators to implement monetary policy and financial risk management.
罗煜, 甘静芸, 何青. 中国金融形势的动态特征与演变机理分析:1996-2016[J]. 金融研究, 2020, 479(5): 21-38.
LUO Yu, GAN Jingyun, HE Qing. Modeling China's Financial Condition Dynamics and Their Mechanism: 1996-2016. Journal of Financial Research, 2020, 479(5): 21-38.
Allen, F., and D. Gale, 1999, “Diversity of Opinion and Financing of New Technologies” , Journal of Financial Intermediation, 8 :68~89 .
[13]
Allen, F., and D. Gale, 2000, “Comparing Financial Systems”, Cambridge, MA :MIT Press .
[14]
Bai, J., and P. Perron, 1998, “Estimating and Testing Linear Models with Multiple Structural Changes”, Econometrica, 66(1): 47~0.
[15]
Beaton, K., R. Lalonde, and C. Luu, 2009, “A Financial Conditions Index for the United States”, Discussion Papers,09-11,Bank of Canada.
[16]
Beck, T., 2000, “Financial Structure and Economic Development: Financial Industry and Country Evidence”, World Bank Publications.
[17]
Bernanke, B. S., and B. P. Eliasz, 2005, “Measuring the Effects of Monetary Policy: A Factor-augmented Vector Autoregressive (FAVAR) Approach”, The Quarterly Journal of Economics, 120(1): 387~422.
[18]
Boivin, J., and S. Ng, 2006, “Are More Data Always Better for Factor Analysis？”, Journal of Econometrics, 132(1):169~194.
[19]
Boot, A. W. A., and A. Thakor, 1997, “Financial System Architecture”, Review of Financial Studies, 10: 693~733
[20]
Diamond, D. W., 1984, “Financial Intermediation and Delegated Monitoring”, Review of Economic Studies, 51: 393~414.
[21]
English, W., K. Tsatsaronis, and E. Zoli, 2005,“Assessing the Predictive Power of Measures of Financial Conditions for Macroeconomic Variables”,BIS Papers Chapter,in: Investigating the Relationship Between the Financial & Real Economy,Vol(22),228~252.
[22]
Gauthier, C., C. Graham, and Y. Liu, 2004, “Financial Conditions Indexes for Canada”, Working Papers,04-22,Bank of Canada.
[23]
Goodhart, C., and B. Hofmann, 2002, “Asset Prices and the Conduct of Monetary Policy”, Royal Economic Society Conference.
[24]
Guichard, S., and D. Turner, 2008, “Quantifying the Effect of Financial Conditions on US Activity”, OECD Economics Department Working Papers No.9.
[25]
Hatzius, J., P. Hooper, and F. S. Mishkin et al, 2010, “Financial Conditions Indexes: A Fresh Look after the Financial Crisis”, NBER Working Papers.
[26]
Holz, M., 2005, “A Financial Conditions Index as Indicator for Monetary Policy in Times of Low, Stable Inflation and High Financial Market Volatility”, Macroeconomics and Macroeconomic Policies-Alternatives to the Orthodoxy, 28-29 October, in Berlin.
[27]
Koop, G., and D. Korobilis, 2013, “Large Time-varying Parameter Vars”, Journal of Econometrics, 177(2): 185~198.
[28]
Koop, G., and D. Korobilis, 2014, “A New Index of Financial Conditions”, European Economic Review, 71:101~116.
[29]
Matheson, T., 2014, “New Indicators for Tracking Growth in Real Time”, Journal of Business Cycle Measurement and Analysis,(2013)2,51~71.
[30]
Mayes, D. G., and M. Virén, 2000, “The Exchange Rate and Monetary Conditions in the Euro Area”, Review of World Economics, 136(2):199~231.
[31]
Montagnoli, A., and O. Napolitano, 2004, “Financial Condition Index and Interest Rate Settings: A Comparative Analysis”, Money Macro and Finance (MMF) Research Group Conference 2004 Money Macro and Finance Research Group.
[32]
Raftery, A. E., M. Karny, P. Ettler, 2010, “Online Prediction under Model Uncertainty via Dynamic Model Averaging: Application to A Cold Rolling Mill”, Technometrics, 52: 52~66.
[33]
Swiston, A. J., 2008, “Financial Conditions Index: Putting Credit Where Credit is Due”, Social Science Electronic Publishing, 08(8/161):1~35.
