中国金融形势的动态特征与演变机理分析:1996-2016

本文分析了1996-2016年中国金融形势的变化趋势及影响金融形势的主导变量的动态特征,探究不同金融市场发展状况对中国金融整体形势及金融风险的影响力变迁.我们首次运用动态模型选择的时变因子增广向量自回归模型(DMS-TVP-FAVAR)测算了中国月度金融形势指数,考察了货币政策、外汇市场和资本流动、货币市场、银行业、股票市场、债券市场、非传统金融市场和国外金融市场对中国金融形势的差别化影响.研究发现,样本期内货币供应量一直是影响中国金融形势最主要的因素,非传统金融市场、外汇市场的影响程度日益加深;在国际金融危机期间,国外金融市场对中国金融形势表现出主导性的影响.总的来看,对中国金融形势的动态特征与演变机理分析有助于及时识别潜在金融风险.

Modeling China's Financial Condition Dynamics and Their Mechanism: 1996-2016

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 M_j=(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.