Abstract | With the reform and development of the domestic financial industry in recent years, financial factors play a more important role in the economy. Both the enterprise high debt problem, the stock market crash in 2015, and the real estate bubble in 2016, all which tell us that China's credit and financial markets are fragile to these negative impacts. Since 2016, China's non-financial enterprises leverage rate soared from 153% in the end of 2015 to 257% in the end of 2016, ranking first in the world, which increase their risk of default. If the trend goes on like this, China's commercial banks will suffer more loss of default. By the end of 2016, the loss category loan of commercial banks in China is up to ¥2391 million, increased ¥771 million compared to the beginning of the year. Other things being equal, the loan losses of banks will seriously affect the liquidity of banks, leading to financial imbalances and economic fluctuations in China. Although it has been almost 10 years since the outbreak of the Global Financial Crisis(GFC) in 2008, the world economy hasn’t completely recovered from the impact which draws massive discussions and debates continuously. For example ,The Shanghai Development Research Foundation held a symposium of “2017 Symposium on Global Economy and Finance—Reflections on Global Financial Crisis: Causes, Aftermaths and Solutions” on Sep 5th, 2017. Nowadays the global economy , especially China’s economy, is facing a new twist. In order to cope with new changes in global economy, it is still urgent to review and reflect on GFC, particularly via the causes and aftermaths of GFC from current perspectives, by analyzing the transmission mechanism of the financial shocks and the responses of nations to the GFC, and to summarize experiences and lessons from it,.
As we all know that financial factors play an important role in business cycle . To account for the role played by different financial shocks and frictions in the economic fluctuations in China, we consider financial frictions on banks, on households, and on firms in a DSGE model with a entrepreneur sector and a bank sector, and we assume two types of households: patient and impatient. We analyze which kind of shocks were amplified by the frictions on banks and how much the redistribution of wealth affect the business fluctuations under different models--the RBC model, a model with traditional financial frictions on both firms and households, and our proposed model, which combines financial and banking frictions. Compared to previous studies, this paper makes the following contributions: (1) We combine two sets of financial frictions in the model: not only entrepreneurs face frictions in obtaining funds from banks, but also banks face frictions in obtaining funds from households. (2) We introduce both the conventional business-cycle shocks as well as redistribution shocks and credit squeezes shocks. Furthermore, we decompose the financial shocks into household default shock, entrepreneur default shock, household loan-to-value rate shock and entrepreneur loan-to-value rate shock. (3) Based on the estimation of the DSGE model, we illustrate the model’s transmission mechanism in the deposit market, the loan market and the capital market by plotting the model-consistent demand and supply curves derived from the relevant Euler equations. (4) We compare the responses to an estimated one standard deviation household default shock in three models: the RBC model, a model with traditional financial frictions on both firms and households, and our model, which combines conventional financial frictions and banking frictions.
According to the simulation results, we obtain the following conclusions: (1) The financial frictions on banks work mostly to amplify financial shocks affecting banks’ net worth, but matter relatively less for other financial shocks or traditional business-cycle shocks. (2) Because of the crowding out effect, the negative impact of household default shock on economic activity is greater than the negative impact of entrepreneur default shock. (3) The entrepreneur loan-to-value rate shock has a positive impact on output and investment, while the household loan-to-value rate shock has a negative impact on output and investment. (4) The steady-state output, consumption, and investment are higher in the model without banks than in the model with banks by 0.7%, 0.3% and 1.8% respectively. (5) In the benchmark model of dual financial frictions a one standard deviation household default shock leads to a decline in output after one year is 1.5 times that of a single financial friction model, 4 times that of the RBC model.
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