摘要 我国为应对2008年国际金融危机的冲击采取了一系列经济刺激政策,在发挥“稳增长”作用的同时,也在一定程度上导致我国企业部门杠杆水平快速上升,但与此同时,不良贷款率并没有随企业部门杠杆的上升而显著增加。为了解释企业部门违约与杠杆的周期特征,本文在金融加速器模型(Bernanke et al.,1999)基础上,引入政府对企业部门的违约救助机制,建立DSGE模型进行讨论。进一步地,本文还通过一个不合意的去杠杆政策试验表明,忽略资产价格稳定(或者说金融稳定)前提下的去杠杆政策,反而会使企业部门的杠杆和违约率同时上升到一个较高水平。最后,引入一个盯住预期资产价格的动态救助规则能够发挥稳定经济的作用,并提高社会福利水平。
Summary:
Following the 2008 global financial crisis, China adopted a series of stimulus policies to protect against external shocks. These have played a role in stabilizing economic growth but have also promoted increased levels of leverage in various sectors, especially the non-financial enterprise sector. While the leverage rate of the non-financial enterprise sector has risen, the non-performing loan ratio in the banking sector has not shown an upward trend. Intuitively, as leverage increases, the subsequent increase in the external financing premium will also raise borrowing costs and lead to a higher default rate. However, this is not consistent with the data for China during this period. To explain this puzzle, this paper develops a DSGE model that incorporates a bailout mechanism into the financial accelerator model of Bernanke et al. (1999). Our model helps us better understand the default and leverage cycles of the non-financial enterprise sector in China. Our motivation in introducing a bailout mechanism is that in practice, local governments have incentives to provide direct or implicit bailouts to the enterprise sector to stimulate economic growth. There are two channels: first, the lower default rate resulting from bailouts can help commercial banks to relax constraints on the credit supply, thereby increasing credit to the enterprise sector; second, the lower external financing premiums due to a low default rate can stimulate the enterprise sector to invest more, leading to increased demand for credit and higher leverage. Consequently, bailouts play the dual role of leverage accelerator and default decelerator. By computing the steady state, the results show that a higher degree of bailout of the nonfinancial enterprise sector leads to higher leverage and a lower default rate. We then simulate and analyze the impact of technology, risk, and bailout shocks on the economy. Technology shocks make leverage increase, and thus can partially explain the variation in leverage. Technology and risk shocks both lead to economic recession, and thus both make the default rate increase; only bailout shocks cause the default rate to fall. Therefore, it is necessary to incorporate the factor of default bailout to explain the characteristics of the non-performing loan ratio of the banking sector. This paper also simulates a policy experiment on deleveraging and finds that asset prices fall sharply due to deleveraging, which leads to increased leverage. This implies that achieving the policy objective of deleveraging requires asset price stability; thus, financial stability is important for deleveraging policy. Accordingly, a dynamic bailout rule is proposed that considers expected asset prices. Through simulation, we show that when the bailout rule considers expected asset prices, asset prices fluctuate less under technological, risk, and bailout shocks. Reduced fluctuation in asset prices means that capital is more stable under external shocks, which ultimately stabilizes output and consumption and thus improves social welfare. The main contributions of this paper are threefold. First, it develops a DSGE model with a bailout mechanism based on Bernanke et al. (1999), which provides a framework for understanding the formation mechanism behind the high leverage of the nonfinancial enterprise sector in China. Second, it provides a better understanding of the effects of deleveraging policy and the relationship between deleveraging and financial stability. If deleveraging policy ignores the need to stabilize asset prices or lacks the relevant policy tools, deleveraging will lead to the decline of asset prices, which will bring about recession and increased leverage. This study explains why China's previous deleveraging policies had undesirable outcomes. Third, the proposed model can be extended by introducing nominal rigidities to examine the role of monetary policy in supporting asset prices in the deleveraging process. It explains why the central bank's financial stability function and prudent monetary policy are necessary for the deleveraging process. This paper's findings suggest that the two objectives of stabilizing economic growth and deleveraging are not necessarily contradictory if supported by a policy of financial stabilization. This conclusion differs from that reached in the previous literature. The model in this paper can also be extended to include both the state-owned and private-owned sectors when the government provides only an incomplete bailout to the former; such a model can be used to further analyze the impact of the bailout mechanism on resource misallocation.
陆磊, 刘学. 违约与杠杆周期——一个带有救助的金融加速器模型[J]. 金融研究, 2020, 479(5): 1-20.
LU Lei, LIU Xue. Default and Leverage Cycles: A Financial Accelerator Model with Bailouts. Journal of Financial Research, 2020, 479(5): 1-20.
Adrian, T., and Shin, H. S. (2010). Liquidity and leverage. Journal of Financial Intermediation, 19(3), 418~437.
[9]
Adrian, T., and Shin, H. S. (2013). Procyclical leverage and value-at-risk. The Review of Financial Studies, 27(2), 373~403.
[10]
Akinci, O., and Chahrour, R. (2018). Good News Is Bad News: Leverage Cycles and Sudden Stops. Journal of International Economics, 362~375.
[11]
Bernanke, B. S., Gertler, M., and Gilchrist, S., 1999, “The Financial Accelerator in a Quantitative Business Cycle Framework”, Handbook of Macroeconomics, 1341~1393.
[12]
Bernanke, B. S., and Gertler, M., 2001, “Should Central Banks Respond to Movements in Asset prices？”, The American Economic Review, 91(2), 253~257.
[13]
Carlstrom, C. T., Fuerst, T. S., and Paustian, M., 2016, “Optimal contracts, aggregate risk, and the financial accelerator”, American Economic Journal: Macroeconomics, 8(1), 119~47.
[14]
Chang, Chun, Zheng Liu, Mark M. Spiegel, Jingyi Zhang, 2017, “Reserve Requirements and Optimal Chinese Stabilization Policy”, Federal Reserve Bank of San Francisco Working Paper 2016-10. http://www.frbsf.org/economic-research/publications/working-papers/wp2016~10.pdf.
[15]
Christiano, L. J., Motto, R., and Rostagno, M., 2003, “The Great Depression and the Friedman-Schwartz Hypothesis”, Journal of Money, Credit and Banking, 35(6), 1119~1197.
[16]
Christiano, L. J., Eichenbaum, M., and Evans, C. L., 2005, “Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy”, Journal of political Economy, 113(1), 1~45.
[17]
Christiano,L J.,Motto,R.,and Rostagno,M.,2010,“Financial Factors in Economic Fluctuations”,Working Paper Series from European Central Bank.No.1192.
[18]
Coeurdacier, N., Rey, H., and Winant, P., 2011, “The risky steady state”,American Economic Review, 101(3), 398~401.
[19]
de Groot, O., 2013, “Computing the risky steady state of DSGE models”, Economics Letters, 120(3), 566~569.
[20]
Fernández, A., and Gulan, A., 2015, Interest Rates, Leverage, and Business Cycles in Emerging Economies: “The role of Financial Frictions”, American Economic Journal: Macroeconomics, 7(3), 153~88.
[21]
Geanakoplos, J. (2010). The Leverage Cycle. Nber Macroeconomics Annual, 24(1), 1~65.
[22]
Greenwood, J., Hercowitz, Z., and Huffman, G. W. (1988). Investment, Capacity Utilization, and the Real Business Cycle. The American Economic Review, 78(3), 402~417.
[23]
Guerrieri, L., and Iacoviello, M. (2015). OccBin: A Toolkit for Solving Dynamic Models With Occasionally Binding Constraints Easily. Journal of Monetary Economics,22~38.
[24]
Guerrieri, L., and Iacoviello, M. (2017). Collateral Constraints and Macroeconomic Asymmetries. Journal of Monetary Economics, 28~49.
[25]
Justiniano, A., Primiceri, G. E., and Tambalotti, A. (2015). Household Leveraging and Deleveraging. Review of Economic Dynamics, 18(1), 3~20.
[26]
Kiyotaki, N., and Moore, J.,1997, “Credit Cycles”, Journal of Political Economy, 105(2), 211~248.
[27]
Smets, F., and Wouters, R., 2007, “ Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach”, The American Economic Review, 97(3), 586~606.
