scholarly journals The Nexus of Monetary Policy and Shadow Banking in China

2018 ◽  
Vol 108 (12) ◽  
pp. 3891-3936 ◽  
Author(s):  
Kaiji Chen ◽  
Jue Ren ◽  
Tao Zha
Keyword(s):  
Monetary Policy ◽  
Empirical Evidence ◽  
Theoretical Explanation ◽  
Bank Loans ◽  
Bank Credit ◽  
Shadow Banking ◽  
Policy Rule ◽  
Institutional Facts ◽  
Monetary Policy Rule ◽  
Effectiveness Of Monetary Policy

We study how monetary policy in China influences banks’ shadow banking activities. We develop and estimate the endogenously switching monetary policy rule that is based on institutional facts and at the same time tractable in the spirit of Taylor (1993). This development, along with two newly constructed micro banking datasets, enables us to establish the following empirical evidence. Contractionary monetary policy during 2009–2015 caused shadow banking loans to rise rapidly, offsetting the expected decline of traditional bank loans and hampering the effectiveness of monetary policy on total bank credit. We advance a theoretical explanation of our empirical findings. (JEL E32, E52, G21, O16, O23, P24, P34)

Monetary-Policy Rule as a Bridge: Predicting Inflation without Predictive Regressions

2018 ◽  
Vol 53 (6) ◽  
pp. 2559-2586 ◽  
Author(s):  
Jian Hua ◽  
Liuren Wu
Keyword(s):  
Monetary Policy ◽  
Interest Rate ◽  
Interest Rates ◽  
Historical Analysis ◽  
Policy Rule ◽  
Estimation Errors ◽  
Predictive Regressions ◽  
Inflation Rates ◽  
Monetary Policy Rule ◽  
Out Of Sample

A major issue with predicting inflation rates using predictive regressions is that estimation errors can overwhelm the information content. This article proposes a new approach that uses a monetary-policy rule as a bridge between inflation rates and short-term interest rates and relies on the forward-interest-rate curve to predict future interest-rate movements. The 2-step procedure estimates the predictive relation not through a predictive regression but far more accurately through the contemporaneous monetary-policy linkage. Historical analysis shows that the approach outperforms random walk out of sample by 30%–50% over horizons from 1 to 5 years.

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