In 2013, China’s central bank introduced the standing lending facility to manage the volatility of interbank market rates. This study is the first of its kind in the academic literature by examining the effect of this new monetary policy tool. Based on monthly data between December 2015–November 2018, the empirical results show that the standing lending facility operations can significantly reduce the volatility of overnight SHIBOR, an important policy rate in the Chinese financial markets.
The author would like to thank the Editor and two anonymous referees for helpful comments on an earlier version of this paper. All errors are the author’s sole responsibility.
Appendix A Variance Equations of EGARCH Model
Between November 2013–November 2015, the PBC published its SLF operations on a quarterly or even far less frequent basis. Since November 2015, the PBC started to publish its SLF operations on a monthly basis although the exact dates are not disclosed (see http://www.pbc.gov.cn/zhengcehuobisi/125207/125213/125443/125857/index.html, in Chinese. The English version does not provide enough information, see http://www.pbc.gov.cn/en/3688229/3688335/3730279/index.html). Accordingly, the EGARCH model is based on monthly data between November 2015–November 2018.
As the open market operations intend to influence the level, and the SLFs intend to smooth the volatility of interbank rates, for the mean equations, one lag order of the dependent variable, the open market operations, and SLF operation are included as explanatory variables, and for variance equation, the SLF is the only explanatory variable. However, if including open market operations into the variance equations, the performance of SLF does not change in terms of sigh and significance level.
Model A: Dependent Variable: the first difference of monthly average SHIBOR;
Model B: Dependent Variable: the first difference of monthly average CHIBOR;
Model C: Dependent Variable: the first difference of monthly average R001;
Model D: Dependent Variable: the first difference of monthly average DR001
Method: ML ARCH – Student’s t distribution (BFGS/Marquardt steps). Sample (adjusted): 2015M12 2018M11. Included observations: 36 after adjustments
|Model A||Model B||Model C||Model D|
Note: ***, **, and * denote 1 %, 5 %, and 10 % significance, respectively
The above results show that the signs of the change of ln(SLF) are all negative, showing the potential effect of smoothing volatility. However, they are all insignificant within a 10 % confidence level. It is probably because the significance has lost after using the monthly average interbank rates and the monthly SLFs without knowing the exact dates. Model 4 has a negative r-squared, showing the insignificance of explanatory variables.
Appendix B: Unit Root Tests
Augmented Dickey-Fuller test statistic
Null Hypothesis t-Statistic Prob.* ∆ in Ln(SLF) has a unit root −6.857 0.000 ∆ in Volatility of SHIBOR −10.315 0.000 ∆ in Volatility of CHIBOR −9.787 0.000 ∆ in Volatility of R001 −5.996 0.000 ∆ in Volatility of DR001 −10.135 0.000
Phillips-Perron test statistic
Null Hypothesis Adj. t-Stat Prob.* ∆ in Ln(SLF) has a unit root −7.224 0.000 ∆ in Volatility of SHIBOR −11.405 0.000 ∆ in Volatility of CHIBOR −10.883 0.000 ∆ in Volatility of R001 −26.979 0.000 ∆ in Volatility of DR001 −10.713 0.000
Appendix C: Residual Diagnostics: Correlogram – Q – Statistics
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