# Analysis of Systemic Risk Scenarios and Stabilization Effect of Monetary Policy under the COVID-19 Shock and Pharmaceutical Economic Recession

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Theoretical Basis of Stabilizing Effect of Monetary Policy

#### 2.1. Stabilization Goals

#### 2.2. Stabilization Tools

#### 2.2.1. Policy Rate

#### 2.2.2. Open Market Operations

#### 2.2.3. Standing Facilities

#### 2.2.4. Statutory Deposit Reserve Ratio (SDRR)

#### 2.3. Stabilization Channels

#### 2.3.1. Neoclassical Conduction Channels

- (1)
- Interest Rate Channel (IRC)

- (2)
- Asset Price Channel (APC)

- (3)
- Exchange Rate Channel (ERC)

#### 2.3.2. Non-Neoclassical Transmission Channels

- (1)
- Bank Lending Channel (BLC)

- (2)
- Broad Credit Channel (BCC)

## 3. Weakening Analysis of Stabilization Effect Based on IS–MP Model

#### 3.1. Model Construction

#### 3.2. Model Analysis

#### 3.2.1. Impact of Financial Intermediary Credit Supply on Output in Systemic Risk Scenarios

#### 3.2.2. Weakening of the Stabilization Effect of Traditional Monetary Policy in Systemic Risk Scenarios

## 4. Quantitative Analysis of China’s Systemic Risks Based on the Financial Stress Index (FSI)

#### 4.1. Indicator Selection

#### 4.2. Financial Stress

#### 4.2.1. Currency Market Financial Stress

#### 4.2.2. Bond Market Financial Stress

#### 4.2.3. Stock Market Financial Stress

#### 4.2.4. Forex Market Financial Stress

#### 4.2.5. Real Estate Market Financial Stress

#### 4.3. Synthesis of CFSI Based on the Time-Varying Modified CRITIC Weighting Method

_{it}be the value of the i-th indicator in the t-th period, and its standardized value is represented by X

_{it}. The standardization method is

_{it}≤ 1. Generally speaking, the calculation formula of the FSI is $FS{I}_{t}={\displaystyle \sum _{i=1}^{n}{\omega}_{it}{X}_{it}}$, that is, assigning corresponding weights to different indicators X

_{it}, and then synthesizing the Financial Stress Index FSI

_{t}.

_{ij}is the correlation coefficient between index i and index j [60]. Therefore, according to the Contrast Intensity and Conflicting Character of the indicators, the information content Cj of the indicator j is quantified as

## 5. Characteristic Fact Investigation of the Stabilizing Effect of China’s Monetary Policy

## 6. Empirical Analysis of the Stabilization Effect of China’s Monetary Policy in the Systemic Risk Scenarios

#### 6.1. Model Construction

_{t}∊ (1, …, M) is an unobservable zoning variable, which obeys discrete-time traversal. It is assumed that the transition of the unobservable regional variable st between regional systems is represented by transition probabilities [65]. The transition probability from system i to system j is

_{t}), A

_{j}(s

_{t}), and ∑(s

_{t}) depend on the variable series mean of the zone variable s

_{t}. A

_{j}(s

_{t}) is an n × n matrix of variable parameters that depends on the zoning variable s

_{t}, describing the correlation between variables. ∑(s

_{t}) is the variance of the disturbance term depending on the zoning variable; p is the lag order of the model.

#### 6.2. Variable Selection

#### 6.3. Model Estimation

#### 6.3.1. Variable Stationarity Test

#### 6.3.2. Form Determination of MS-VAR Model

_{t}= (rM2_gro, rR007, cfsi, cpi, RGDP_gro) is a five-dimensional time series vector; μs

_{t}is the mean vector of regional system transition; A

_{j}is the vector autoregressive parameter matrix; p is the lag order equal to two; and ε

_{t}is a zero-mean.

#### 6.3.3. Estimated Results

#### 6.4. Analysis of Measurement Results

## 7. Discussion

## 8. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 2.**Credit market equilibrium without financial intermediation and systemic risks. “*” represents the L value and i

_{b}/i

_{s}value when the LD curve intersects with the LS curve.

**Figure 4.**Impact of financial intermediation supply shocks. (

**a**) shows a decline in the level of supply of financially intermediated credit; (

**b**) shows a decline in the demand for financially intermediated credit; and (

**c**) shows the output in equilibrium represented by the intersection of the IS and MP curves.

**Figure 6.**Impact of monetary policy on systemic risks in the financial system. In Figure 6, when the central bank tries to ease monetary policy and the MP curve moves from MP

_{1}to MP

_{2}(

**c**), the policy interest rate decreases to the level of i

_{s}

_{3}(

**b**), the financial intermediation credit demand curve moves to the position of XD

_{3}(

**b**), the financial intermediation credit supply curve XS moves to the position of XS

_{3}(

**a**), and the volume of financial intermediation credit supply (demand) decreases to the level of L

_{3}(

**a**).

**Figure 17.**Financial Stress Sub-Indices of various financial markets. (

**a**) Currency Market Sub-Indices (CFSI-C); (

**b**) Bond Market Sub-Indices (CFSI-B); (

**c**) Stock Market Sub-Indices (CFSI-S); (

**d**) Forex Market Sub-Indices (CFSI-F); and (

**e**) Real Estate Market Sub-Indices (CFSI-R).

**Figure 20.**Fluctuations in China’s deposit reserve ratio and one-year time deposit rate. Data source: CEIC.

**Figure 21.**Trend of China’s systemic risk and the stabilization effect of monetary policy. NOTE: (1) The level of systemic risk in China is measured by CFSI. In addition, to better show the trend of China’s systemic risk and the stabilizing effect, the CFSI is multiplied by 100 in the figure. (2) Data source: CEIC.

**Figure 22.**Filtered, smoothed, and predicted probabilities for zones. (

**a**,

**b**) show the filtered probability, smoothed probability, and predicted probability for the time period from the second quarter of 2007 to the second quarter of 2019 for Zone 1 and Zone 2, respectively.

**Figure 23.**Impulse response results of inflation to monetary policy shocks in the two zones. (

**a**) Zone 1 response to orth. stock to rM2_gro; (

**b**) Zone 1 response to orth. stock to rR007; (

**c**) Zone 2 response to orth. stock to rM2_gro; and (

**d**) Zone 2 response to orth. stock to rR007.

**Figure 24.**Impulse response results of output growth to monetary policy shocks in the two zones. (

**a**) Zone 1 response to orth. stock to rM2_gro; (

**b**) Zone 1 response to orth. stock to rR007; (

**c**) Zone 2 response to orth. stock to rM2_gro; and (

**d**) Zone 2 response to orth. stock to rR007.

Variable Name | Calculation Method | Influence Direction | |
---|---|---|---|

Currency market | SHIBOR term spread (M1) | The difference between 1-year period and 7-day SHIBOR | Negative |

Spread between interbank offered rate and Treasury bond yield (M2) | The spread between the 3-month interbank offered rate and the 3-month Treasury bond maturity yield | Positive | |

Bond market | Treasury bond maturity spread (B1) | The term spread between the 10-year Treasury bond and the 3-month Treasury bond yield to maturity | Negative |

Spread between corporate bonds and government bonds (B2) | The difference between the yield to maturity of the 1-year AAA corporate bonds and the 1-year Treasury bonds | Positive | |

Stock market | Stock price volatility (S1) | IGARCH volatility of the Shanghai Composite | Positive |

Forex market | Real effective exchange rate volatility (F1) | IGARCH volatility of the real effective exchange rate | Positive |

Exchange rate fluctuations (F2) | IGARCH volatility of RMB to USD | Positive | |

Real estate market | House price volatility (H1) | IGARCH volatility of commodity housing sales prices | Positive |

House price overvaluation level (H3) | Regression residuals of per capita disposable income and interest rates on real estate prices | Positive |

Variable Selection | Variable Description | Data Sources | |
---|---|---|---|

Monetary policy variables | Real money supply (rM2_gro) | Year-on-year growth rate of M2 excluding price factors | CEIC |

Real short-term rate (rR007) | R007 excluding price factors | CEIC | |

Real economic variables | Actual output (rGDP_gro) | Year-on-year growth rate of real GDP | CEIC |

Inflation (CPI) | CPI year-on-year growth rate | CEIC | |

Systemic risk level | China Financial Stress Index (CFSI) |

Variable Name | T Statistic | p-Value |
---|---|---|

rGDP_gro | −4.900567 *** | 0.0012 |

CPI | −4.250878 *** | 0.0014 |

CFSI | −3.183057 ** | 0.0271 |

rR007 | −3.327355 ** | 0.0211 |

rM2_gro | −4.655218 *** | 0.0027 |

Variable Mean | Variable Intercept | ||
---|---|---|---|

A_{j} Constant | Constant Variance | Markov-Switching Mean Vector Auto-Regressive Model (MSM-VAR) | Markov-Switching Intercept Vector Auto-Regressive Model (MSI-VAR) |

Variable Variance | Markov-Switching Mean Heteroskedastic Vector Auto-Regressive Model (MSMH-VAR) | Markov-Switching Intercept Heteroskedastic Vector Auto-Regressive Model (MSIH-VAR) | |

A_{j} Variable | Constant Variance | Markov-Switching Mean Autoregressive Coefficient Vector Auto-Regressive Model (MSMA-VAR) | Markov-Switching Intercept Autoregressive Coefficient Vector Auto-Regressive Model (MSIA-VAR) |

Variable Variance | Markov-Switching Mean Autoregressive Coefficient Heteroskedastic Vector Auto-Regressive Model (MSMAH-VAR) | Markov-Switching Intercept Autoregressive Coefficient Heteroskedastic Vector Auto-Regressive Model (MSIAH-VAR) |

_{j}represents the vector autoregressive parameter matrix.

Model Form | Log Likelihood | AIC | HQ | SC |
---|---|---|---|---|

MSM(2)-VAR(1) | −85.499 | 5.6122 | 6.3739 | 7.6198 |

MSMA(2)-VAR(1) | −351.0524 | 17.4715 | 18.5994 | 20.4444 |

MSMH(2)-VAR(1) | −65.7095 * | 5.4167 * | 6.3981 * | 8.0035 * |

MSMAH(2)-VAR(1) | −351.0524 | 18.0838 | 19.4314 | 21.6358 |

MSI(2)-VAR(1) | −86.8419 | 5.667 | 6.4287 | 7.6747 |

MSIA(2)-VAR(1) | −145.574 | 10.0234 | 11.4882 | 13.8843 |

MSIH(2)-VAR(1) | −69.9882 | 5.5914 | 6.5728 | 8.1781 |

Conversion Probability | Zone 1 | Zone 2 |
---|---|---|

Zone 1 | 0.9565 | 0.0435 |

Zone 2 | 0.0769 | 0.9231 |

Sample | Frequency | Duration | |
---|---|---|---|

Zone 1 | 24.0 | 0.6389 | 23.00 |

Zone 2 | 25.0 | 0.3611 | 13.00 |

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**MDPI and ACS Style**

Dong, H.; Zheng, Y.; Li, N.
Analysis of Systemic Risk Scenarios and Stabilization Effect of Monetary Policy under the COVID-19 Shock and Pharmaceutical Economic Recession. *Sustainability* **2023**, *15*, 880.
https://doi.org/10.3390/su15010880

**AMA Style**

Dong H, Zheng Y, Li N.
Analysis of Systemic Risk Scenarios and Stabilization Effect of Monetary Policy under the COVID-19 Shock and Pharmaceutical Economic Recession. *Sustainability*. 2023; 15(1):880.
https://doi.org/10.3390/su15010880

**Chicago/Turabian Style**

Dong, Hao, Yingrong Zheng, and Na Li.
2023. "Analysis of Systemic Risk Scenarios and Stabilization Effect of Monetary Policy under the COVID-19 Shock and Pharmaceutical Economic Recession" *Sustainability* 15, no. 1: 880.
https://doi.org/10.3390/su15010880