# Systemic Importance of China’s Financial Institutions: A Jump Volatility Spillover Network Review

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## Abstract

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## 1. Introduction

- (1)
- Many scholars investigated the jump volatility of a single financial asset on its price fluctuation from the perspective of prediction. We first propose Granger-causality test to identify the jump volatility spillover among financial institutions.
- (2)
- Financial markets are extremely volatile, and the low-frequency data might lose a lot of important information. By employing 5-min high-frequency data, we establish the jump volatility spillover network, which can capture the jump volatility spillover among financial institutions.
- (3)
- We use entropy weight TOPSIS rather than a single indicator to comprehensively assess the SIFIs.

## 2. Methodology

#### 2.1. Network Construction

#### 2.2. Indicator for Assessing the Systemic Importance of Financial Institutions

#### 2.2.1. Out Degree

#### 2.2.2. Clustering Coefficient

#### 2.2.3. Closeness Centrality

#### 2.2.4. Leaderrank Algorithm

#### 2.3. Entropy Weight TOPSIS

## 3. Data

## 4. Empirical Analysis

#### 4.1. Jump Volatility Spillover Network Construction of Financial Institution

#### 4.2. Assessing the Systemic Importance of Financial Institutions

#### 4.3. Assessing the Dynamic Systemic Importance of Financial Institutions

## 5. Conclusions and Discussion

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 1.**Jump volatility spillover network of financial institution. Note: Nodes (financial institutions) from the same sector are signed as the same shape and color. Depositories, broker dealer, and insurances are labelled as red circle, blue square, and green triangle, respectively.

**Figure 2.**The number of total linkages of the jump volatility spillover network as a percentage of all possible linkages over time.

**Table 1.**The descriptive statistics of 5-min high-frequency closing price data of 24 financial institutions.

Code | Financial Institution | Abbreviation | Mean | SD | MAX | MIN |
---|---|---|---|---|---|---|

Panel A: Depositories | ||||||

S000001.SZ | Ping An Bank | PAB | 14.77 | 5.38 | 44.45 | 8.03 |

S002142.SZ | Bank of Ningbo | NBCB | 13.00 | 3.58 | 24.27 | 5.87 |

S600000.SH | Shanghai Pudong Development Bank | SPDB | 14.73 | 6.91 | 61.80 | 7.11 |

S600015.SH | Huaxia Bank | HXB | 10.37 | 2.14 | 23.52 | 6.14 |

S600016.SH | China Minsheng Banking Corp., Ltd. | CMBC | 7.56 | 1.85 | 16.38 | 3.89 |

S600036.SH | China Merchants Bank | CMB | 17.21 | 6.57 | 43.58 | 9.42 |

S601009.SH | Bank of Nanjing | NJBK | 11.24 | 3.55 | 23.49 | 6.41 |

S601166.SH | Industrial Bank | CIB | 18.94 | 8.47 | 61.79 | 8.62 |

S601169.SH | Bank of Beijing | BOB | 10.24 | 3.19 | 22.57 | 5.53 |

S601328.SH | Bank of Communications | BOCOM | 6.01 | 1.76 | 16.15 | 3.60 |

S601398.SH | Industrial and Commercial Bank of China Ltd. | ICBC | 4.61 | 0.83 | 8.35 | 3.15 |

S601939.SH | China Construction Bank | CCB | 5.39 | 1.17 | 10.18 | 3.50 |

S601988.SH | Bank of China | BOC | 3.53 | 0.69 | 6.93 | 2.44 |

S601998.SH | China CITIC Bank | CNCB | 5.60 | 1.29 | 10.93 | 3.39 |

Panel B: Broker-dealers | ||||||

S000686.SZ | Northeast Securities | NESC | 17.82 | 8.78 | 56.33 | 5.10 |

S000728.SZ | Guoyuan Securities | GYSC | 15.36 | 7.00 | 45.70 | 5.55 |

S000783.SZ | Changjiang Securities | CJSC | 11.50 | 4.78 | 40.15 | 4.07 |

S600030.SH | CITIC Securities | CITICS | 19.05 | 10.57 | 98.07 | 9.10 |

S600109.SH | Sinolink Securities | SLSC | 17.83 | 9.28 | 71.66 | 5.77 |

S600837.SH | Haitong Securities | HTSEC | 14.17 | 7.25 | 61.58 | 7.06 |

S601099.SH | Pacific Securities | PSC | 9.66 | 6.30 | 47.45 | 1.94 |

Panel C: Insurances | ||||||

S601318.SH | Ping An Insurance (Group) Co. of China, Ltd. | PAI | 47.47 | 14.37 | 112.55 | 19.90 |

S601601.SH | China Pacific Insurance (Group) Co., Ltd. | CPIC | 24.72 | 7.09 | 50.25 | 10.36 |

S601628.SH | China Life Insurance (Group) Co., Ltd. | CLI | 23.24 | 6.65 | 60.60 | 12.89 |

Symbol | OD | C | CC | LR |
---|---|---|---|---|

PAB | 2 | 1.0000 | 0.2602 | 0.0200 |

NBCB | 8 | 0.8393 | 0.1667 | 0.0120 |

SPDB | 8 | 0.5600 | 0.1684 | 0.0389 |

HXB | 9 | 0.6889 | 0.1633 | 0.0151 |

CMBC | 8 | 0.7632 | 0.1739 | 0.0645 |

CMB | 10 | 0.6574 | 0.1509 | 0.0157 |

NJBK | 1 | 0.0000 | 0.2883 | 0.0120 |

CIB | 1 | 1.0000 | 0.2909 | 0.0129 |

BOB | 9 | 0.5688 | 0.1739 | 0.0806 |

BOCOM | 8 | 0.6081 | 0.1928 | 0.0747 |

ICBC | 9 | 0.5854 | 0.1633 | 0.0786 |

CCB | 8 | 0.5112 | 0.1882 | 0.0930 |

BOC | 10 | 0.6136 | 0.1538 | 0.0403 |

CNCB | 10 | 0.6667 | 0.1495 | 0.0120 |

NESC | 1 | 1.5000 | 0.2991 | 0.0157 |

GYSC | 6 | 0.8429 | 0.1975 | 0.0302 |

CJSC | 1 | 0.0000 | 0.3107 | 0.0271 |

CITICS | 3 | 0.8333 | 0.2222 | 0.0157 |

SLSC | 2 | 2.0000 | 0.2520 | 0.0120 |

HTSEC | 2 | 2.0000 | 0.2520 | 0.0120 |

PSC | 4 | 1.0833 | 0.2133 | 0.0120 |

PAI | 1 | 0.5278 | 0.3107 | 0.0764 |

CPIC | 7 | 0.6685 | 0.1975 | 0.0720 |

CLI | 9 | 0.3382 | 0.1684 | 0.1563 |

Rank | Symbol | Score | Rank | Symbol | Score |
---|---|---|---|---|---|

1 | CLI | 0.7489 | 13 | GYSC | 0.2846 |

2 | CCB | 0.5160 | 14 | CMB | 0.2829 |

3 | BOB | 0.4962 | 15 | CNCB | 0.2703 |

4 | ICBC | 0.4898 | 16 | HXB | 0.2658 |

5 | BOCOM | 0.4605 | 17 | NBCB | 0.2539 |

6 | CPIC | 0.4385 | 18 | NESC | 0.2239 |

7 | CMBC | 0.4409 | 19 | PSC | 0.2088 |

8 | BOC | 0.3697 | 20 | PAB | 0.1914 |

9 | PAI | 0.3244 | 21 | CITICS | 0.1701 |

10 | SPDB | 0.3179 | 22 | CIB | 0.1472 |

11 | SLSC | 0.2920 | 23 | CJSC | 0.0705 |

11 | HTSEC | 0.2920 | 24 | NJBK | 0.0123 |

Index | Equal Weight | PCA | TOPSIS | EWTOPSIS |
---|---|---|---|---|

Correlation coefficients | 0.3355 | 0.5193 *** | 0.4871 *** | 0.5647 *** |

Rank | Symbol | Score | Rank of MC |
---|---|---|---|

1 | PSC | 0.9087 | 18 (46,722,978,887) |

2 | CIB | 0.8685 | 11 (199,800,000,000) |

3 | NBCB | 0.8603 | 20 (36,175,000,000) |

4 | NESC | 0.8553 | 24 (18,917,836,544) |

5 | HTSEC | 0.8381 | 12 (176,610,181,629) |

6 | ICBC | 0.8361 | 1 (2,167,782,336,669) |

7 | BOCOM | 0.8336 | 5 (522,280,130,274) |

8 | PAB | 0.8219 | 16 (64,100,729,703) |

9 | GYSC | 0.7977 | 19 (42,005,029,000) |

10 | SLSC | 0.7787 | 23 (22,425,428,420) |

Rank | Symbol | Score | Rank of MC |
---|---|---|---|

1 | CIB | 0.9579 | 14 (79,900,000,000) |

2 | CMB | 0.9230 | 7 (251,933,891,562) |

3 | PSC | 0.9229 | 18 (27,435,468,619) |

4 | NBCB | 0.9100 | 21 (18,750,000,000) |

5 | PAB | 0.8718 | 16 (36,046,919,598) |

6 | ICBC | 0.8123 | 1 (1,452,981,997,613) |

7 | CCB | 0.8122 | 2 (1,088,991,131,440) |

8 | CITICS | 0.8014 | 10 (133,603,922,140) |

9 | PAI | 0.7996 | 6 (261,557,349,223) |

10 | BOCOM | 0.7904 | 5 (292,496,470,707) |

Rank | Symbol | Score | Rank of MC |
---|---|---|---|

1 | HTSEC | 0.9081 | 14 (256,372,893,000) |

2 | PAB | 0.8741 | 15 (212,626,927,426) |

3 | CJSC | 0.8731 | 19 (62,553,148,673) |

4 | CIB | 0.8451 | 10 (336,845,313,758) |

5 | PSC | 0.8150 | 23 (40,635,675,469) |

6 | HXB | 0.7961 | 16 (143,827,801,960) |

7 | CMB | 0.7886 | 7 (478,924,867,963) |

8 | SPDB | 0.7878 | 12 (322,331,986,051) |

9 | CITICS | 0.7845 | 11 (335,880,700,848) |

10 | CLI | 0.7522 | 4 (1,019,790,556,400) |

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## Share and Cite

**MDPI and ACS Style**

Yang, X.; Zhao, X.; Gong, X.; Yang, X.; Huang, C.
Systemic Importance of China’s Financial Institutions: A Jump Volatility Spillover Network Review. *Entropy* **2020**, *22*, 588.
https://doi.org/10.3390/e22050588

**AMA Style**

Yang X, Zhao X, Gong X, Yang X, Huang C.
Systemic Importance of China’s Financial Institutions: A Jump Volatility Spillover Network Review. *Entropy*. 2020; 22(5):588.
https://doi.org/10.3390/e22050588

**Chicago/Turabian Style**

Yang, Xin, Xian Zhao, Xu Gong, Xiaoguang Yang, and Chuangxia Huang.
2020. "Systemic Importance of China’s Financial Institutions: A Jump Volatility Spillover Network Review" *Entropy* 22, no. 5: 588.
https://doi.org/10.3390/e22050588