# Spillover Effect of the Interaction between Fintech and the Real Economy Based on Tail Risk Dependent Structure Analysis

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Literature Review

## 3. Methodology

#### 3.1. SVAR and Granger Causality Test

_{1t}and u

_{2t}are white noise; q and s both are the lagging periods.

_{α}(q, n − k) under the given significance level α of F distribution, then reject the original hypothesis, where n is the sample size and k is the number of variables to be evaluated.

#### 3.2. GARCH Model

_{t}is the error term.

#### 3.3. Conditional Copula Function

**Condition 1.**- C(u, 0|w) = C(0, v|w) = 0, C(u, 1|w) = u, C(1, v|w) = v; where u, v ϵ I.
**Condition 2.**- u
_{1}, u_{2}, v_{1}, v_{2}are arbitrary variables of type I respectively, u_{1}≤ u_{2}, v_{1}≥ v_{2}, and C(u_{2}, v_{2}|w) – C(u_{2}, v_{1}|w) − C(u_{1}, v_{2}|w) + C(u_{1}, v_{1}|w) ≥ 0.

_{1}and F

_{2}, respectively, and they have connection functions $\mathrm{C}\left({\mathrm{u}}_{1},{\mathrm{u}}_{2}\right)$. The correlation coefficients of upper tail and lower tail are defined as

_{U}or λ

_{L}is 0, then X and Y are independent of each other.

#### 3.4. R-Vine Copula Model

**Condition 1:**- Vine = (T
_{1}, …, T_{m}). **Condition 2:**- T
_{1}is a tree with N_{1}nodes and E_{1}edges on the vine structure. N_{1}= {1, 2, …, n} is all nodes on the tree. The connection between nodes is the edge, and E_{1}represents the set of all edges on the first layer tree. **Condition 3:**- T
_{i}(i = 2, …, m) represents the ith tree on the vine except T_{1}, and N_{1}is the node on T_{1}, which meets N_{i}∈ N_{1}∪ E_{1}∪ E_{2}∪ E_{3}∪ ⋯ ∪ E_{i−1}.

_{i}(i = 1, …, n − 1). Assuming that there are n − i + 1 nodes and n − i edges on T

_{1}, for the remaining n − 2 trees, the edges on T

_{i}are transformed into new nodes of T

_{i+1}.

_{i}is the set of all edges on each layer of the tree, e = $\mathrm{j}\left(\mathrm{e}\right),\mathrm{k}\left(\mathrm{e}\right)|\mathrm{D}\left(\mathrm{e}\right)$ is one of the edges, j(e), k(e) are the condition nodes at both ends of the edge, D(e) is the condition set, and ${\mathrm{c}}_{\mathrm{j}\left(\mathrm{e}\right),\mathrm{k}\left(\mathrm{e}\right)|\mathrm{D}\left(\mathrm{e}\right)}\left(\xb7,\xb7\right)$ is the connection function between the two nodes.

**Step 1:**- Determine the breakdown structure

**Step 2:**- Select two-dimensional copula function

**Step 3:**- Parameter estimation

#### 3.5. CoVaR Model

_{A}and X

_{B}, and the joint density function is f(x

_{A}, x

_{B}), and the edge density function is f(x

_{A}) and f(x

_{B}), and c is the copula connection function between two nodes, then the conditional density function of time series X is

## 4. Results

#### 4.1. Sample and Data Processing

_{t}is the closing price at time t.

#### 4.2. Result of Time Series Analysis

#### 4.2.1. ADF Test

#### 4.2.2. Granger Causality Test

#### 4.2.3. ARCH Effect Test

^{2}(k) statistic shows that the Fintech return series and the CSI index return series have conditional heteroscedasticity.

#### 4.3. Result of Edge Distribution

_{1}represents the coefficient of the ARCH term, namely the square lag term of residual error. β

_{1}represents the GARCH term, that is, the coefficient of the lag term of the conditional variance itself.

_{1}+ β

_{1}is close to 1, indicating that the volatility of return has strong sustainability. In addition, the ARCH term and GARCH term coefficients of each return series are significantly positive at the 95% confidence level, and α

_{1}+ β

_{1}is less than 1, meeting the requirements of model stability.

#### 4.4. Results of Dependent Structure by R-Vine Copula Model

_{U}and λ

_{L}represent the upper and lower tail correlation coefficients, respectively.

#### 4.5. Result of Risk Spillover Effect

## 5. Discussion

#### 5.1. Rationality of Methodology

#### 5.1.1. Time Series Approach

#### 5.1.2. Deficiency of Copula

#### 5.1.3. Advantages of R-Vine Copula

#### 5.2. Managerial Implication

#### 5.3. Limitation and Future Work

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

- Li, J. Thoughts on internet finance. Manag. World
**2015**, 31, 1–7. [Google Scholar] - Lu, Y. Industry 4.0: A survey on technologies, applications and open research issues. J. Ind. Inf. Integrat.
**2017**, 6, 1–10. [Google Scholar] [CrossRef] - Zilgalvis, P. The need for an innovation principle in regulatory impact assessment: The case of finance and innovation in European. Policy Internet
**2015**, 6, 377–392. [Google Scholar] [CrossRef] - Gennaioli, N.; Shleifer, A.; Vishny, R. Neglected risks, financial innovation and financial fragility. J. Financ. Econ.
**2012**, 104, 452–468. [Google Scholar] [CrossRef] [Green Version] - Chen, X.H.; Teng, L.; Chen, W. How does fintech affect the development of the digital economy? Evidence from China. N. Am. J. Econ. Financ.
**2022**, 61, 101697. [Google Scholar] [CrossRef] - Chen, X.H.; Yan, D.; Chen, W. Can the digital economy promote fintech development? Growth Chang.
**2021**, 53, 221–247. [Google Scholar] [CrossRef] - Shin, Y.J.; Choi, Y. Feasibility of the fintech industry as an innovation platform for sustainable economic growth in Korea. Sustainability
**2019**, 11, 5351. [Google Scholar] [CrossRef] [Green Version] - Tian, X.; Li, R.; Yang, G. The impact of fintech on the development of real economy—An empirical analysis based on the dual path of financial innovation. Guangdong Soc. Sci.
**2021**, 17, 5–15. [Google Scholar] - Sun, P.; Zhang, R. Whether financial innovation promotes or hinders economic growth: A panel analysis from the perspective of technological progress. Mod. Econ. Sci.
**2012**, 33, 26–35. [Google Scholar] - Thorsten, B.; Tao, C.; Chen, L. Financial innovation: The bright and the dark sides. J. Bank. Financ.
**2016**, 72, 28–51. [Google Scholar] - Lee, I.; Yong, J.S. Fintech: Ecosystem, business models, investment decisions, and challenges. Bus. Horiz.
**2018**, 61, 35–46. [Google Scholar] [CrossRef] - Rafal, S.; Daniel, P. Conditional correlation coefficient as a tool for analysis of contagion in financial markets and real economy indexes based on the synthetic ratio. Proc. Soc. Behav. Sci.
**2016**, 220, 452–461. [Google Scholar] - Vucinic, M.; Luburic, R. Fintech, risk-based thinking and cyber risk. J. Cent. Bank. Theo. Pract.
**2022**, 11, 27–53. [Google Scholar] [CrossRef] - Chen, R.; Chen, H.; Jin, C.; Wei, B.; Yu, L. Linkages and spillovers between internet finance and traditional finance: Evidence from China. Emerg. Market. Financ. Trad.
**2020**, 56, 1196–1210. [Google Scholar] [CrossRef] - Namchoochai, R.; Kiattisin, S.; Darakorn Na Ayuthaya, S.; Arunthari, S. Elimination of fintech risks to achieve sustainable quality improvement. Wirel. Person. Comm.
**2020**, 115, 3199–3214. [Google Scholar] [CrossRef] - Saraji, M.K.; Streimikiene, D.L.; Kyriakopoulos, G.L. Fermatean fuzzy critic-copras method for evaluating the challenges to Industry 4.0 adoption for a sustainable digital transformation. Sustainability
**2021**, 13, 9577. [Google Scholar] [CrossRef] - Yu, C.; Wang, X. Empirical test of financial innovation on high-quality economic development. Stat. Decis. Making.
**2021**, 37, 88–92. [Google Scholar] - Lin, J.; Zhao, H. Research on risk spillover effect of Shanghai, Shenzhen and Hong Kong Stock Markets—Based on time-varying ΔCoVaR Model. Sys. Eng. Theory Pract.
**2000**, 40, 1533–1544. [Google Scholar] - Karimalis, E.N.; Nokimos, N.K. Measuring systemic risk in the european banking sector: A copula Co-VaR approach. Eur. J. Financ.
**2018**, 24, 944–975. [Google Scholar] [CrossRef] [Green Version] - Joe, H.; Li, H.J.; Nikoloulopoulos, A.K. Tail dependence Functions and Vine Copulas. J. Multivariat. Anal.
**2010**, 101, 252–270. [Google Scholar] [CrossRef] [Green Version] - Sriboonchitta, S.; Kosheleva, O.; Nguyen, H.T. Why are vine copulas so successful in econometrics? Int. J. Uncertain Fuzz.
**2015**, 23, 133–142. [Google Scholar] [CrossRef] - Alanazi, F.A. A mixture of regular vines for multiple dependencies. J. Prob. Stat.
**2021**, 5559518, 1–15. [Google Scholar] [CrossRef] - Bedford, T.; Daneshkhah, A.; Wilson, K.J. Approximate uncertainty modeling in risk analysis with vine copulas. Risk Anal.
**2016**, 36, 792–815. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Zhu, K.L.; Kurowicka, D.; Nane, G.F. Common sampling orders of regular vines with application to model selection. Comput. Stat. Data Anal.
**2020**, 142, 106811. [Google Scholar] [CrossRef] - Schepsmeier, U. A goodness-of-fit test for regular vine copula models. Econom. Rev.
**2019**, 38, 25–46. [Google Scholar] [CrossRef] [Green Version] - Kim, D.; Kim, J.M.; Liao, S.M.; Jung, Y.S. Mixture of D-vine copulas for modeling dependence. Comput. Stat. Data Anal.
**2013**, 64, 1–19. [Google Scholar] [CrossRef] - Nikoloulopoulos, A.K.; Joe, H.; Li, H. Vine copulas with asymmetric tail dependence and applications to financial return data. Comput. Stat. Data Anal.
**2012**, 56, 3659–3673. [Google Scholar] [CrossRef] - Karakas, A.M.; Demir, A.; Calik, S. Vine copula approach for modeling dependence of commodity and stock markets. J. Stat. Manag. Sys.
**2022**, 25, 1877904. [Google Scholar] - Autchariyapanitkul, K.; Piamsuwannakit, S.; Chanaim, S.; Sriboonchitta, S. Optimizing stock returns portfolio using the dependence structure between capital asset pricing models: A vine copula-based approach. Causal Infer. Econom.
**2016**, 622, 319–331. [Google Scholar] - Czado, C.; Schepsmeier, U.; Min, A. Maximum likelihood estimation of mixed C-vines with application to exchange rates. Stat. Model.
**2012**, 12, 229–255. [Google Scholar] [CrossRef] [Green Version] - Zhang, Z.; Zhang, T. Risk correlation measurement of major global stock markets—Based on semi Parametric C-Vine Copula Model. Financ. Rev.
**2018**, 10, 23–34. [Google Scholar] - Reboredo, J.C.; Ugolini, A. Downside/upside price spillovers between precious metals: A vine copula approach. N. Am. J. Econ. Financ.
**2015**, 34, 84–102. [Google Scholar] [CrossRef] - Guo, W. Structural deleveraging and systemic risk spillovers of financial institutions: Promotion or inhibition? J. Centr. Univ. Financ. Econ.
**2020**, 40, 26–41. [Google Scholar] - Zhang, D.L.; Yan, M.L.; Tsopanakis, A. Financial stress relationships among euro area countries: An R-vine copula approach. Eur. J. Financ.
**2018**, 24, 1587–1608. [Google Scholar] [CrossRef] - Heston, S.L.; Nandi, S. A Closed-form GARCH option valuation model. Rev. Financ. Stud.
**2000**, 13, 585–625. [Google Scholar] [CrossRef] - Ji, H.; Wang, H.; Liseo, B. Portfolio diversification strategy via tail-dependence clustering and ARMA-GARCH vine copula approach. Aust. Econ. Pap.
**2018**, 57, 265–283. [Google Scholar] [CrossRef] - Patton, A.J. Modeling asymmetric exchange rate dependence. Int. Econ. Rev.
**2006**, 47, 527–556. [Google Scholar] [CrossRef] - Sklar, A. Fonctions de repartition an dimensions et Leurs Marges. Publ. Inst. Stat. Univ. Paris
**1959**, 8, 229–231. [Google Scholar] - Hernandez, J.A.; Hammoudeh, S.; Nguyen, D.K. Global financial crisis and dependence risk analysis of sector portfolios: A vine copula approach. Appl. Econ.
**2017**, 49, 2409–2427. [Google Scholar] [CrossRef] [Green Version] - He, M.; Li, H. Dependence structure and extreme risk spillover among global stock markets: Financial complexity analysis based on Rattan Copula. Manag. Rev.
**2020**, 32, 102–110. [Google Scholar] - Dißmann, J.; Brechmann, E.C.; Czado, C. Selecting and estimating regular vine copula and application to financial returns. Comput. Stat. Data Anal.
**2013**, 59, 52–69. [Google Scholar] [CrossRef] [Green Version] - Brechmann, E.C.; Schepsmeier, U. Modeling dependence with C- and D-vine copulas: The R-package CD vine. J. Stat. Softw.
**2013**, 52, 1–27. [Google Scholar] [CrossRef] [Green Version] - Jiang, K. Research on financial risk spillover and its prevention in real estate industry—Analysis based on time-varying copula CoVaR model. Price Theory Pract.
**2020**, 40, 87–91. [Google Scholar]

Variable | Min | Max | Mean | Stdev | Skew | Kurtosis | J-B Test | ADF | Stable |
---|---|---|---|---|---|---|---|---|---|

Fintech | −9.41 | 7.57 | 0.00 | 1.64 | −0.41 | 6.75 | 450.28 *** | −26.32 * | Yes |

Real Econ. | −8.04 | 6.10 | 0.02 | 1.47 | −0.40 | 6.14 | 478.13 *** | −26.09 * | Yes |

Energy | −8.05 | 6.07 | −0.04 | 1.39 | −0.44 | 6.68 | 437.33 *** | −26.37 * | Yes |

Material | −9.51 | 5.65 | 0.01 | 1.54 | −0.57 | 6.69 | 455.71 *** | −26.37 * | Yes |

Selective consumer | −9.34 | 4.73 | 0.04 | 1.52 | −0.62 | 6.02 | 324.89 *** | −26.54 * | Yes |

Consumer goods | −8.17 | 5.82 | 0.02 | 1.72 | −0.31 | 5.20 | 160.24 *** | −27.23 * | Yes |

IT | −9.84 | 6.58 | 0.04 | 2.06 | −0.40 | 4.79 | 117.45 *** | −26.84 * | Yes |

Medical | −7.05 | 4.78 | 0.05 | 1.63 | −0.28 | 3.76 | 27.33 *** | −26.94 * | Yes |

Telecom | −10.20 | 6.77 | −0.04 | 2.06 | −0.33 | 5.90 | 270.09 *** | −25.91 * | Yes |

Public Uti. | −8.06 | 4.05 | −0.02 | 1.01 | −0.95 | 10.59 | 1867.48 *** | −28.08 * | Yes |

Manufact. | −9.54 | 5.09 | 0.02 | 1.40 | −0.66 | 8.09 | 843.01 *** | −26.79 * | Yes |

Null Hypothesis | Lags | F-Statistic | Prob. | Result |
---|---|---|---|---|

Fintech does not Granger cause RE | 1 | 6.7582 | 0.0094 * | Reject |

RE does not Granger cause Fintech | 1 | 9.1337 | 0.0025 * | Reject |

Fintech does not Granger cause RE | 2 | 4.3756 | 0.0127 ** | Reject |

RE does not Granger cause Fintech | 2 | 5.2415 | 0.0054 * | Reject |

Fintech does not Granger cause RE | 3 | 7.1633 | 9 × 10^{−5} * | Reject |

RE does not Granger cause Fintech | 3 | 4.2180 | 0.0055 * | Reject |

Fintech does not Granger cause RE | 4 | 6.2489 | 5 × 10^{−5} * | Reject |

RE does not Granger cause Fintech | 4 | 3.2788 | 0.0109 ** | Reject |

Fintech does not Granger cause RE | 5 | 5.0928 | 0.0001 * | Reject |

RE does not Granger cause Fintech | 5 | 3.0986 | 0.0086 * | Reject |

Fintech does not Granger cause RE | 6 | 5.1886 | 3 × 10^{−5} * | Reject |

RE does not Granger cause Fintech | 6 | 2.7154 | 0.0125 ** | Reject |

Fintech does not Granger cause RE | 7 | 5.5768 | 2 × 10^{−6} * | Reject |

RE does not Granger cause Fintech | 7 | 2.36461 | 0.0208 ** | Reject |

Fintech does not Granger cause RE | 8 | 4.7075 | 1 × 10^{−5} * | Reject |

RE does not Granger cause Fintech | 8 | 1.9797 | 0.0453 ** | Reject |

Variable | $\mathbf{Q}\left(6\right)$ | ${\mathbf{Q}}^{2}\left(6\right)$ | $\mathbf{Q}\left(36\right)$ | ${\mathbf{Q}}^{2}\left(36\right)$ |
---|---|---|---|---|

Fintech | 13.028 ** | 7.043 | 42.082 ** | 36.218 |

Real Economy | 7.331 | 19.652 *** | 36.149 | 51.165 ** |

Energy | 8.072 | 24.140 *** | 22.969 | 36.025 |

Material | 9.659 *** | 13.930 ** | 41.542 *** | 53.176 ** |

Selective consumer | 8.214 | 19.576 *** | 29.039 | 52.037 |

Consumer goods | 4.235 | 31.335 *** | 28.525 | 59.509 *** |

IT | 7.812 | 15.009 ** | 41.590 | 58.534 *** |

Medicine | 0.538 | 43.541 *** | 23.969 | 87.534 *** |

Telecom | 4.854 | 15.760 ** | 53.274 ** | 55.960 ** |

Public utilities | 11.184 * | 4.336 | 49.727 * | 10.268 |

Manufacture | 11.412 * | 9.247 | 34.707 | 47.446 * |

^{2}(k) represent whether the auto-correlation coefficients of return series and return square series lag 1–36 orders are combined to 0, respectively. *, **, *** denote significant at the confidence level 1%, 5% and 10%, respectively.

Variable | ARCH-LM(2) | ARCH-LM(4) |
---|---|---|

Fintech | 3.892 *** | 2.922 *** |

Real Economy | 19.122 *** | 11.826 *** |

Energy | 8.122 *** | 6.386 *** |

Material | 24.558 *** | 12.663 *** |

Selective consumer | 68.744 *** | 35.605 *** |

Consumer goods | 19.916 *** | 15.528 *** |

IT | 15.637 *** | 8.132 *** |

Medicine | 13.529 *** | 13.879 *** |

Telecom | 9.791 *** | 6.852 *** |

Public Utilities | 4.458 *** | 3.394 *** |

Manufacture | 6.66 *** | 4.054 *** |

Variable | ${\mathsf{\alpha}}_{0}$ | ${\mathsf{\alpha}}_{1}$ | ${\mathsf{\beta}}_{1}$ | ${\mathsf{\alpha}}_{\mathbf{1}}$$\mathbf{+}{\mathsf{\beta}}_{\mathbf{1}}$ |
---|---|---|---|---|

Fintech | 0.123 * | 0.05 ** | 0.911 *** | 0.961 |

Energy | 0.153 * | 0.059 ** | 0.869 *** | 0.928 |

Material | 0.11 ** | 0.073 *** | 0.888 *** | 0.961 |

Selective consumer | 0.125 ** | 0.068 ** | 0.883 *** | 0.951 |

Consumer goods | 0.172 * | 0.06 ** | 0.884 *** | 0.944 |

IT | 0.219 * | 0.05 ** | 0.9 *** | 0.95 |

Medicine | 0.077 ** | 0.054 *** | 0.919 *** | 0.973 |

Telecom | 0.148 ** | 0.056 *** | 0.915 *** | 0.971 |

Public Utilities | 0.038 ** | 0.047 ** | 0.917 *** | 0.964 |

Manufacture Ind. | 0.092 ** | 0.054 ** | 0.903 *** | 0.957 |

R-Vine | Tree Structure | Type | Par | Par2 | Kendall’s Tau | ${\mathsf{\lambda}}_{\mathbf{U}}$ | ${\mathsf{\lambda}}_{\mathbf{L}}$ |
---|---|---|---|---|---|---|---|

First tree | Ma,EN | t | 0.80 | 6.20 | 0.60 | 0.41 | 0.41 |

FC,Pub | t | 0.79 | 3.04 | 0.58 | 0.52 | 0.52 | |

Inf,Tele | t | 0.89 | 6.26 | 0.70 | 0.53 | 0.53 | |

Ind,Inf | t | 0.87 | 8.00 | 0.67 | 0.45 | 0.45 | |

FC,Ma | t | 0.88 | 3.92 | 0.69 | 0.60 | 0.60 | |

FC,Ind | t | 0.90 | 4.02 | 0.71 | 0.63 | 0.63 | |

FC,SE | t | 0.87 | 6.50 | 0.67 | 0.49 | 0.49 | |

SE,Co | t | 0.78 | 12.12 | 0.57 | 0.23 | 0.23 | |

Co,Me | t | 0.75 | 9.66 | 0.54 | 0.24 | 0.24 | |

Second tree | FC,EN|Ma | SG | 1.12 | 0.00 | 0.10 | - | 0.14 |

Ind,Pub|FC | SJ | 1.21 | 0.00 | 0.11 | - | 0.23 | |

Ind,Tele|Inf | SJ | 1.20 | 0.00 | 0.10 | - | 0.22 | |

FC,Inf|Ind | t | 0.25 | 6.79 | 0.16 | 0.06 | 0.06 | |

Ind,Ma|FC | SG | 1.17 | 0.00 | 0.15 | - | 0.19 | |

SE,Ind|FC | SG | 1.18 | 0.00 | 0.15 | - | 0.20 | |

Co,FC|SE | SC | 0.08 | 0.00 | 0.04 | 0.00 | - | |

Me,SE|Co | F | 2.52 | 0.00 | 0.26 | - | - | |

Third tree | Ind,EN|FC,Ma | SG | 1.06 | 0.00 | 0.05 | - | 0.07 |

Inf,Pub|Ind,FC | N | −0.16 | 0.00 | −0.10 | - | - | |

FC,Tele|Ind,Inf | SJ | 1.09 | 0.00 | 0.05 | - | 0.11 | |

Ma,Inf|FC,Ind | G270 | −1.13 | 0.00 | −0.11 | - | - | |

SE,Ma|Ind,FC | t | 0.09 | 10.37 | 0.06 | 0.01 | 0.01 | |

Co,Ind|SE,FC | J270 | −1.02 | 0.00 | −0.01 | - | - | |

Me,FC|Co,SE | F | 1.23 | 0.00 | 0.13 | - | - | |

Fourth tree | Inf,EN|Ind,FC,Ma | t | −0.28 | 7.29 | −0.18 | 0.00 | 0.00 |

Ma,Pub|Inf,Ind,FC | SC | 0.08 | 0.00 | 0.04 | 0.00 | - | |

Ma,Tele|FC,Ind,Inf | C | 0.00 | 0.00 | 0.05 | - | 0.00 | |

SE,Inf|Ma,FC,Ind | C | 0.08 | 0.00 | 0.04 | - | 0.00 | |

Co,Ma|SE,Ind,FC | SC | 0.10 | 0.00 | 0.05 | 0.00 | - | |

Me,Ind|Co,SE,FC | F | 0.51 | 0.00 | 0.06 | - | - |

Risk Spillover | VaR | CoVaR | $\mathsf{\Delta}\mathbf{CoVaR}$ | $\mathbf{\%}\mathsf{\Delta}\mathbf{CoVaR}$ |
---|---|---|---|---|

Fintech → Energy | 3.68 | 4.42 | 3.25 | 88.09% |

Fintech → Material | 3.48 | 4.08 | 3.00 | 86.23% |

Fintech → Selective consume | 3.58 | 4.21 | 2.25 | 62.87% |

Fintech → Consume | 3.09 | 3.86 | 3.08 | 99.53% |

Fintech → IT | 3.30 | 4.15 | 3.45 | 104.79% |

Fintech → Medicine | 3.66 | 4.34 | 3.49 | 95.31% |

Fintech → Telecom | 4.24 | 5.10 | 3.57 | 84.05% |

Fintech → Public Uti. | 3.52 | 4.17 | 2.55 | 72.51% |

Fintech → Manufacture | 4.19 | 4.98 | 3.52 | 83.96% |

Risk Spillover | VaR | CoVaR | $\mathsf{\Delta}\mathrm{CoVaR}$ | $\mathbf{\%}\mathsf{\Delta}\mathrm{CoVaR}$ |
---|---|---|---|---|

Energy → Fintech | 3.04 | 3.94 | 2.04 | 67.20% |

Material → Fintech | 3.07 | 3.96 | 2.48 | 80.71% |

Selective consumer → Fintech | 4.29 | 5.50 | 4.25 | 99.06% |

Consumer → Fintech | 2.73 | 3.28 | 2.14 | 78.45% |

IT → Fintech | 2.31 | 2.83 | 1.31 | 56.78% |

Medicine → Fintech | 3.11 | 4.11 | 2.45 | 78.73% |

Telecom → Fintech | 3.55 | 4.79 | 2.44 | 68.83% |

Pub Uti. → Fintech | 3.96 | 5.13 | 4.08 | 103.08% |

Manuf. Ind. → Fintech | 3.62 | 4.91 | 2.59 | 71.52% |

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**MDPI and ACS Style**

Peng, Z.; Ke, J.
Spillover Effect of the Interaction between Fintech and the Real Economy Based on Tail Risk Dependent Structure Analysis. *Sustainability* **2022**, *14*, 7818.
https://doi.org/10.3390/su14137818

**AMA Style**

Peng Z, Ke J.
Spillover Effect of the Interaction between Fintech and the Real Economy Based on Tail Risk Dependent Structure Analysis. *Sustainability*. 2022; 14(13):7818.
https://doi.org/10.3390/su14137818

**Chicago/Turabian Style**

Peng, Zhikai, and Jinchuan Ke.
2022. "Spillover Effect of the Interaction between Fintech and the Real Economy Based on Tail Risk Dependent Structure Analysis" *Sustainability* 14, no. 13: 7818.
https://doi.org/10.3390/su14137818