Linkages and Spillovers between Internet Finance and Traditional Finance: Evidence from China

Chen, Rongda; Chen, Huiwen; Jin, Chenglu; Wei, Bo; Yu, Lean

doi:10.1080/1540496x.2019.1658069

Cited by 29 publications

(20 citation statements)

References 41 publications

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“…The literature [20] studies the degree of risk spillovers between the Internet and traditional financial industries using the CoVaR model. Their study shows that the risk of internet finance is more likely to spillover to the banking industry followed by the insurance industry and, lastly, the securities industry.…”

Section: Discussionmentioning

confidence: 99%

“…Different from previous literature [9,20,35], we studied the influence of internet finance ecological subject on the sustainable development of financial ecosystem from empirical perspectives. The risk transmission effect between the traditional financial ecological subjects has been studied [19], but the influence of internet finance on the financial ecosystem of the traditional financial industries has not been studied.…”

Section: Discussionmentioning

confidence: 99%

“…Although there are several studies on the spillover effect among the ecological subjects of traditional finance markets [16][17][18][19], there are only a few studies on the dynamic correlation and risk transmission effects between the ecological subjects of internet finance and traditional finance. Only Chen et al [20] used the conditional value at risk (CoVaR) to measure the degree of spillover when internet finance is in extreme risk. This study shows that the risk of internet finance is more likely to spillover to the banking industry, followed by the insurance industry, and, lastly, the securities industry.…”

Section: Literature Reviewmentioning

confidence: 99%

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The Influence of Internet Finance on the Sustainable Development of the Financial Ecosystem in China

Liu

Wang

2020

Sustainability

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As a new species in the financial ecosystem, internet finance has significantly impacted traditional finance and has improved the diversity and ended the long-term stability of the financial ecosystem. From the perspective of the interaction between the ecological subjects of the Internet and traditional finance, this study examines the influence of internet finance on the sustainability of the financial ecosystem in China. We tested the dynamic correlation and risk transmission at the volatility level between the ecological subjects of internet finance and the banking, securities, and insurance industries by establishing a dynamic conditional correlation-generalized autoregressive conditional heteroskedasticity (GARCH) model of Baba, Engle, Kraft, and Kroner (DCC-GARCH-BEKK). The result indicates a positive dynamic correlation between internet finance and traditional finance almost all of the time. The introduction of internet finance has changed the risk transmission effect among the ecological subjects of traditional finance. Based on empirical findings, this study provides suggestions to promote the sustainable development of internet finance and the whole financial ecosystem. Our research not only has strong practical significance but also contributes significantly to the literature on internet finance and sustainable development.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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The Influence of Internet Finance on the Sustainable Development of the Financial Ecosystem in China

Liu

Wang

2020

Sustainability

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“…The classification of a new sample can be predicted with the voting method for judgment [23]. Logistic regression model to classify data requires mastering the Sigmoid function [24], which can be expressed as equation (1).…”

Section: B Data Recognition and Mining Methods Of Internet Finance Based On Big Data Technologymentioning

confidence: 99%

“…With the continuous development of the third technological revolution, the Internet has gradually integrated into various fields. The emergence of Internet finance has brought about tremendous changes in the boundaries of the financial industry [1,2]. In recent years, the network credit finance has developed rapidly, which reflects that the financial industry has great potential in combination with the Internet, can break the traditional time and geographical constraints, and shows the freedom of finance [3].…”

Section: Introductionmentioning

confidence: 99%

Innovative Risk Early Warning Model Based on Internet of Things Under Big Data Technology

Liu

2021

IEEE Access

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An innovative financial risk early warning model based on the Internet of Things (IoT) big data technology is proposed to maintain the long-term stable development of Internet finance. The Internet credit finance is introduced, a financial risk identification method based on the big data technology is proposed, and a risk assessment method for Internet finance based on back propagation neural network (BPNN) is put forwarded in this study. The actual Internet financial data is selected for verification and analysis. The results reveal that the identification and prediction model of Internet credit finance risk based on big data technology can realize risk analysis of online credit in the Internet credit finance, and can give corresponding credit ratings to new customers on credit platforms. The training error is the lowest when the number of hidden layer nodes is 14; and the training error is the smallest when the learning rate is 0.06; Based on the BPNN, it can accurately assess the financial risks of the Internet credit platform, and the accuracy of its risk level prediction has reached 100%. This study can provide a theoretical basis for the application of IoT big data technology and neural network models in the financial field, and also give an important reference for the risk management of Internet finance.

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Value‐at‐Risk with quantile regression neural network: New evidence from internet finance firms

Zeng,

Lau,

Abdul Bahri

2023

Appl Stoch Models Bus & Ind

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Traditional risk measurements have proven inadequate in capturing tail risk and nonlinear correlation. This study proposes a novel approach to measure financial risk in the Internet finance industry: a new Value‐at‐Risk (VaR) measurement based on quantile regression neural network (QRNN). Sparrow Search Algorithm (SSA) is utilized to optimize the QRNN model, which improves the model's performance in predicting internet finance risk. By comparing the TGARCH‐VaR and QR‐VaR approaches, our study demonstrates the effectiveness of the QRNN‐VaR approach and its potential to improve the accuracy of risk prediction in the Internet finance industry. This study further examines and compares the risks between the traditional and internet finance industries. It also considers the unique impact of COVID‐19 on industry risk based on statistical testing for differences and machine learning models. Our results indicate that the level of risk in the Internet finance industry is higher than in the traditional finance industry. Moreover, COVID‐19 has contributed to increased risk within the Internet finance industry. These findings have significant implications for investors and policymakers seeking to better understand and manage risks within the Internet finance industry, particularly in the ongoing COVID‐19 pandemic.

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Linkages and Spillovers between Internet Finance and Traditional Finance: Evidence from China

Cited by 29 publications

References 41 publications

The Influence of Internet Finance on the Sustainable Development of the Financial Ecosystem in China

The Influence of Internet Finance on the Sustainable Development of the Financial Ecosystem in China

Innovative Risk Early Warning Model Based on Internet of Things Under Big Data Technology

Value‐at‐Risk with quantile regression neural network: New evidence from internet finance firms

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