Research on the Dependence Structure and Risk Spillover of Internet Money Funds Based on C‐Vine Copula and Time‐Varying t‐Copula

Ma, Huizi; LinLin,; SunHan,; QuYue,

doi:10.1155/2021/3941648

Cited by 7 publications

(11 citation statements)

References 31 publications

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“…The application of liquidity risk results in portfolio research also needs further exploration. Besides, the literature [1][2][3][4][5][6][7][25][26][27] already proved the necessity of portfolio design, but whether the portfolio design of different IMFs can also follow similar portfolio ideas still needs further proof.…”

Section: Literature Reviewmentioning

confidence: 99%

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Optimal Investment Portfolios for Internet Money Funds Based on LSTM and La-VaR: Evidence from China

Wang

2022

Mathematics

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The rapid development of Internet finance has impacted traditional investment patterns, and Internet money funds (IMFs) are involved extensively in finance. This research constructed a long short-term memory (LSTM) neural network model to predict the return rates of IMFs and utilized the value-at-risk (VaR) and liquidity-adjusted VaR (La-VaR) methods to measure the IMFs’ risk. Then, an objective programming model based on prediction and risk assessment was established to design optimal portfolios. The results indicate the following: (1) The LSTM model results show that the forecast curves are consistent with the actual curves, and the root-mean-squared error (RMSE) result is mere 0.009, indicating that the model is suitable for forecasting data with reliable time-periodic characteristics. (2) With unit liquidity cost, the La-VaR results match the actuality better than the VaR as they demonstrate that the fund-based IMFs (FUND) have the most significant risk, the bank-based IMFs (BANK) rank 2nd, and the third-party-based IMFs (THIRD) rank 3rd. (3) The programming model based on LSTM and the La-VaR can meet different investors’ preferences by adjusting the objectives and constraints. It shows that the designed models have more practical significance than the traditional investment strategies.

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Section: Literature Reviewmentioning

confidence: 99%

“…IMFs are the money market funds released by fund companies on Internet platforms and gather the idle funds of individual investors and then invest them with fund management companies (Ma et al (2021) [1]). IMFs have the advantages of convenience and reasonable return rates, attracting a wide range of investors.…”

Section: Introductionmentioning

confidence: 99%

Optimal Investment Portfolios for Internet Money Funds Based on LSTM and La-VaR: Evidence from China

Wang

2022

Mathematics

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“…In literature [10] from the analysis of the loan data of users on the credit platform, it is found that online credit is superior to traditional credit methods in cost control and the benefits it brings are higher than those of traditional credit business, so it is of practical significance to promote online credit. Literatures [11,12] adopt linear support vector machine classifier, combine genetic algorithm and particle swarm optimization to select parameters, and use discriminant analysis to identify the bankruptcy grade of bank customers. The research shows that this method can better identify bankrupt customers than the original model.…”

Section: Introductionmentioning

confidence: 99%

Research on the Construction of Financial Supervision Information System Based on Machine Learning

Lin

Shangle

Zhao

et al. 2022

Wireless Communications and Mobile Computing

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In order to fully implement systematic, continuous and effective supervision of financial institutions and promote the safe, steady, and efficient operation of China’s financial system, this research needs to develop a fully intelligent financial supervision information system, so as to take measures to effectively prevent and resolve financial risks. In this paper, based on ML (machine learning), an LSTM (long short-term memory) model with good comprehensive performance is built. This model is different from the existing scorecard model which relies on statistical learning. It not only further reduces the dependence on financial experts, but also has the ability of rapid iteration. The post-loan risk early warning model based on RF (random forest) algorithm is designed. The model parameters are optimized, which makes the risk early warning model have higher accuracy. The results show that when the data amount is 10,000 to 50,000, the accuracy of the model is relatively high when it is input into the model. When the amount of data is low, the overall throughput of the model is still very high. It shows that the pre-warning model of post-lending risk constructed in this paper has a strong risk prediction ability.

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“…Internet money market funds (IMMFs), also known as Internet money funds, are Internet financial products with the broadest social participation. IMMFs usually gather idle funds of individual investors to obtain profit with the help of internet wealth management platforms [1][2] and have long been lucrative for shareholders and fund firms. Take the largest IMMF in China, Yu'E Bao, as an example; its scale was up to about 764.578 billion Yuan (approximately 120.19 billion dollars) as of the end of the third quarter of 2021.…”

Section: Introductionmentioning

confidence: 99%

Risk of Internet Money Market Funds and Its Spillover Effect: Based on La-VaR, DCC-GARCH and Minimum Spanning Tree

Huizi¹,

Huizi²,

Wang³

et al. 2022

Journal of Internet Technology

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Internet money market funds (IMMFs) are China’s most wildly participated Internet financial products. This research mainly focused on the liquidity risk of IMMFs by establishing a La-VaR model with the cost of unit liquidity and further discussed the liquidity risk spillover between different IMMFs with La-VaR and minimum spanning tree algorithm. The results show the following: (a) The proposed La-VaR model is superior to the conventional VaR in evaluating the liquidity risk of IMMFs. The case study on Yu’E Bao also proves its superiority. (b) IMMFs with greater yield volatility face more significant liquidity risk pressure. (c) Risk spillover effects exist in IMMFs, and IMMFs with an extensive fund scale are more likely to spread liquidity risk to the entire market.

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Research on the Dependence Structure and Risk Spillover of Internet Money Funds Based on C‐Vine Copula and Time‐Varying t‐Copula

Cited by 7 publications

References 31 publications

Optimal Investment Portfolios for Internet Money Funds Based on LSTM and La-VaR: Evidence from China

Optimal Investment Portfolios for Internet Money Funds Based on LSTM and La-VaR: Evidence from China

Research on the Construction of Financial Supervision Information System Based on Machine Learning

Risk of Internet Money Market Funds and Its Spillover Effect: Based on La-VaR, DCC-GARCH and Minimum Spanning Tree

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