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Big data, big challenges: risk management of financial market in the digital economy

Big data, big challenges: risk management of financial market in the digital economy

Yang, Jinlei, Zhao, Yuanjun, Han, Chunjia, Yanghui, Liu and Yang, Mu (2021) Big data, big challenges: risk management of financial market in the digital economy. Journal of Enterprise Information Management. ISSN 1741-0398 (In Press) (doi:https://doi.org/10.1108/JEIM-01-2021-0057)

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Abstract

Purpose– The purpose of the research is to assess the risk of the financial market in the digital economy through the quantitative analysis model in the big data era. It’s a big challenge for the government to carry out financial market risk management in the big data era.

Design/methodology/approach– In this study, a generalized autoregressive conditional heteroskedasticity-vector autoregression (GARCH-VaR) model is constructed to analyze the big data financial market in the digital economy. Additionally, the correlation test and stationarity test are carried out to construct the best fit model and get the corresponding VaR value.

Findings– Owing to the conditional heteroscedasticity, the index return series shows the leptokurtic and fat tail phenomenon. According to the AIC (Akaike Information Criterion), the fitting degree of the GARCH model is measured. The AIC value difference of the models under the three distributions is not obvious, and the differences between them can be ignored.

Originality/value– Using the GARCH-VaR model can better measure and predict the risk of the big data finance market and provide a reliable and quantitative basis for the current technology-driven regulation in the digital economy.

Item Type: Article
Uncontrolled Keywords: big data, digital economy, risk management, financial market, GARCH model, GARCH-VaR model
Faculty / School / Research Group: Faculty of Business
Faculty of Business > Department of Systems Management & Strategy
Faculty of Business > Networks and Urban Systems Centre (NUSC)
Faculty of Business > Networks and Urban Systems Centre (NUSC) > Connected Cities Research Group
Last Modified: 09 Sep 2021 13:27
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None
URI: http://gala.gre.ac.uk/id/eprint/33811

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