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

    Yang, J. and Zhao, Y. and Han, Chunjia and Liu, Y. and Yang, M. (2022) Big data, big challenges: risk management of financial market in the digital economy. Journal of Enterprise Information Management 35 (4/5), pp. 1288-1304. ISSN 1741-0398. (In Press)

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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 is 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.

    Metadata

    Item Type: Article
    School: School of Business, Economics & Informatics > Management
    Depositing User: Chunjia Han
    Date Deposited: 07 Mar 2022 12:30
    Last Modified: 24 Jun 2022 18:28
    URI: https://eprints.bbk.ac.uk/id/eprint/47576

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