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

Abstract

PurposeThe 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/approachIn 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.FindingsOwing 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/valueUsing 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.

The digital economy, enterprise digital transformation, and enterprise innovation

“…Digital economy has garnered substantial attention from scholars in recent years; they often focus on macrolevel outcomes, such as risk in the financial market (Yang et al, 2021), high‐quality development (Zhao et al, 2020), and economic growth (Bulturbayevich & Jurayevich, 2020). Digital economy brings both opportunities and challenges to enterprise, how to improve innovation performance and keep competitive is critical to enterprise performance in the digital age.…”

The trickle-down effect of big data use to predict organization innovation: the roles of business strategy alignment and information sharing

“…The advent of "Big Data" has transformed the firms' working styles (Peng et al, 2022;Contreras Pinochet et al, 2021) and enforces a firm's top management to bring changes in their production system to capture a competitive advantage in the highly globalized markets (Aydiner et al, 2019). Yang et al (2022) highlighted that big challenges using big data can be resolved to minimize the financial risk in the digital economy. Therefore, the use of big data to bring strategic change in business processes has become an emerging and exciting topic among managers and data scientists (Narwane et al, 2021;Aydiner et al, 2019), because BDU can promise 4-6% higher productivity in the business process (M€ uller et al, 2018).…”

“…, 2022; Contreras Pinochet et al., 2021) and enforces a firm's top management to bring changes in their production system to capture a competitive advantage in the highly globalized markets (Aydiner et al ., 2019). Yang et al. (2022) highlighted that big challenges using big data can be resolved to minimize the financial risk in the digital economy.…”

Guest editorial: Editorial for special issue on the role of big data on the transition to circular economy and sustainable operations management

“…Times series and regression methods are other topics that we published some high-quality papers (Miao, 2021; Xie et al , 2021; Yang et al , 2021a; Yaoteng and Xin, 2021; Duan et al , 2022). For example, Zhao and Chen (2021) examined the role of an effective government and an effective market in preventing and controlling the withdrawal of innovators.…”