Econometric study reveals a predictive financial risk assessment framework for virtual currency in IT enterprises, highlighting pathways to forecast risk from volatile digital assets.
Virtual currency is a consequence of deep digitalization, which in turn has significantly affected the market economy and introduced new types of payments. In essence, it is a kind of digital currency that is used and stored exclusively in electronic form. This includes various types, but the most famous is of course Bitcoin. This has led to the fact that the private sector has also begun to use it, including enterprises. Virtual currencies continue to develop and gain popularity but are also accompanied by issues of regulation, security and volatility. One of the most significant problems is the issue of financial risks that arise when using it. Of course, it is IT companies that most often use virtual currencies today to develop and implement new digital products and services, for example, in the field of blockchain technologies or digital payment systems. Therefore, they will be the object of our research. The purpose of the article is to present our own method of assessing financial risks and justify its effectiveness in comparison with the already established ones. At the same time, the proposed approach takes into account the specifics of the use of virtual currency by enterprises. As a result, a correlation-regression analysis of the influence of individual factors on the level of generalizing indicators of retrospective assessment of the riskiness of using virtual currency for the enterprises we selected is presented. Using the data presented in the article, management can predict changes in generalizing indicators of retrospective assessment of the riskiness of using virtual currency depending on changes in the relevant indicators-factors.
No takes yet. Share an insight, caveat, or question.
Bykov et al. (2025) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: