The role of business analytics in strategic management of financial derivatives for risk hedging

Abstract

The relevance of the study is determined by the growing need to enhance the accuracy of financial risk forecasting and the quality of strategic decision-making in derivative management, particularly amid increasing market volatility and the rapid digitalisation of analytical processes. The purpose of this article is to clarify the role of business analytics in supporting strategic decisions regarding derivative instruments, thereby enhancing hedging performance and ensuring financial stability. Methods. System analysis is applied to identify structural relations between analytical technologies and risk-management processes; the comparative approach is used to evaluate the effectiveness of VaR, ES, GARCH and neural-network models; modelling is employed to assess the impact of analytics on hedging outcomes; generalisation is used to formulate practical recommendations for improving data-driven strategic decisions. Results. It has been established that applying GARCH models improves the accuracy of short-term volatility forecasts relative to static risk-assessment tools, thereby enhancing hedge-ratio calibration. Neural network architectures (LSTM, CNN) improve the detection of price anomalies and tail-risk events, thereby strengthening the performance of option-based hedging strategies. The integration of cloud-based streaming analytics (Azure Synapse, AWS Athena) reduces the time to update risk indicators from hours to seconds, enabling real-time portfolio rebalancing. The combined use of VaR and ES modules with machine-learning algorithms reduces risk-estimation errors and optimises capital allocation for risky positions. Organisational and technological barriers have been identified, including data incompatibility across trading platforms, the absence of unified API standards, insufficient automation of risk-modelling processes, and misalignment between national regulations and EU requirements (EMIR, MiFID II). Conclusions. Recommendations include implementing a Data Governance Framework to reduce data inconsistencies and enhance model reliability; expanding the use of ML- and NN-based forecasting to improve volatility prediction and hedging efficiency; and harmonising national regulations with EU directives, thereby reducing compliance costs, increasing transparency, and facilitating access to international financial markets. Prospects for further research involve developing a methodology for digital dynamic hedging based on streaming analytics and evaluating the effectiveness of integrated risk analytics systems in the corporate and banking sectors.