Open Access
A Dynamic Nexus: Integrating Big Data Analytics and Distributed Computing for Real-Time Risk Management of Derivatives Portfolios
4
Faculty of Economics and Business, Universitas Gadjah Mada, Yogyakarta, Indonesia
4
Associate Professor, Department of Information Systems Bandung Institute of Technology,Bandung, Indonesia
Abstract
The growing complexity and velocity of derivatives markets demand risk management systems capable of processing massive, high-frequency data streams and responding to rapidly evolving exposures. This paper presents a critical review and conceptual framework for integrating Big Data analytics with distributed computing architectures to enable real-time risk management of derivatives portfolios. We analyze current practices in market and credit risk computation, highlighting limitations in traditional centralized infrastructures, including latency bottlenecks, computational inefficiencies, and delayed visibility into systemic risk signals. Emerging technologies — such as in-memory distributed clusters, event-driven streaming pipelines, and scalable machine learning models — are examined for their potential to accelerate valuation adjustments, margin calculations, and stress testing under volatile market conditions. We propose an architecture that leverages heterogeneous data sources, parallelized pricing engines, and continuous predictive analytics to support dynamic hedging decisions and regulatory compliance with near-zero latency. Key challenges, including data quality governance, model interpretability, cyber-resilience, and cost-to-performance trade-offs, are discussed to guide successful implementation. The synthesis underscores that a harmonized Big Data–distributed computing ecosystem can fundamentally enhance the accuracy, agility, and robustness of derivatives risk management — enabling financial institutions to mitigate emerging risks proactively while sustaining competitive advantage in increasingly digital capital markets.
Keywords
Big Data Analytics,
Real-Time Risk Management,
Derivatives Portfolios,
Distributed Computing
References
Savalei, "Real - Time Management of Financial Risks in the Derivatives Market," International Journal of Economics & Business Administration, vol.7, no.1, pp.36 - 51, 2019.
X. Zhang, Y. Li, "Real - time Risk Monitoring for Derivatives Portfolios," in Proc. IEEE International Conference on Data Science and Advanced Analytics, 2021, pp.556 - 565.
J. Sifounakis, M. Trihinas, and G. Floros, "Real - Time Risk Management," in Mastering Derivatives Markets, 2021, ch.12, pp.86 - 97.
J. Wu, D. Kumar, "The Applications of Big Data Analytics in the Financial Sector," Journal of Financial Services Technology, vol.3, no.2, pp.99 - 112, June 2021.
M. Zaharia, R. Xin, P. Wendell, et al., "Apache Spark: A Unified Engine for Big Data Processing," Communications of the ACM, vol.59, no.11, pp.56–65, Nov.2016.
X. Li, H. Bao, J. Shang, "Big data and financial stability: Challenges and implications,” in Proc. IEEE. Int. Conf. on Cloud Computing and Big Data Analysis, 2018, pp.539 - 542.
G. Dicuonzo, G. Galeone, E. Zappimbulso, and V. Dell'Atti, "Risk Management 4.0: The Role of Big Data Analytics in the Bank Sector," International Journal of Economics and Financial Issues, vol.9, no.6, pp.40 - 47, 2019.
Kesarpu, S., & Hari Prasad Dasari. (2025). Kafka Event Sourcing for Real-Time Risk Analysis. International Journal of Computational and Experimental Science and Engineering, 11(3). https://doi.org/10.22399/ijcesen.3715
N. Patel, "Big Data and Machine Learning for Real - Time Risk Analytics in Financial Markets", in IEEE Big Data Conference, Atlanta, GA, USA, Dec.2020.
J. Smith, B. Jones, A. Williams, "Big Data in Financial Risk Management," IEEE Transactions on Big Data, vol.5, no.2, pp.323 - 335, April 2019.
Reddy Gundla, S. (2025). PostgreSQL Tuning for Cloud-Native Java: Connection Pooling vs. Reactive Drivers. International Journal of Computational and Experimental Science and Engineering, 11(3). https://doi.org/10.22399/ijcesen.3479
Ashutosh Chandra Jha. (2025). DWDM Optimization: Ciena vs. ADVA for <50ms Global finances. Utilitas Mathematica, 122(2), 227–245. Retrieved from https://utilitasmathematica.com/index.php/Index/article/view/2713
Evaluating Effectiveness of Delta Lake Over Parquet in Python Pipeline. (2025). International Journal of Data Science and Machine Learning, 5(02), 126-144. https://doi.org/10.55640/ijdsml-05-02-12
Vennamaneni, P. R. (2024). Delta Lake optimization techniques for scalable lakehouse architectures. International Journal of Sustainability and Innovation in Engineering (IJSIE), 2, 290–314. https://doi.org/10.56830/IJSIE202409
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