THE CONVERGENCE OF FINANCE AND HIGH-PERFORMANCE COMPUTING: IMPLICATIONS FOR MODELING ACCURACY AND RISK MITIGATION
Keywords:
High-Performance Computing (HPC), Financial Modeling, Algorithmic Trading, Real-time Analysis, Quantitative Risk ManagementAbstract
This article investigates the transformative impact of High-Performance Computing (HPC) on financial modeling and risk assessment through a comprehensive analysis of three case studies in the banking and investment sectors. By examining HPC implementations in complex financial modeling, real-time risk analysis, and large-scale data processing, we demonstrate significant improvements in model accuracy, risk mitigation strategies, and decision-making processes. Our findings reveal that HPC-driven solutions achieved a 40% increase in modeling speed, a 30% enhancement in risk prediction accuracy, and enabled real-time analysis of market data streams exceeding 1 million transactions per second. Despite challenges in data security and system integration, the adoption of HPC led to more robust financial strategies and improved regulatory compliance. This article highlights the growing importance of HPC in modern finance and offers insights into future trends, including the integration of quantum computing and artificial intelligence. Our article contributes to the understanding of HPC applications in finance and provides practical recommendations for financial institutions seeking to leverage advanced computational methods for competitive advantage in an increasingly complex global market.
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