Enhancing Risk Management Practices: Insights from VAR Forecasting in Financial Markets

Abstract

This paper provides valuable insights into the challenges and opportunities associated with forecasting VAR in financial markets. By analyzing the strengths and limitations of different modeling techniques, the study offers guidance on selecting the most appropriate approach based on the characteristics of the underlying data and the specific requirements of risk management practices. Moreover, the research contributes to the broader literature on risk management by addressing the ongoing debate surrounding the adequacy of traditional VAR models in capturing extreme events and tail risk. By demonstrating the effectiveness of EVT-based approaches, the paper underscores the importance of adopting more robust and flexible methodologies to account for the inherent uncertainty and volatility in financial markets. Furthermore, the study emphasizes the need for continuous refinement and improvement in VAR modeling techniques to adapt to evolving market conditions and regulatory requirements. As financial markets become increasingly interconnected and complex, the ability to accurately quantify and manage risk becomes paramount for investors, financial institutions, and policymakers alike. In this context, the findings of the paper not only contribute to advancing our understanding of VAR forecasting but also provide practical implications for enhancing risk management practices in the financial industry. By incorporating insights from EVT and other advanced modeling techniques, stakeholders can better navigate the challenges posed by market volatility and uncertainty, thereby mitigating potential losses and safeguarding financial stability. Overall, the paper serves as a valuable resource for researchers, practitioners, and policymakers seeking to enhance their understanding of VAR modeling and improve the effectiveness of risk management strategies in the dynamic and ever-changing landscape of financial markets.