New Risk Measures: Magnitude and Propensity Approach

Risk measurement, an interdisciplinary field that incorporates probabilistic modeling, data analysis, algorithmic efficiency, and financial markets, is a critical aspect of modern risk management. Traditional risk measures such as Value at Risk (VaR) and Expected Shortfall (ES) offer a single deterministic value representing the potential losses in a given distribution. However, these one-dimensional risk measures may not adequately capture the complexity of real-world risks. This study investigates the integration of magnitude and propensity in risk analysis to enhance risk assessment and decision-making processes. The objective is to develop a comprehensive framework that combines these two key dimensions to provide a more nuanced perspective on risk management. By leveraging historical data and statistical techniques, the research quantifies the frequency and severity of risks, leading to a deeper understanding of their impact. Real-world data and case studies are analyzed to contribute to the advancement of risk measurement and evaluation practices. By offering a more nuanced and robust characterization of risk, the proposed three-dimensional magnitude-propensity approach has the potential to enhance risk management practices across various domains, ultimately contributing to more informed decision-making and improved financial stability.