Introduction

Quantitative risk measures involve numerical values and statistical techniques to assess risk. These measures provide a clear and objective view of the risk profile of an organization or investment. Some examples of quantitative risk measures are Value at Risk (VaR), expected loss, economic capital, standard deviation, beta and sharpe ratio. In this chapter, we will only cover VaR and economic capital.

Qualitative risk measures rely on subjective assessment and judgment to evaluate risk. These measures are useful for understanding risks that are difficult to quantify. Some example of qualitative risk measures are scenario analysis, stress testing, SWOT analysis and risk assessment matrix. In this chapter, we will only scenario analysis and stress testing.

Enterprise Risk Management (ERM) is a comprehensive framework that organizations use to identify, assess, manage, and monitor risks across all areas of their operations.

Integrating both quantitative and qualitative risk measures within an Enterprise Risk Management (ERM) framework allows firms to comprehensively assess and mitigate risks. This holistic approach enhances decision-making, resilience, and regulatory compliance, thereby supporting sustainable growth and long-term success.

Quantitative Measures: VaR and Economic Capital

VaR estimates the maximum potential loss a portfolio might experience within a specified confidence level over a given time horizon. The formula for VaR is:

$$\text { VaR }=\text { Portfolio Value } \times Z \times \sigma$$ Where:

• $Z$ represents the $z$-score associated with the desired confidence level.
• $\sigma$ denotes the standard deviation of portfolio returns.

Economic capital, on the other hand, represents the liquid capital required to cover unexpected losses. For instance, if the one-day VaR of a bank is USD 10 million and the bank holds $10 million in liquid reserves, it means that the bank has adequate economic capital and it is unlikely that the bank will face insolvency in a one-day event with expected tail risk.

Qualitative Risk Assessment: Scenario Analysis and Stress Testing

Scenario analysis evaluates potential future risks and their alternative outcomes by comparing best-case and worst-case scenarios, often by examining extreme values of variables. It includes various risk categories and aids risk managers in decision-making by quantifying qualitative concepts through what-if analysis. The objective of scenario analysis is to assess the potential full magnitude of losses, even in cases where the probability of loss is very small.

Stress testing is a form of scenario analysis and it is aimed at analyzing the financial outcomes by applying “stress” on an underlying parameter which is used to model the outcome. The stress is applied on one parameter at a time to assess its impact on the outcome. For example, stress testing can be used to determine the impact of severe change in interest rates on the deposits of a bank.

Note: While using scenario analysis or stress testing to determine worst case, two types of parameters are considered - historically sourced and estimated variables. Historically sourced parameters have the advantage of being observable, but there is also a limitation that historical trends may not necessarily predict future outcomes. Estimated variables are hypothetical forecasts based on risk managers’ assumptions. These variables have the advantage of providing a clear understanding of firm’s sensitivity to various qualitative risk factors, but there is also a limitation that such parameters can introduction estimation errors or model risk.

Enterprise Risk Management (ERM)

Enterprise risk management (ERM) is a comprehensive process for managing risk across an entire organization, rather than isolating it within individual departments. This top-down approach considers risks in relation to their potential impact on various parts of the company, rather than independently. A major challenge of ERM is the tendency to oversimplify risk management by reducing it to a single metric, such as VaR or economic capital. However, the 2007–2009 financial crisis demonstrated that risk is multi-dimensional and requires a nuanced approach from multiple perspectives. Effective ERM combines statistical analysis with informed judgment to address the complex nature of risk.

The primary objective of ERM is to understand and manage risks at the enterprise level, incorporating risk considerations into strategic business planning. ERM goes beyond merely aggregating risks and it integrates the risk holostically in the organization’s strategic planning to ensure comprehensive risk management.

Test Your Understanding