The spread of the COVID-19 virus has blindsided conventional risk models. By understanding what went wrong, investors can develop a more forward-looking approach to risk management that considers multiple scenarios for a highly uncertain market environment.

Risk models have become much more sophisticated following the global financial crisis (GFC). But most risk models—which rely predominantly on historical data—struggled to cope with the unprecedented effects of the COVID-19 pandemic. These included: the sudden disruption of global supply chains; the shutdown of cities worldwide; the breakdown of formerly reliable correlations between industry sectors and between investment factors; and the collapse of OPEC+* which triggered violent reactions in energy and stock prices (Display).

No model has been designed to cope with this combination of circumstances. Investors need to understand the strengths and weaknesses of their models to determine whether their signals can be relied upon in crisis situations in general, and through the coronavirus crisis in particular.

Understanding the Basis for Risk Modelling

Standard risk models are designed to measure a portfolio’s absolute risk (volatility) or relative risk versus a benchmark (tracking error). They then break down the risk into separate risk factors such as biases to currencies, industries, styles etc., leaving a residual component, which is not explained by pre-determined risk factors. State-of-the-art use of such models allows managers to concentrate risk where they have an edge or skill, and to avoid risk where they don’t. Conventional models typically fall into three categories (Display):