The global financial crisis reinforced the need to think seriously about tail risks, especially as traditional risk systems failed to warn of extreme losses and contagion spread across asset classes and markets. Plan sponsors would do well to consider a new forward-looking approach to scenario analysis and embrace a framework that allows them to explore potential portfolio weaknesses and tail risks. This is an integral part of ensuring that portfolio performance is robust in a diverse range of states of the world.
To implement a forward-looking scenario framework, it’s important to create a rich set of extreme but plausible outcomes in which to incorporate expert judgement. By integrating investment experts with scenario construction, investors can benefit from richer scenarios, more complex views, and an additional layer of oversight.
In order to capture sudden and persistent changes (regime shifts) and non-linear interdependence between financial assets, it is necessary to move beyond traditional single-regime models and the assumption that returns are normally distributed and consider a multi-variate regime model.
By using Monte Carlo simulations to create the market model as well as an efficient numerical process (a methodology known as entropy pooling), investors can re-value all option-like positions for each simulation outcome.
This approach allows co-movements between assets to be modelled as a non-linear function of the magnitude of the shock applied. Put together, the approach is more able to capture the pernicious nature of tail risks, and helps ensure that portfolio performance is robust to a comprehensive range of possible states of the world.
The value of successful forward-looking scenario analysis should not lie in the exact specification of portfolio gains and losses in a specific scenario. Results should be intuitively correct but not spuriously accurate. Rather, forward-looking scenario analysis should used to explore potential portfolio weaknesses through the interaction of subject matter experts who specify scenario shocks, risk managers who model the inferred losses, and fund managers who can use the results to challenge and enhance their intuitive understanding of the behaviour of their portfolios in extreme scenarios. An entropy pooling methodology helps to achieve this.
Rahul Khasgiwale is Investment Director, Standard Life Investments Inc.