US public sector pension plan managers believe the plans they support will spend more on scenario modelling and stress testing in the next two years to help manage the increased risk of market shocks according to new research from Ortec Finance.
The study with senior US public sector pension plan professionals who collectively help manage over USD1.315 trillion, found increased appetite for investment in tools that help generate and analyse plausible risk–return scenarios.
Despite reporting extremely high levels of efficacy with existing stress testing and scenario modelling – 44 per cent say they are very effective and 56 per cent say they are quite effective at asset liability management (ALM) – over the next two years 90 per cent of the public sector pension professionals interviewed say the plans they work for will spend more on tools in this area.
Currently 44 per cent of the public sector pension professionals interviewed by Ortec Finance, outsource their asset liability management (ALM) studies entirely to third parties, and 54 per cent say they outsource partially.
When asked to assess their in-house ALM capability, 26 per cent of those pension professionals interviewed rated their teams excellent; with the remainder describing them as good or average.
Just one in five (20 per cent) of those surveyed say they take both assets and liabilities into account when assessing risk; 42 per cent say they look solely at liabilities while 38 per cent consider assets only.
Marnix Engels, Managing Director, Pension Strategy Ortec Finance says: “Market shocks continue to dominate the investment climate and the degree of uncertainty is extremely high for US public sector pension plan sponsors.
“The stochastic models currently available to sponsors and their advisers including the Monte Carlo simulations are too simplistic and may generate results that do not fully account for major economic and market shock events – such as COVID-19. It makes sense to increase spending on advanced tools that offer more realistic and useful insights in changing market conditions.”
Engels adds: “An ideal model would be adaptive and time-varying, and able to account for current market conditions and factor in uneven distributions of results in real-time.”
