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UniSuper, the $23 billion Australian pension
fund for those working in higher education and research, has developed an
in-house risk budgeting and factor analysis program that monitors the extent to
which the fund deviates from its strategic asset allocation, and ensures the
fund’s active risk is allocated appropriately between managers. Drawing on past
academic research, the head of research and risk management David Schneider and
head of public markets Dennis Sams, have extended conventional models to set a minimum
excess return hurdle at which active risk is appropriate, and encapsulate the
extent to which the active risk assigned to each of the fund’s managers is
consistent with the expected performance of those managers.

“The new model is
causing us to question a lot of our assumptions,” Schneider said, pointing to
its use of currency hedging as an example. Traditionally UniSuper has used currency
hedging across all of its portfolios; now it is considering adjusting the level
of hedging depending on the investment option’s level of risk. The UniSuper
Risk Budgeting and Optimisation System (TURBOs) has been designed by the fund
to determine how each manager generates their returns; identify market factor
exposures for each manager and aggregate these to determine overall factor
exposures; and assess the expected future alpha for each manager.

risk budgets have been derived with reference to tracking error, however for
UniSuper, which has both a defined benefit and a defined contribution plan,
Schneider believed this was inappropriate. “This approach is appropriate for asset
managers whose mandates are often specified in terms of tracking error
limitations. However, the concern with this approach for institutions with
guaranteed liabilities is that there is no direct interaction between the maximum
tracking error and the fund’s liabilities,” he said.

UniSuper sets its
strategic asset allocation to meet the investment objectives of the
accumulation options, and pay the liabilities for the defined benefit division
as they fall, so any deviation from the SAA is a source of risk to the fund. “The
marginal increase to risk is only justifiable if the fund’s expected alpha
exceeds the benefit that could be obtained by changing the fund’s SAA benchmark,
and moving along the fund’s constrained efficient frontier,” Schneider said.

“This idea provides an inequality that is used in our risk budgeting formulation,
namely that each option’s ex-ante alpha needs to exceed a minimum hurdle to
justify a departure from beta allocations.” The work undertaken by UniSuper is
built up from prior work on risk budgeting – including that of Mena (2007),
Litterman (2003), Scherer (2000), Kozun (2001) and Sharpe (2002). However the
authors extended these findings by, amongst other things, removing the
simplifying assumption that excess returns between managers are uncorrelated;
introducing the idea that to justify active risk, one needs to exceed a hurdle
return in excess of 0 per cent.

In order to meet these objectives six processes
were outlined to be computed by TURBOs: 1. Attributes each manager’s returns between
a series of market factor exposures (beta) and an observed expost (historic)
alpha component. This step is resolved using factor analysis and multiple
regression, with appropriate adjustments to manage collinearity, heteroskedasticity
and co-integration 2. Determines the ex-post total risk (volatility) and
tracking error, and assesses the marginal and proportional contribution to that
risk from each manger 3. Uses Bayesian techniques to determine an ex-ante
estimate of each manager’s alpha 4. Assesses the extent to which the beat
exposure differs to the fund’s SAA benchmarks 5. Uses risk budgeting techniques
to set a minimum excess return hurdle at which active risk is appropriate and assess
the extent to which the hurdle is expected to be achieved 6. Employs reverse
optimisation to confirm whether the active risk assigned to each of the fund’s
managers is consistent with the expected performance of the manager.


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