The portfolio of a defined benefit (DB) pension plan is often described in terms of its market value exposure—such as a “60/40” mix between risky assets (equities) and less risky assets (bonds). While “asset mix” is an easy way to describe the broad level of risks in DB portfolios, it is not the best way to think about the pension risks that matter for at least two reasons. First, while the asset mix explains why some funds are riskier than others, even a static asset mix (same 60/40) has a changing risk profile over time. For example, equity sectors—such as energy or technology—become more or less concentrated over time even when the portfolio is invested passively (e.g., S&P 500 Index). Second, what matters for optimizing portfolios is the marginal risk contributions that assets make relative to their expected returns.
It is absolutely necessary, then, to use relevant risk metrics when constructing DB portfolios and monitoring their efficiency over time. This risk transparency includes, for example:
- Total Fund Views: DB plans should compare their market value allocations (e.g., 60/40) to their risk allocations. The risk contribution from equities is higher than its market value exposure. The degree to which it is higher might surprise some people. The impact of derivatives or alternative investments is best analyzed through a risk lens.
- “Deeper Dives”: DB plans should be able to investigate the source of risk by aggregating risks in different ways (e.g., currency or sector), at the total fund level or any other grouping that makes sense (e.g., by asset class or manager).
- Active Risk (“Tracking Error”): Funds want to outperform their benchmarks, and this alpha goal means taking active risk or tracking error. It’s important to measure and monitor this risk on a forward-looking (ex-ante) basis. Ex-post realizations provide useful information, but there’s a difference between what actually happened and what could have happened.
- Funding Risk: Beating benchmarks is great, but if liabilities grow faster than passive returns, alpha won’t likely make much of a difference. Finally, the critical piece of the overall analysis transforms the liabilities into a benchmark and runs through the same analytics to evaluate where there is a potential gap between the assets and liabilities.
There are many analytics that can be incorporated into these groups above. For example, Value at Risk (VaR) is a common metric used to quantify risk in normal market conditions, but it should be augmented by stress testing to see what happens when things aren’t so normal. There are specific metrics for specific asset classes that provide good insight—durations and PV01s for fixed income, and greeks for derivatives. All of these calculations add more color to the risk picture.
Register for our June 22, 2022 webinar in which we will take on the notion of #3 Active Risk. Specifically, the idea of risk budgeting and answering the question of whether you are taking on enough or too much risk. To determine the adequacy of risk levels, comparing risk to a benchmark is a common approach. There are many techniques, such as portfolio optimization, that can be used to more quantifiably answer this question.
Written by Roman Chorneyko
VP, Cloud Solutions, SS&C Algorithmics