Insurance Insights Q1 2025: Enhancing portfolios using updated capital market assumptions

Invesco’s Capital Market Assumptions (CMAs) are updated on a regular basis and incorporate the latest market developments. These are often critical inputs for asset allocation analyses, especially when assessed over a longer time horizon. In this section, we outline a practical approach to compare changes in expectations over time (whether up or down) as they apply to a portfolio, and then determine whether new asset classes can assist in improving the overall risk-return profile – essentially, can we enhance efficiencies?

For the purposes of this case study, we have used Singapore’s Risk Based Capital 2 (RBC 2) framework for assessing the relevant capital charges as an example of a generic risk-based capital regime and use the base currency as USD. We see a trend of convergence to similar RBC frameworks across the APAC region and so we feel the direction of changes on allocations under one risk-based capital regime can still be meaningful and of relevance across others.¹

A starting point for any insurance strategic asset allocation exercise is to assess how asset class expected returns stack up against potential capital charges. Of course, an insurer needs to consider the relative attractiveness of various asset classes from both an absolute return perspective and from an expected return-to-risk perspective. It is important to look at both these aspects or else we may be left with a less efficient portfolio. Government bonds typically have no credit charge – so, any incremental yield would look attractive; emerging market bonds, on the other hand, often have a higher yield, but at the cost of higher spread charges. Therefore, striking a balance between these parameters is important within a portfolio setting. In most cases, insurers will choose to balance these opposing factors and construct a portfolio accordingly. Of course, as these parameters will change depending on market conditions, insurers need to continually seek out relative advantages between asset classes that may surface. Hence, it is important to assess portfolios across several dimensions and ensure that portfolios are re-assessed on a regular basis to ensure the objectives are still valid and achievable.

Implementing Capital Market Assumptions (CMAs) into our analysis

In this example, we start with a generic asset allocation comprising the following asset classes (represented by the indices/proxies below) – meant to broadly represent a plain vanilla portfolio (government bonds to manage duration; credit exposure for yield enhancement; and listed equities, including some real estate exposure, to provide potential upside/alpha opportunities):

The charts (Figures 1 and 2) compare risk-return profiles of the generic portfolio above using the December 2023 CMAs and the latest December 2024 CMAs (or the September 2024 CMAs in case December 2024 are not available for some of the asset classes). This can help isolate the impact on the expected yield/return driven mainly by changing capital market assumptions.

Figure 1 (left) shows the portfolio characteristics on an economic basis and Figure 2 (right) shows the characteristics on an RBC basis. The purple lines represent the frontiers with CMAs from December 2023 and those in blue represent the CMAs as of December 2024 (or the latest available CMAs).

We observe that there is a slight increase in the expected return of the portfolio over a 10-year horizon, reflecting updated capital market assumptions/yields, with smaller changes in expected economic volatility and virtually unchanged RBC charges (the latter reflecting very slight drift in the underlying representative indices). If we investigate some of the data, we see that the increase is due primarily to the upward shift in yields (from a new money perspective), while some of the growth asset assumptions have come down marginally. The macro environment is clearly dynamic and ever-changing and thus views and expectations evolve over time, incorporating additional information gleaned over the past year.

The effects of adding more asset classes to a portfolio

As part of the asset allocation analysis, the next step is to consider asset classes that can improve the risk-return profile. As we highlighted earlier, while capital charges for certain asset classes might be relatively high, their correspondingly higher expected returns can still make them appealing. As part of a portfolio, the additional benefit of diversification may also help with generating further efficiencies.

We continue our analysis by taking the above starting portfolio and adjusting it to include new asset classes and then re-examining the risk-return profile with our portfolio analytics tool (Invesco Vision²), based on the latest CMAs.

Looking at the components of the existing portfolio, we decided to increase the exposure to private markets to a limited extent – specifically those asset types that we feel can act as good complements to existing exposures. Accordingly, we slightly reduced exposure to global high yield and allocated to private credit, reduced exposure to public REITs and allocated to a value-add real estate fund, and finally, reduced exposure to public global equities and allocated to large leveraged buyouts. Our updated portfolio now comprises the following exposures, represented by the indices/proxies below:

We then compare the risk-return profile of this adjusted portfolio to the original portfolio (all using the latest available 2024 assumptions). The charts below demonstrate changes to the efficient frontier by adding these new asset classes and indicates where the original and enhanced portfolios lie.

Figure 3 (left) shows the portfolio characteristics on an economic basis and Figure 4 (right) indicate the characteristics on an RBC basis. The blue lines represent the starting portfolio, and the green lines represent the adjusted enhanced portfolio – both sets using the CMAs as of December 2024 (or the latest available CMAs).

We observe an improvement in the return profile of the adjusted enhanced portfolio — that is, the green dot representing this enhanced portfolio has moved upwards compared to the blue dot which represents the starting portfolio. The adjusted portfolio shows a very slight reduction in risk (on an economic basis), with a small increase in the RBC charge estimate – the latter driven by a small exposure to a private real estate fund (which has a slightly higher charge when considering embedded leverage – to be conservative). Overall, the enhanced portfolio still looks fairly efficient with the higher expected return offsetting the slight increase in estimated RBC charges. We should highlight that this result has been obtained with small adjustments to the portfolio (+/- 1% to 2% across specific asset classes). Additional adjustments can be made to focus on other key parameters as required/desired.

We observe that we have been able to improve the portfolio profile by selectively adding asset classes that have favorable risk-reward characteristics, and that are complements to existing exposures. Increasingly, we are finding that this means a reduction in exposures to public/listed assets and a corresponding increase in private/unlisted assets to generate additional pick-up.

Of course, we do stress that these sources of additional premium need to be assessed and analyzed carefully and insurers need to ensure that other key parameters (such as liquidity) remain well managed and in accordance with risk appetites. Private assets can be a good adjunct to public asset classes and can help bring about additional diversification. This opens up the potential for further adjustments to the asset allocation. The example here is meant to illustrate the process that can be followed, and, using Invesco Vision, the results can be assessed quite swiftly.

What this means for insurance portfolios – potential routes to implementation

The next logical question for insurers is how they can implement such changes. While insurers, for the most part, and driven in no small measure by updated regulations/financial reporting standards, may prefer segregated mandates, gaining access to such new asset classes (especially private assets) could be time and resource intensive. We increasingly find that insurers are looking towards easily tradeable investments when assessing how best to bridge the gap between gaining (almost) immediate market access and developing that longer-term private asset exposure buildout (including, but not limited to, laying out a roadmap, considering specialist managers, and deploying capital over time). Exchange-traded funds (ETFs) are increasingly meeting this need of providing market beta access to private asset classes (such as broadly syndicated bank loans and structured credit such as CLOs, for example) in a low cost, liquid, and transparent manner. ETFs generally cover a wide range of strategies (passive and active) and can assist insurers in transitioning to their long-term asset allocation in a cost-effective, efficient manner.

The case study above illustrates the process through which insurers can review, assess, and enhance their asset allocation by identifying suitable asset classes to include in their portfolios with favorable risk-reward characteristics.

Investment risks

The value of investments and any income will fluctuate (this may partly be the result of exchange rate fluctuations) and investors may not get back the full amount invested. Diversification and asset allocation do not guarantee a profit or eliminate the risk of loss.

Invesco Investment Solutions (IIS) develops Capital Market Assumptions (CMAs) that provide long-term estimates for the behavior of major asset classes globally. The team is dedicated to designing outcome-oriented, multi-asset portfolios that meet the specific goals of investors. The assumptions, which are based on 5- and 10-year investment time horizon, are intended to guide these strategic asset class allocations. For each selected asset class, IIS develop assumptions for estimated return, estimated standard deviation of return (volatility), and estimated correlation with other asset classes. Estimated returns are subject to uncertainty and error and can be conditional on economic scenarios. In the event a particular scenario comes to pass, actual returns could be significantly higher or lower than these estimates.