Strategic Asset Allocation in Asia:
Optimizing Across Portfolios
Publication Date: 09th Jan, 2017
Many regional insurers in Asia have been evaluating and re-positioning their asset portfolios, generating a lot of interest and activities
in Strategic Asset Allocation (SAA) and tools to help evaluate investment strategies. We see a few key drivers behind these recent changes:
Continued low interest rates, and fewer and fewer levers available for insurance companies to maintain their profitability.
This is a particularly acute issue for markets where intense competition left many insurers with legacy portfolios that have high
guaranteed interest rates.
Statutory required capital regulations are maturing with increased focus on asset-liability management. This is part of an overall
attitudinal shift towards encouraging better risk management, to be balanced against previous national priorities of increasing insurance
Some regulators loosened restrictions on insurers to invest overseas, sometimes in response to the low yields available to insurers
Non-traditional asset classes are gaining attention due to both demand (yield) and supply factors (e.g. tightening of banking
regulations opened opportunities for insurers to finance infrastructure projects).
A common challenge insurers face amidst all this change is processing all the implications of these changes across multiple portfolios.1
In this article, we use a simple case study to introduce a data-driven process to evaluate alternatives presented across multiple portfolios.
Such a process has the advantage of increasing the transparency and confidence in the robustness of the recommendations, as trade-offs
to be quantified and explained.
1A common issue across Asia is the need to split, at a minimum, the portfolio across participating and non-participating products,
and it’s not uncommon to see 10+ asset portfolios in a single market. Regional insurers often need to further optimize asset
allocations across countries, especially if “overseas” investments are to be coordinated (one country’s “overseas” might be
1. Understanding your Liability and Capital Profile
Wisdom suggests that spending time to clearly define the problem early on can help avoid an unnecessary waste of efforts down the line.
An insurance company typically owns more than one product line. The cash flow and risk profiles of different products often vary drastically.
For our simple case study, we’ll use the example of a simplified insurance company with two product portfolios – a long-term life product
and a short-term health product. Figure 1 illustrates the liability cash flow patterns and duration profiles of these two portfolios.
Although setup to have similar valuations, the two product lines have quite different size and timing of cash flows, interest rate
sensitivities, etc. The better the SAA team understands the product portfolios, the more likely they can arrive at an effective solution
for asset liability management. Our own experience is that some insurers in developing markets do yet not have a good sense of their cash
flow positions due to the absence of a robust asset-liability model.
Figure 1: Best estimate liability cash flow and duration profile
2. Defining the SAA Objectives and Constraints
Insurance companies have multiple stakeholders driving different objectives and constraints on the asset allocation decision,
for example risk, investments and actuarial groups to name a few, compounded across local and regional offices for Asian insurers.
Often it is up to the SAA team to balance these different needs, and necessitates engaging multiple parties particularly when asset
allocation conflicts arise.
Due to the different regulations across Asia (with each market at different levels of maturities), unique conflicts can arise for
regional insurers where a good asset allocation in one market can create issues for the parent company operating under different regulations
(e.g. different risk-reward tradeoffs under local statutory and Solvency II capital regimes for European insurers). This leads to an
iterative process of objective/constraint setting and result testing. It is our belief that a proper SAA model can greatly speed up the
feedback cycle to make the process more efficient – and less frustrating – for the project team.
3. Constructing the Asset Universe and Return Assumptions
Developing future asset returns assumptions can be a challenge, especially for non-traditional asset classes in which an insurer
has no prior experience. External asset managers and investment consultants may be able to provide perspectives on the appropriate
return target, implementation strategies, and realistic expense levels. Figure 2 demonstrates a Markowitz-style risk/return trade-off
that could be adopted by an insurance company contemplating overseas and alternative investments, and is a good starting point for
screening whether certain asset classes make sense at a high-level and for visually catching any unrealistic assumptions prior to
running any models.
Figure 2: Asset return assumptions and capital risk charge
4. Evaluating Risk-Return Trade-offs with a SAA Model
Traditionally, financial and risk reporting models are re-purposed to perform SAA analysis by running brute-force trials across different
asset allocations. While this is a reasonable approach for resource-constrained actuarial teams, trying to determine a set of optimal
allocation this way often leads to manual, tedious and time consuming projects. Perhaps more importantly, such an approach makes deeper,
more insightful studies prohibitive to undertake as it would require performing many different model runs using a resource- and
time-intensive process. We think this is a missed opportunity for many insurers, because of the high leverage a good SAA study can bring
to the organization.
One promising trend we have seen in Asia is a gradual switch towards building “light” SAA models that extend existing actuarial models to
deliver faster analysis of different asset allocations decisions. There are significant benefits to having a model which abstracts
SAA-insensitive elements (for instance, mortality risk) from calculations to improve speed across analytical iterations without sacrificing
With the aid of speedy, light SAA models, we could go beyond traditional analyses that were typically only feasible on a small number of
asset allocations, and enter the realm of large scale analyses. We strongly believe that a quantitative change in the data and results
available can lead to a qualitative change in our understanding of the issue and the solutions.
To illustrate this we tested the simple two portfolio situation with a light SAA model to test hundreds of thousands of different asset
allocations. The first two charts of Figure 3 visualize the results as clouds of points on the risk-return space at two levels of
granularity – fund and total company level.
This helps overcome a challenge with asset allocation optimizations where two very different asset allocations can give rise to similar
‘outcomes’ (as measured by the selected risk/return metrics). This ‘many-to-one’ mapping characteristic makes optimization exercises
challenging and unreliable.
In Figure 3, we highlight two competing portfolios with similar risk levels (at the total company level):
Portfolio 1 is more efficient at the company level (it lies on the efficient frontier) but the resulting allocation for the
health product fund is clearly suboptimal (it lies far from the frontier).
Portfolio 2 is close to the efficient frontier, yet its company-level portfolio is visibly suboptimal.
Figure 3: Tracking portfolios that are efficient at different aggregation levels
This illustrates how selecting ‘optimal’ portfolios at the fund-level does not guarantee the best results at the total company level.
In effect, the company’s capital could be more efficiently used by opting for a seemingly ‘suboptimal’ allocation in portfolio 2.
In this case, the apparent trade-off between the two portfolios stems from the choice of fixed income durations. Portfolio 1 invests
heavily in long-term high-quality bond to help with the overall long duration requirement driven from the life product fund.
In effect, the health product fund has subsidized the other fund to bolster overall performance. And this is “encouraged” because the
long-term products have much higher capital charges than shorter term products and are more capital intensive. Even in this simple example,
we generated a scenario where there is a trade-off between fund optimality and company optimality. While it may seem reasonable to put the
company-level efficiency as the priority, management questions arise such as whether it’s sensible to allow one fund to subsidize, and what
would be the KPI and performance compensation implications?
5. SAA and ALM Presentations, and Embedding the Decisions into Operations
The finale and one of the most important steps: an SAA analysis is only as useful as its improvement to the business, and we need to
“measure what matters”, as how Peter F. Drucker puts it: “What gets measured gets improved”. Implementing an SAA is one of the highest
leverage activities that is easy to put off for an insurance company.
Changing the SAA (for instance, adding a new asset class or change the asset mix) has wide implications on the business operations from
product pricing through capital management. Embedding this into the decision-making process requires a sound governance structure, together
with comprehensive reporting requirements, to ensure the key technical and commercial considerations are covered.
In this article, we described a process to develop a Strategic Asset Allocation (SAA). We showed how a combination of small-scale,
intuitive runs can be combined with larger-scale, computationally-intensive runs to provide more insights than traditional approaches of
running a handful of allocations or trying to run optimization algorithms on the fragile problem.
The key enablers of these new methodologies is a lighter, accurate SAA model that can be built on top of existing systems, and the advent of
cheap computational power that allows our focus to shift from trying to run optimizations and instead focus on generating the full set of
results from which the analyst can explore using modern analytical techniques.
Finally, through the analysis on trade-off between fund and company level optimality, we also showed how the results of these new types of
analyses can be visualized to better communicate the insights to senior management and demonstrate the value of investing into the SAA models
Jack Law, Consultant
Is a consultant of Coherent Capital Advisors Limited. He can be contacted at Jack.Law@coherent.com.hk