Strategic Asset Allocation in Asia:

Optimizing Across Portfolios

Publication Date: 09th Jan, 2017


This article was published by the Society of Actuaries in the February 2017 edition of Risks and Rewards.

https://www.soa.org/Library/Newsletters/Risks-And-Rewards/2017/february/rar-2016-iss-69.pdf
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:


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 another’s “domestic”)

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 model accuracy.

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):
  1. 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).
  2. 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.

Conclusion

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 and analytics.


Michael Chan, FSA
Is a co-founder of Coherent Capital Advisors Limited. He can be contacted at Michael.Chan@coherent.com.hk.

Fred Ngan, FSA
Is a co-founder of Coherent Capital Advisors Limited. He can be contacted at Fred.Ngan@coherent.com.hk.

Thomas Tang, FIAA
Is a director of Coherent Capital Advisors Limited. He can be contacted at Thomas.Tang@coherent.com.hk.

Jack Law, Consultant
Is a consultant of Coherent Capital Advisors Limited. He can be contacted at Jack.Law@coherent.com.hk.




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