Insurance may seem pretty straight forward: insurers collect premiums or payments from customers and then pay out benefits to policyholders and beneficiaries as claims become due. What makes this process more complicated is the timing and severity of these claims. In the case of life insurance products, insurers typically keep (and invest) the premiums for a relatively long time before paying out the benefits or claims. The investment portfolio should reflect the liability characteristics – whether it is the guaranteed interest payment credited to policyholders, unusually long duration cash flows, or undesired timing or size of policy surrenders – and address the potential liquidity risk, reinvestment risk and optionality resulting from the interplay between assets and liabilities. The bottom-up segmentation approach seeks to address the minimum rate guarantee, convexity or duration, and potential default loss considerations. However, it does not adequately reflect the potential diversification or interplay among different segments within the insurance enterprise.
It is common for life insurers to “segment” their assets to support multiple liability blocks (see Chart 1). Although this segmentation approach may have been driven by regulatory requirements, this bottom-up approach may ignore the potential interdependences among different segments (and components of the segment) of the overall investment portfolio. The modern portfolio theory (MPT) offers a framework to evaluate the return and risk trade-off of portfolios with different allocations, accounting for diversification or contagion effects. This is referred to as an efficient frontier framework.
Portfolio configuration is an iterative process that starts from satisfying (or defeasing) liability requirements, such as duration target, liquidity expectation and minimum rate guarantee, to asset characteristics or capital market environment, such as potential default losses for the fixed income securities and market value fluctuation for equities.
Moreover, depending on the regulatory and rating agency capital charges for different asset classes, portfolio configuration should evaluate return versus the required capital trade-offs. In the course of portfolio configuration, these regulatory and rating agency considerations can become “constraints” in the optimization process. This iterative process, as outlined in Chart 2, serves as a critical foundation for segment level portfolio construction.
For certain annuity products (and/or Universal Life) where policyholder behaviors are sensitive to the capital market environment, life insurers should pay close attention to policyholder optionality risk (withdrawal, lapse or surrender). For example, annuity contract holders may wish to move their contract to other insurers that offer more favorable crediting rates during a sharply rising rate environment. In this environment, life insurers’ fixed income portfolio market values decline and would probably suffer a realized loss should the insurers need to liquidate the position to meet the annuity contract holders’ demands. The industry has coined this phenomenon “dis-intermediation” risk. In mathematical terms, the annuity contract cash flows have a “positive” convexity; i.e., the duration of annuity product cash flows “shortened” under a rising rate environment.
From the asset perspective, specifically, Mortgage Backed Securities (MBS) tend to have a “negative” convexity; i.e., the duration of the cash flow is “extended” during a rising rate environment. Mortgage owners are unlikely to re-finance or prepay when rates are high, as shown in Chart 3. This example of opposite interest rate sensitivity of life insurance assets and products highlights the importance of incorporating a liability profile into the investment portfolio construction process.
As a life insurer offers multiple insurance products (segments), the insurance company can be viewed as an “enterprise portfolio” consisting of various “segments.” A profit-seeking insurance organization needs to generate favorable return on equity for its capital providers. Starting from the traditional return on equity (ROE) definition, we can further decompose the ROE of a life insurance enterprise into the following DuPont formula:
This holistic top-down approach considers the characteristics and interactions of invested assets and insurance products. The economic framework utilizes marked-to-market total return, volatility and correlation to assess an insurance enterprise’s return and risk trade-offs through the modern portfolio theory efficient frontier analysis. As shown in Chart 4, point A represents the company’s existing return and risk profile; and the efficient frontier represents potential opportunities for the company to achieve a higher risk-adjusted return profile. For example, point B illustrates a higher return with the same level of risk, while point C illustrates the same return at a lower level of risk; both points are considered to be more “efficient” than the current profile A.
In contrast to the top-down EBAA™ process, the bottom-up approach often neglects the critical interdependence considerations within the MPT framework that insurers should address when managing their enterprise investment portfolios.
The EBAA™ process provides a holistic framework to assist in asset optimization within the context of multiple product segment interactions. It also seeks to address the common question of how insurance companies should manage the assets that are not “earmarked or assigned” to certain liabilities or reserves; i.e., the assets supporting surplus. This “surplus asset allocation” question should be addressed under a holistic top-down framework (see Chart 5). Depending on the asset optimization results, the optimal portfolio reconfiguration can potentially take place within these unassigned assets (surplus account).
According to the modern portfolio theory, “enterprise-level” optimizations should yield better outcomes than “segment-level” optimizations due to potential diversification benefits captured under the global framework. Within the context of insurance enterprise optimization, the traditional segment level ALM might miss the implicit diversification benefits that are considered under the enterprise level asset optimization. However, the segment level ALM produces considerations (or constraints) that are critical components in the enterprise optimization. In other words, the top-down optimization builds on the segment level ALM and assists to further enhance the optimization outcome.
- Life insurers’ investment portfolio construction should address their liability characteristics and requirements. The traditional bottom-up ALM approach serves as an initial step to the investment portfolio construction.
- Our suggested holistic EBAA™ process embraces the bottom-up ALM process and takes it a step further, incorporating the diversification considerations into the enterprise-level optimization analysis.
- More importantly, this EBAA™ approach helps provide an integrated framework to evaluate the investment allocation for assets that are not earmarked for or assigned reserves; i.e., the surplus account assets. These assets are not typically addressed in an ALM only approach.
- NEAM believes a holistic EBAA™ process would help insurers to potentially enhance the risk-adjusted returns of their portfolio. To learn more about the potential benefits of our EBAA™ process,
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