- Customised benchmarks are essential for insurance portfolio management. Solvency II magnifies their importance.
- Customisation captures a company’s unique business profile, including:
- The timing and variability of liability cash flows.
- The firm’s targeted risk profile and financial strength rating.
- The baseline asset mix consistent with management objectives.
- Benchmark construction is influenced by a range of factors:
- Liability profile. Insurance liability cash flows are a key factor in building portfolio benchmarks designed to optimise risk-adjusted ROE.
- The firm’s definition of risk (e.g. ROE volatility, ROE downside).
- Other factors, including capital ratios and stress test limits.
- Benchmarks provide a reference point for risk management and performance evaluation:
- What are the appropriate ranges for exposures to non-diversifiable risk factors such as interest rates, liquidity spreads and stock prices?
- What are the appropriate limits for exposures to a single market sector or issuer?
- How will the firm’s investment results vary across economic and market scenarios?
- How well has the asset management team performed?
Insurance portfolio management is not simply a question of earning returns on assets. It is also a crucial part of an insurer’s efforts to create value for stakeholders. Consequently, portfolio strategies must incorporate a firm’s liability characteristics as well as regulatory, rating agency and other considerations. The process of building customised benchmarks helps establish a framework for portfolio management that is consistent with a company’s overall business objectives.
Solvency II measures capital as the difference between the market-consistent values of assets versus liabilities. This will bring transparency to the economic volatility of insurance company balance sheets. Well constructed benchmarks facilitate portfolio strategies that optimise tradeoffs among required regulatory capital levels, volatility and return.
This paper discusses the creation and use of customised benchmarks for insurance portfolios. Benchmark construction is illustrated with a mean/variance asset allocation model and stress testing.
Market Indices versus Customised Benchmarks
Market-based fixed-income indices have specific, documented rules that identify a representative sample of securities in the market sector that a particular index tracks. The resulting index portfolio reflects the risk and return characteristics of that market sector. Investors often use market indices as a benchmark to measure the level and sources of value created by asset managers.
The usefulness of a benchmark is directly related to how well it reflects an investor’s objectives and risk preferences. The primary objectives of an insurance portfolio are to meet policyholder guarantees and to provide a competitive, risk-adjusted return to shareholders. Because each insurance company has a unique product mix and liability cash flow profile, widely used market indices (e.g. Barclays Capital’s aggregate bond indices) generally are not consistent with these objectives. Customised benchmarks are required in order to reflect an insurance company’s unique liability cash flows, risk/return targets, and related investment restrictions imposed by management, regulators and rating agencies.
Barclays Capital, JPMorgan Chase, Merrill Lynch and Citigroup maintain a wide range of indices and sub-indices. Benchmark customisation varies the weights of index sub-components to meet an insurance company’s objectives while preserving desirable benchmark characteristics such as transparency and investability.
A well constructed benchmark reflects the passive portfolio that would best support an insurance company’s business. Customised benchmarks explicitly communicate a company’s investment objectives, establish a baseline for defining active management risks that may be taken in pursuit of excess return, and provide a relevant basis for portfolio performance evaluation, risk management and communication.
Customised Benchmark Construction
Benchmark construction is influenced by a range of factors. Perhaps the most critical of these are an insurance company’s liability profile, risk/reward preferences and capital position. The following paragraphs discuss and illustrate customised benchmark construction.
Insurance Portfolios Revolve Around Policy Liabilities
Insurance companies are leveraged investors. They “borrow” policyholder premiums to purchase investment assets and service their “debt” by paying policyholder benefits. Shareholders’ risk/reward profiles are, by definition, the level/risk of the difference between asset and liability returns. Investment strategy should therefore revolve around a company’s liabilities. Shareholder return and volatility are not measured as absolutes based on assets alone; rather, they are measured based on assets relative to liabilities. Similarly, Solvency II measures regulatory capital levels based on the difference between the market-consistent values of assets versus liabilities. Liability-driven benchmarks should lead to less risk for policyholders, a more efficient risk/reward profile for shareholders, and a more stable regulatory capital position.
Insurance companies often focus on maximising the investment portfolio’s book yield to achieve higher operating earnings. However, as recognised by Solvency II, portfolio market value and the economic value of capital are critical to a company’s long-term balance sheet health and profitability. Most insurance companies therefore balance two objectives: high book yield/income and maximising the total return of assets versus liabilities. The latter objective is most relevant to this discussion because benchmark candidates are selected to maximise shareholder total return at various risk levels. In practice, the high book yield/income objective is addressed through a yield emphasis during portfolio construction.
Liability-Replication and the Solvency II Market Proxy
Before a customised portfolio benchmark can be built, the creation of a market proxy to replicate insurance liabilities is required. In Solvency II QIS5 (quantitative impact study 5), the market proxy is defined by the present value of an insurance company’s “best estimate” liability cash flows discounted at swaps minus 10 basis points (bps) plus a liquidity premium. The liquidity premium is derived from the spread over swaps of a standardised model portfolio of corporate bonds and varies by currency. This liability proxy provides the starting point for constructing asset portfolio benchmarks designed to maximise expected excess return over the liability proxy for specific levels of risk.
As a simple illustration, assume that an insurance company’s policy liabilities have a value equal to 90% of the market value of assets. The 10% difference between assets and liabilities is the value of balance sheet surplus.1 Furthermore, assume that the liability cash flows are replicated by a mix of one-, three-, five- and 10-year market proxy liabilities (as defined by Solvency II) with the following weights:
- One-Year Liability Market Proxy = 20% of assets
- Three-Year Liability Market Proxy = 20%
- Five-Year Liability Market Proxy = 20%
- 10-Year Liability Market Proxy = 30%
- Total Market Proxy Liabilities = 90% of assets
Once the liability-replicating market proxy is defined, customised portfolio benchmarks can be created.
Investment Portfolio Benchmark Optimisation
A mean/variance asset allocation model is used to illustrate the construction of efficient investment portfolio benchmarks. The model is long assets (100%) and short liabilities (90% of assets). Liability cash flows and characteristics are defined by the replicating market proxies outlined in the previous section. The model’s objective function selects asset index weights to maximise the total return on balance sheet surplus (i.e. the difference between asset and liability returns) for a range of risk levels, where risk is defined as the standard deviation of surplus returns. Exhibit 1 illustrates model inputs, which are shown in dark blue.
To create a customised portfolio benchmark, an extensive range of indices and sub-indices are available by sector, subsector, quality and duration. For this illustration, asset class choices are limited to the following Barclays fixed-income indices:2
- Cash: Three-month LIBOR Swap Index
- Mortgage-Backed (RMBS and CMBS) and Asset-Backed Securities
- Investment-Grade Credit
- BB Rated High-Yield
The asset allocation model requires forward-looking return, volatility and correlation estimates.3 For simplicity, this example bases its estimates on five years of historical data, from December 2001 to December 2006. This period includes the US recession of 2002, expansion in 2003, and an extended period of equilibrium growth. In practice, judgment drives decisions about how historical relationships are combined with the current outlook to arrive at reasonable forward-looking assumptions.
Exhibits 2 and 3 display optimisation model results. Exhibit 2 plots the risk/return tradeoff for five targeted risk levels, ranging from 0% to 20% surplus return volatility (x-axis). The first point on the far left, with 0% excess return volatility, is the risk-free or hedge portfolio. It is 90% long a theoretical liability-replicating market proxy, exactly hedging liabilities to produce a zero net return contribution. The remaining 10% of assets, funded by surplus, is invested in cash.4 Cash is shown to return 2.5%, the average rate earned over the five-year period used in the analysis.
The hedge portfolio provides a low risk reference point. However, insurance companies will try to enhance surplus returns with more risky investment strategies designed to outperform liabilities. Exhibit 2 plots four alternative benchmark portfolios with surplus return volatility ranging from 5% to 20% and pre-tax returns from 7% to 12%. The asset index weights associated with these four alternative customised benchmarks are shown in Exhibit 3. Ultimately, the benchmark choice is a management decision that is influenced by factors such as the company’s target financial strength rating and regulatory capital position.
Downside Risk and Stress Testing
Mean/variance asset allocation models, like the one used in our illustration, are convenient and relatively simple to use. However, an underlying assumption is that fixed-income returns are normally distributed, which is not the case. Spread assets have asymmetric downside risk. Stress tests can supplement mean/variance models by showing how mean/variance optimised portfolios performed in stressful historical periods; this sheds light on the downside tail risk that is not evident with normal distributions.
The financial crisis that began in 2007 marks an extraordinary period of market stress. A severe stress test constraint uses the annualised benchmark returns from July 1, 2007 to October 31, 2008 for each asset class. The constraint limits the one-year return on surplus to -50% during this period. All benchmark portfolios created in the earlier analysis, other than the liability-replicating portfolio, fail to satisfy this constraint. The constraint moves the efficient frontier and associated asset class mixes to a significantly lower risk and return profile, as shown in Exhibits 4 and 5.
Exhibit 4 shows that no portfolio with surplus return volatility greater than 11% will satisfy the -50% return constraint. Portfolios with 5% and 10% return volatility produce expected returns 1% and 2% lower than the unconstrained example. As illustrated in Exhibit 5, the downside constraint is met only with a Treasury/agency weight of nearly 50%. The stress test results highlight the importance of evaluating the cost/benefit tradeoffs of severe downside protection, including implications for insurance product pricing.
An alternative approach to incorporating downside risk changes the objective function’s definition of risk from return volatility to a measure that focuses on downside return outcomes. Downside risk definitions include:
- The probability that return falls below a specified minimum.
- The expected loss when return falls below a specified minimum.
In a downside-risk optimisation, the model’s objective function maximises expected returns for various levels of downside risk.
Using Customised Benchmarks
A customised benchmark is an important reference point for investment guidelines, risk management and investment strategy communication.
Setting Active Risk Limits
Customised benchmarks can be thought of as passive portfolios that fit a company’s liabilities and management objectives. Once a benchmark is established, asset managers use it as a point of reference. In particular, when market conditions change, managers adjust portfolio risk exposures relative to the benchmark. Changes in relative values offered in the market typically translate into new exposures to rates, sectors and issuers. The benchmark and risk budget drive the size of these exposures.
Investment guidelines should specify benchmark-relative limits on exposure to non-diversifiable risk factors (e.g. interest rates) and to diversifiable risk factors (e.g. exposure to individual issuers). Limits should include measures of sensitivity to individual risk factors as well as overall portfolio risk. Examples of relevant risk measures include:
- Tracking error
- Sector exposure
- Issuer exposure
- Average quality
- Spread duration
Scenario analysis measures the implications of active risk limits using either historical or forward-looking assumptions. A company’s regulatory capital position and the impact of risk positions on capital requirements and ratios are key considerations in establishing risk limits.
A well constructed benchmark facilitates communication between insurance company management and the asset management team. Portfolio positioning relative to the benchmark reflects an asset manager’s views about risk/reward opportunities in the market and provides a starting point for a more detailed discussion about portfolio strategy and risk factor exposures.
Portfolio performance is evaluated relative to the benchmark, and excess returns are attributed to active positions taken by the manager. Effective and proactive communication about the size of and rationale for active risk exposures should lead to actual performance results that are well understood by both asset managers and insurance company management.
Other Issues and Considerations
Benchmark construction and portfolio management should consider a range of other important issues. The following paragraphs touch briefly on some of these.
The benchmark analysis assumed that insurance liability cash flows were well-defined. Clearly, many insurance products contain significant amounts of actuarial risk (i.e. variation in cash flows that is random and not related to events in the economy or markets). To the extent that this risk is uncorrelated with market factors and cannot be hedged at a reasonable cost, the efficient frontier risk/reward menu may change significantly.
The efficient frontier from our original optimisation, which included no actuarial risk, is shown in the dark blue line in Exhibit 6. The light blue line introduces 50 bps of uncorrelated actuarial risk to insurance liability cash flows. The result is residual risk of 4.5% on surplus returns for the lowest risk portfolio. Uncorrelated actuarial risk limits how much risk can be hedged and introduces a steeper return/risk tradeoff, particularly at lower risk levels. Moving from the lowest risk portfolio’s 4.5% return volatility to 5% produces an expected return pick-up of 2%. The information ratio for this move is four times, or 200 bps of extra return for 50 bps of extra risk.
Some insurance products have payouts that are driven by events in the economy and markets. For example, a poor economy may reduce the amount of driving done by a property/casualty company’s auto insurance customers, reducing claims and providing a benefit that offsets wider asset spreads. Long-term care policies offered by life insurance companies will often have payouts that vary with inflation. These relationships are important considerations for the benchmark and portfolio strategy.
Certain insurance products (e.g. single-premium deferred annuities) and investment asset classes (e.g. mortgage-backed securities) have significant embedded, path-dependent options. The benchmark analysis ignored these. Valuing these options and their factor exposures requires more sophisticated modeling techniques, but they are an important consideration.
The benchmark analysis optimised pre-tax total return on surplus. Benchmark construction must also consider a company’s ability to utilise tax-favoured investments and the asymmetry in costs and benefits of erring on one side or the other of the optimal allocation to these instruments.
The benchmark analysis assumed that a company holds the same level of equity capital, regardless of portfolio composition. In a full analysis, companies will adjust the cost of equity to reflect differences in the firm’s risk profile. They will also consider regulatory and rating agency perspectives and the need to increase capital for riskier portfolios to maintain solvency capital ratios and financial strength ratings. Solvency II will have a significant impact on the capital requirements associated with different investment strategies. Changing capital requirements are modeled as a variable that reflects changes in portfolio risk.
Benchmarks need to be rebalanced periodically for a number of reasons, including:
- Changes in a company’s business mix can change expected liability cash flows.
- Changes in the composition of market indices can occur as new securities are issued and other securities are removed.
- Management’s objectives may be modified as conditions change in markets for investments or insurance.
Management, rating agencies and regulators have concerns that may impose other constraints on the benchmark portfolio. Examples include liquid asset levels, average credit quality, yield objectives, asset allocation differences from peer group companies, and accounting issues.
Well constructed benchmarks are an essential part of the foundation for managing assets and creating value for insurance companies. They reflect a company’s liability profile and risk preferences. Combined with active risk limits, these benchmarks are used to define the boundaries within which an asset manager can generate returns. As passive portfolios, they illustrate a manager’s active risk exposures and identify the level and sources of excess return.
Benchmark construction is influenced by a wide range of factors. These include asset, liability, regulatory and tax considerations. The careful construction and use of a liability-driven benchmark has several benefits. It facilitates substantive communication between a company’s management and their portfolio managers and guides portfolio strategies to optimise shareholders’ risk-adjusted return profile.
“Quantitative Analysis of Fixed Income Portfolios Relative to Indices,” in Handbook of Portfolio Management (Frank Fabozzi), 1998.
Quantitative Management of Bond Portfolios, Lehman Brothers, 2001
Engineering LDI: Circumspect Pension Planning, Western Asset, 2008
- The asset-to-surplus ratio is 10 in our example. As of December 31, 2009, the ratio of general account assets to surplus was equal to 11 for the life insurance industry (source: ACLI tabulations of NAIC data), and 2.6 for the property/casualty industry (source: Insurance Services Office Limited).
- Asset classes are limited for simplicity. In practice, a wide range of fixed-income, equity and alternative asset class indices can be used.
- The objective function optimises excess return over the matched-duration risk-free rate. This isolates returns from spread duration, separating them from rate returns. The example constrains surplus duration to a maximum of 0.25 years, which ensures that the dollar duration of assets and liabilities is closely matched.
- It is assumed that cash is the “risk-free” asset for shareholder surplus. Return volatility is defined relative to the risk-free asset.