Capital-aware active management

Introduction

Solvency regulations are becoming increasingly sophisticated. Traditional investment management processes struggle to deliver optimal solutions for insurance portfolios. This has produced capital-inefficient portfolios.

Insurers have therefore turned to quantitative models that seek to create optimal insurance asset portfolios in terms of solvency capital requirements. However, these portfolios are not necessarily attractive from an investment perspective. They may be capital efficient, but they may not deliver the best returns for a specific level of risk.

Investors need a more holistic approach, combining active asset management and solvency capital modelling. In this way, investors can benefit from an investment process that is informed by, but not determined by, capital rules.

We call this approach ‘capital-aware active management’.

Executive summary

What is the investment issue?

Investors managing insurance assets in a regulated environment face two independent objectives. First, they must seek the optimal balance between risk and return for their investments. Second, they must seek the optimal balance between expected return and the portfolio’s incurred capital charge.

As insurance regulations have become more sophisticated, detailed and risk-sensitive, insurers have focused on the second of these objectives. These regulations are quantitative in nature, involving calculations based on historical data. This has led insurers to turn to algorithmic optimisation methods to provide the ‘optimum’ portfolio strategy.

However, these quant models are backwards-looking. In particular, they rely on rating agencies assessment of risk for corporate bonds. These offer a ‘stale’ measure of risk. Credit rating agencies do not update their ratings in real time to capture ever-changing fundamentals. By contrast, a bond investor using fundamental analysis will incorporate expected changes in ratings in the price they are willing to pay.

As such, these purely quantitative approaches do not provide scope for an active manager to add value. In this paper, we show how active managers can add value through fundamental analysis, while still managing the capital charge: through ‘capital-aware active management’.

How does the author tackle the issue?

The author applies a two-stage screening process to a universe of corporate bonds. First, he selects bonds that are attractive from a fundamental perspective. Second, from this reduced universe, he selects bonds that are attractive from a capital charge perspective. He then compares the performance of the resulting portfolio with the overall universe.

He uses the BAML Sterling Corp. Index to illustrate his approach. This index included 1,004 different bonds from 391 issuers, as at 30 September 2019. He calculates the expected return for each security, assuming that the investor will hold the bond to maturity. And he calculates the solvency capital requirement for each security.

In the first step, the author selects the overweight positions selected by the fundamental approach. He uses a random approach to select the overweight positions, for illustrative purposes. This active process identified 195 as ‘buys’, with a total of 530 bonds outstanding between them.

In the second step, he screens out 25% of the bonds that were least capital efficient, leaving 397 bonds. He then calculates the excess return (matching-adjustment benefit) and the capital charge (SCR) for the overall universe and for the selected portfolio.

What were the findings?

An equal-weighted allocation to all bonds in the index resulted in an excess return of 7.7% and a capital charge of 11.2%. An equal-weighted portfolio of the bonds selected by the screening process increased excess return to 8.8%, with a reduced capital charge of 10.1%. So, capital-aware active management has improved the return to risk-capital ratio.

What are the investment implications?

The author uses the example of an insurer operating under Solvency II. However, the principles presented are more general. The same thinking can be applied to any insurer operating in a regulated solvency capital system looking to use an active investment process.

To be clear, there is a place for a robust quantitative framework in the investment process. These frameworks allow both portfolio managers and insurers to calculate the highest achievable expected return for each level of a portfolio’s incurred capital charge.

However, these quantitative tools, and the metrics they embed, are not sufficient to deliver a genuinely optimal portfolio for an insurance investor. To provide the greatest value, they need to be combined with fundamental analysis based on expert judgement – a capital-aware active approach.

 

 

 

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Authors
David Roseburgh,
Investment Director, Liability-Aware Insurance, Multi-Asset Solutions

Gavin Donnelly,
Investment Manager, Liability-Aware Insurance, Multi-Asset Solutions

 

The limits of quantitative
models for insurance
investments

Chapter 1

Regulatory solvency regimes determine the amount of capital that insurance companies must hold to support the risks within their asset portfolios. In recent years, these regulations have become increasingly sophisticated, detailed and risk-sensitive.

The evolution of solvency regimes

A purely quantitative approach cannot be relied on to deliver a genuinely optimal investment solution for insurers.
  • 2006: The Swiss Solvency Test
  • 2013: Australia’s LAGIC solvency capital approach
  • 2016: Solvency II in the European Union (EU)
  • 2016: Bermudan Solvency Capital Requirement; enhanced in 2019

Today, a number of Asian countries are developing risk-sensitive, probabilistic approaches to solvency risk capital. A similar process is underway in Canada.

The International Association of Insurance Supervisors is consulting on an International Capital Standard in a bid to establish common ground. This would aim to deliver a detailed, risk-sensitive capital assessment method for all insurers.

All of these assessments – as prescribed by regulators – are formulaic, using calculations typically based on historical data.

Insurance investors can incorporate these assessments into algorithmic optimisation methods. These methods calculate the efficient frontier from a risk capital perspective – the highest achievable expected return for each level of a portfolio’s incurred capital charge.

This is particularly effective for fixed-income portfolio construction, where measures of risk, expected return and capital requirements of individual securities are straightforward to calculate.

Quantitative models: positives and pitfalls

These quantitative approaches appeal to both asset owners and asset managers. For insurance companies, they offer the ability to optimise portfolios to meet their specific needs. For asset managers, these models allow them to demonstrate the value of their proposed investment solution.

However, this regulatory capital-optimisation approach may not deliver a genuinely optimal investment solution for insurers. Its focus on maximising or minimising capital-driven metrics places a heavy reliance on regulatory capital calculations.

While the capital formula may be more sophisticated than before, it cannot provide a perfect, or even a reasonable, representation of all forms of risk. It seeks out solutions that work, for now, according to the capital formula. However, these solutions are not necessarily attractive from an investment perspective.

In addition, this quantitative approach does not provide scope for the asset manager to add value using fundamental analysis.

Active managers construct portfolios that reflect their active investment views, as agreed with clients in investment mandates. They use forward-looking judgements alongside analysis of historical data. Yet how can they add value when the investment process is an algorithmic function of the regulatory capital formula?

In this paper, we seek to answer this question. We illustrate how capital-sensitive insurance companies can apply active asset management — even ones that operate under a regulatory solvency capital regime that is sophisticated, detailed and risk-sensitive.

 

 

 
A purely quantitative approach cannot be relied on to deliver a genuinely optimal investment solution for insurers.
 

Capital-aware active
management

Chapter 2

We start from two basic premises. First, the insurer is seeking to benefit from an active investment approach. Second, the insurer prefers a lower capital charge to a higher one. ‘Optimised’ solutions tend to focus on the latter.

In this paper, we propose an alternative approach that addresses both of these objectives.

Case study: A fixed-income portfolio under Solvency II

The ideas, logic and principles behind this approach are general, allowing active views to be incorporated in a range of capital-regulated regimes.

We explore this idea using a bond portfolio of an insurance firm, regulated under Solvency II.

We focus on the trade-off between capital and expected return that arises from the credit risk within the portfolio, specifically with respect to the solvency capital incurred to offset the credit risk of the bonds.

We also look at the expected return generated in excess of an equivalent risk-free bond portfolio.

In this simple case, an insurer uses quantitative algorithmic optimisation without taking any subjective investment views (beyond the assumption that the published regulatory parameters are reasonable).

For each bond, the algorithm needs:

  • some objective measure of expected excess return (e.g. the gross credit spread on the bond minus its Solvency II Matching Adjustment fundamental spread)

  • its capital charge (which, in Solvency II, is the credit spread solvency capital requirement (SCR) prescribed by the Standard Formula, or an insurance firm’s own Internal Model).  

The algorithm calculates the portfolio that maximises some objective function, such as the ratio of expected excess return to solvency capital, or the expected excess return less the cost of the capital.

Insurance investors can constrain the algorithm in various ways, including:

  • a specified duration target
  • a specified cashflow profile
  • maximum exposure to any single issuer
  • limits on the total allocation to different credit ratings
  • a target SCR or expected return.

Chart 1 compares two key metrics for all 1,004 bonds in the BAML Sterling Corporate Index (as at 30 September 2019). The x-axis is the Solvency II Standard Formula credit spread, the SCR of the bond. The y-axis is a measure of the (capitalised) expected excess return of a bond, assuming it is held to maturity.1

The chart shows us that the trade-off between the expected excess return of a bond and its regulatory capital requirement can vary substantially. This is music to the ears of quantitative investment analysts. It means there is scope to generate a vast range of apparently ‘optimal’ solutions, all supported by compelling charts and statistics.

However, this does not help asset owners. They must now judge whether the proposed solution is indeed optimal in the true sense of the word – providing the best solution to their investment needs. In reality, these needs go beyond minimising regulatory capital.

For example, an equal-weighted allocation to all 1,004 bonds in the index results in a capitalised excess return of 7.7% and a capital charge of 11.2%. This gives a ratio of 0.68 for the capitalised expected excess return to SCR.

For each £100 of capitalised return, the insurer would need to source £145 of capital for a net capital consumption of £45. However, if the insurer instead invested only in the 182 bonds sitting above the x = y line, they could more than double that ratio, from 0.68 to 1.41, resulting in net capital generation.2

However, there are problems with this algorithmic approach. For example, it doesn’t consider liquidity and transaction costs. More fundamentally, the capital metric driving the portfolio construction is based on a ‘stale’ measure of risk.

The Solvency II credit capital charge calculation incorporates the credit rating of the bond. Yet credit rating agencies do not update their ratings in real time to capture ever-changing fundamentals. By contrast, a bond’s price will incorporate any changes in its credit rating expected by the market.

Until that rating change is formally effected, however, the bond will continue to appear to the algorithm as an ideal asset. If a downgrade is expected, an algorithmic process will build a portfolio that delivers a very strong metric at the date of the optimisation, but one that deteriorates a few months later.

Applying active management

By contrast, an active fixed-income investor can use fundamental analysis to anticipate the outlook for the credit rating of the bond issuer. He or she can incorporate this forward-looking judgement into his or her investment decision-making process.

However, most active investment processes do not consider the regulatory capital treatment of bond holdings. Chart 1 indicates that a purely fundamental approach can result in an inefficient capital allocation for the insurance firm.

An active investment management process identifies the assets that the investor believes are fundamentally attractive, overweighting these positions. These preferred positions can provide the starting point for constructing a capital-aware active portfolio. Chart 2 highlights which of the bonds in Chart 1 belong in this ‘preferred’ subset. (We have used a random approach to select the overweight positions, for illustrative purposes.)

The chart shows there is no strong connection between the active process and the regulatory capital efficiency of the bonds. These two perspectives are independent of one another.

 
The ideas, logic and principles behind this approach are general, allowing active views to be incorporated in a range of capital-regulated regimes.

Capitalised expected return versus Solvency II Standard Formula Solvency Capital Requirement for BAML UK Corporate Index

SOURCE: BLOOMBERG, ABERDEEN STANDARD INVESTMENTS, 30 SEPTEMBER 2019

A third way: capital-aware active management

A capital-aware active management approach should incorporate both the active management strategy and the regulatory capital efficiency of the bonds. What could this process look like?

The insurance investor could, for example, apply the algorithmic return-on-capital optimisation process to the bonds selected by the active approach. For each asset, the investor replaces the expected returns derived from Solvency II fundamental spread assumptions with the fund manager’s expected return. This combines the active strategy with a capital-driven active investment strategy.

The robustness of this approach would depend on the risk sophistication of the capital formula. The investor can constrain the optimisation to mitigate against these concerns. But this does not address the basic issue.

The fact remains that an optimisation algorithm can only solve for the relatively simple capital formula. It does not incorporate other forms of risk. As a result, it may still deliver a portfolio that is not attractive or sensible from a more holistic risk perspective. The process is still being driven to ‘game’ the regulatory formula as much as possible. Is this the ‘optimal’ approach?

Refining the capital-aware active management process

An alternative starting point is to screen out the most capital-inefficient assets first. The active manager can then use this narrower universe to construct the portfolio in their usual way. This is a less demanding use of the regulatory formula.

The portfolio construction process does not depend on the formula providing an accurate risk measure for all assets. However, it does allow for the fact that regulatory capital is costly. Some assets, including ones that are attractive from an investment perspective, may be capital-inefficient.

This approach screens out assets that are best avoided by a capital-sensitive insurance asset-owner. We summarise these different processes in Diagram 1.

Diagram 1: Alternative investment processes: quantitative optimisation versus capital-aware active management

Source: Aberdeen Standard Investments, April 2020

We can illustrate this revised capital-aware active management strategy for a portfolio measured against the BAML Sterling Corp. Index used above. This index included 1,004 different bonds from 391 issuers, as at 30 September 2019. Of those 391 issuers, the active fixed income process identified 195 as active ‘buys’, with a total of 530 bonds outstanding between them. Screening out 25% of the bonds that were least capital efficient, left 397 bonds.

Chart 3 highlights the bonds that are both active ‘buys’ and the most capital efficient, as well as showing those screened out during this two-stage process.

As noted above, an equal-weighted allocation to all bonds in the index resulted in a matching-adjustment benefit of 7.7% and an SCR of 11.2%. An equal-weighted portfolio of these 397 capital-efficient bonds increased the matching-adjustment benefit to 8.8%, with a reduced SCR of 10.1%.

So, capital-aware active management has materially improved the return to risk-capital ratio. Of course, a purely quantitative approach can also offer this level of improvement or more, at least in the short term. But this would not incorporate the skill of the active investor in assessing the fundamental risk and return characteristics of investments. It would not include any forward-looking assessment of bond ratings.

In practice, an insurer using a quant approach must hope that the regulatory capital process is good at identifying cheap assets. Yet we know this is not the case.

Incorporating judgement

In this paper, we have made the case for incorporating an active approach based on a judgement of the fundamentals of investment. We believe that investors can add value by incorporating forward-looking assessments of business models, industry dynamics and a company’s management.

However, there are quantitative approaches to active management too, not just to risk capital management. Could insurance investors use a purely quantitative approach that combines active investment views with risk capital management, avoiding the need for judgement?

In practice, even quantitative approaches involve judgement. Investors must judge which quantitative models to employ. In addition, asset returns are not normally distributed, with negative outcomes occurring more often than expected under the assumption of normally-distributed returns. This is particularly true for the credit portfolios that dominate insurance portfolios.

These statistical properties, including higher moments such as skew and kurtosis, are hard to capture in an optimisation process. When they are included, they can result in portfolios that are not intuitively sensible in our experience.

Investors also require judgement when placing constraints on the optimisation process, such as setting limits on individual issuers. Today, integrating ESG analysis into the investment process is as good as mandatory. Yet there are no widely agreed sets of measures for ESG factors. Here too, investors must use their judgement.

We developed the example above in the specific context of Solvency II and credit risk. But the ideas, logic and principles behind it are more general. Insurance investors can adopt this approach to any case where our two basic premises apply – an active investment process and a sophisticated, detailed, risk-sensitive regulatory solvency capital regime.

Could the underlying principles of this paper be applied outside the world of regulatory constraints? Looking beyond solvency capital, investors can apply the same thinking to build a carbon-aware active management process. All they would require is some measure, or ranking, of carbon footprint. These too could be integrated into an active investment process. In Strategic Asset Allocation: ESG’s New Frontier, our Multi-Asset Research team set out how investors can adapt their strategy to address our changing climate in a disciplined way that avoids compromising expected returns. The principles set out in this paper allow investors to apply the same thinking to their underlying investments.

Conclusion

To be clear, there is a place for a robust quantitative framework in the investment process. These frameworks allow both portfolio managers and insurers to understand the art of the possible: the highest achievable expected return for each level of a portfolio’s incurred capital charge. They provide a valid solution where taking an active view is not possible or relevant. They can highlight the scope to enhance risk capital management of an existing, well-constructed asset portfolio.

However, these quantitative tools, and the metrics they embed, are not sufficient to deliver a genuinely optimal portfolio for an insurance investor.

To provide the greatest value, they need to be combined with fundamental analysis based on expert judgement – a capital-aware active approach.

 

 


In the context of Solvency II Matching Adjustment, this metric has particular importance since it determines how much the liability valuation can be reduced as a result of taking credit risk in the asset portfolio.

For this rather pathological example, the asset strategy would create £100 of capital through a reduction in liability valuation and require £70 to support it. In other words, this asset portfolio requires less risk capital to support it than it creates.


The views and conclusions expressed in this communication are for general interest only and should not be taken as investment advice or as an invitation to purchase or sell any specific security.

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