This article investigates some of the challenges faced by insurers when choosing new investment strategies to increase their yields.
Fixed income has long been an attractive asset class for insurance companies – they provide predictable cash flows that can be easily used to match expected liability cash flows. In the current low rate environment, however, the returns may be too small to enable insurers to meet minimum guaranteed returns. So many insurers are now increasingly looking to take on additional credit risk to increase their yields.
Of course, investing in credit risk is not something new – most insurers are active in the corporate bond market. In fact, you could argue that credit risk is at the heart of many fixed-type payment products, such as the multi-billion pound UK annuity market. There, insurers have traditionally matched the relatively predictable cash flows of annuities with a diverse portfolio of corporate bonds. As they are able to plan when the cash flows are needed, they do not need liquidity; and so, they can buy and hold these assets until maturity. Traditionally, the higher yield on corporate bonds has been partly shared with the customers, providing a win-win situation.
One of the reasons for the delay in Solvency II was the increased lobbying on behalf of the industry to ensure that this business model was not penalized. As Solvency II is built on the mark-to-market principles of market consistency and calculates capital on a one-year horizon, many insurers using this business model found they were penalized under the initial drafts. The results of this lobbying was the introduction of the Matching Adjustment, which provides capital relief for insurers managing a pool of credit risky assets that match fixed liability type products. Some observers, including the regulators themselves, believe that insurers will start to offer products that take advantage of the capital relief offered for the held-to-maturity backing of liabilities.
The current low interest rate environment, coupled with a favorable treatment under Solvency II, has led many insurers to revisit their asset allocation strategy in favor of credit risk. However, outside the US, bond markets are still dominated by financial institutions and very large corporates. So insurers have started looking elsewhere, including direct lending to corporates and financing long-term infrastructure projects. These asset classes require a long-term commitment of capital and are, by their very nature, even more illiquid than corporate bonds. At the right price, these are perfect investments for insurers, but rather costly for banks – who have been forced to reconsider their liquidity requirements following the events of the past few years and under Basel III. This is not new – a similar This is not new – a similar process is already happening in economies where there are thinly traded bond markets, such as South Africa. There, the leading insurers all actively take part in direct lending throughout the region. Also, the largest insurers in the UK had negotiated favorable maturity dates with firms issuing bonds to ensure they could be used to best match their liabilities. While a move to these new asset classes makes sense, it comes with a set of challenges that any insurer will need to overcome in order to effectively manage the risks.
Lending directly to companies and projects is very different than buying a bond over the counter. Organization is the first challenge an insurer will face when deciding to move into these new asset classes. This new organization will need to be able to do the following:
- Search and compete for deals in a competitive market place. This is neither easy nor inexpensive – it relies on strong networks, competent sales people, and it is time intensive to create these deals.
- Originating deals requires an assessment of risks. This entails setting up origination processes and governance. It requires that insurers process documents, assess applications, challenge assumptions, and record their decisions.
- Ensuring the terms of a deal match the needs of the insurer. For example, infrastructure loans are typically on a floating basis to match the needs of banks, but insurers would prefer a fixed rate to better match their liability profile.
- Work is not finished once the deal is done. Any lender will need to implement processes for dealing with loans that borrowers have problems paying. Can the deal be restructured to reduce the risk of full default? In the event of default, are there collateral, guarantees, or other assets that can be sold to reduce the loss and what are the processes required for managing this?
In addition, an insurer needs to be able to measure and manage risk. The better it can do this, the more it will be able to maximize its returns for a given risk appetite. The main risk management challenges are the measurement of default risk and measuring and then managing portfolio risk, which are covered next.
What is the probability that the borrower will repay the loan? This is the fundamental credit risk question. Currently, many insurers have outsourced this question to the rating agencies.
To assess the bond markets, firms need the opinion of an expert and independent third party who will provide an opinion on the credit worthiness of a bond or issuer. These ratings have become so ingrained in the way the bond markets work that they appear directly in regulation.
For non-traded credit risk, there will be no rating to use, leaving insurers with the challenge of making their own assessments. Credit assessment has long been at the heart of corporate banking and Basel II allows banks to use an internal model to assess the probability of default. So there are a wealth of models and methods available for insurers to use.
For publicly traded companies, it is possible to use a Merton-type model to assess the probability of default from public information (e.g., book value of liabilities and market price).
A standard technique for assessing private companies is to use an econometric model that forecasts probability of default based on the past financial performance of the company. More sophisticated models incorporate additional information about the credit cycle into their assessment to take into account the fact the financial statements can quickly become stale. These models focus on financial ratios and are very similar to the fundamental analysis techniques long practiced by lenders, rating agencies, and equity analysts to understand company performance.
Banks will overlay a qualitative assessment on top of these purely quantitative models, allowing them to capture an assessment about the management, company prospects, and any other information they think are relevant. This qualitative overlay is typically built up from an internal knowledge of how to assess risk. This is similar to the manner in which the ratings agencies work. Over the past few years they have shared scorecards to help the market understand what makes up their ratings.
Assessing the credit risk of infrastructure and other project finance related lending is harder – these are typically special purpose vehicles set up for a given project and will have no historic performance to use to predict performance. The standard technique is to assess the project cash flows under a number of scenarios. As you would expect, there is a spectrum of modeling approaches. At the more complex end, simulations can be used to assess potential cash flow scenarios; and at the simple end, a scorecard can be used to capture the assessment.
Gathering all the relevant data to build and calibrate their internal models has been a significant challenge faced by the banks. As defaults are typically rare events, it has taken many years and large portfolios for banks to have enough data to build reliable models. Many banks continue to rely on vendor data to help them build, calibrate, and validate their internal models.
Measuring default risk allows lenders to think about the standalone risk of a given asset. However, assets are held together in a portfolio. Modern portfolio theory can also be applied to credit assets. The challenge with any portfolio model is to understand the correlation between assets. For credit assets, this is the pairwise default dependency between different assets. Using a granular correlation model allows risk managers to quantify concentration/ diversification.
An effective tool uses a granular correlation model to simulate joint credit events that capture the fat tails and asymmetrical loss distributions seen in credit portfolios. These loss distributions can be used directly within insurers’ Solvency II internal models, allowing them to capture concentration risk/ diversification benefits directly.
In addition to these new asset classes, all credit risk can be measured in the same way, including in government bonds and in concentrations understood and managed.
Banks use credit portfolios management tools to help them to not only understand the risk in their banking booking, but also to better manage their capital resources. Banks will use their portfolio models to set limits, price loans, and understand the risk-adjusted returns on their capital for new deals.
In summary, insurers need to carefully consider many things before increasing their exposure to this new asset class. Given the specter of an extended low-rate environment and what appears to be encouragement from the regulators, we expect this trend to continue.
Of course, there will be other ways that insurers can get involved without necessarily setting up a lending operation. One obvious change could be that as demand increases, corporates could start to issue more bonds. Another possibility would be for insurers and banks to work more closely together, either directly or via market makers. Here, the banks would originate the debt, but pass the risk on to the insurer, with the bank getting paid for its relationship and client management and the insurer paid for the liquidity they can bring. However, similar risk management structures have not been without their issues.
We continue to watch these developments with interest, but one thing is for sure – insurers interested in this asset class need to do more to build up their credit risk modeling expertise and models.
Yukyung is a member of the research team within the Credit Risk Analytics Group at Moody’s Analytics. She focuses on research projects related to fixed income and equity strategies for buy side and other clients. She contributed to developing the CreditEdge Alpha Factor and the firstEDF-based ETF launched by Ossiam. Her expertise is also utilized on other CreditEdge customized projects pertaining to asset managers. In addition, Yukyung is a co-author of various practical research papers. One of her papers was published in the Journal of Fixed Income. Before joining Moody's, Yukyung was at Lehman Brothers as a fixed income strategist in the asset allocation group.
Tony oversees the Moody’s Analytics credit analysis consulting projects for global lending institutions. An expert applied econometrician, he has helped develop approaches to stress testing and loss forecasting in retail, C&I, and CRE portfolios and recently introduced a methodology for stress testing a bank’s deposit book.
Addresses the challenges and opportunities in the global insurance sector, and how they impact the risk management practices of insurers.
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