In our recent interview, Hans Sterte, a seasoned economist, and Senior Partner at House of Reach, shares his rich insights from a career spanning over three decades in the realms of economics, asset management, and strategic investment. Tracing his journey from government institutions to leadership roles in major Swedish pension funds, Hans delves into the intricacies of Asset and Liability Management (ALM) within life insurance companies, highlighting its evolution and current challenges.
I am an economist by training, and began my professional career some 35 years ago at central government institutions like the Central Bank and the Ministry of Finance. During the last 20 years, I have been the Chief Investment Officer on large pension funds in Sweden. Besides economics and financial markets, my interests include geopolitics, history, and sports.
Asset and Liability Management (ALM) is a practice used by financial institutions to find and mitigate financial risks resulting from a mismatch between assets and liabilities.
ALM strategies take a view of the total balance sheet and are thus a combination of risk management and strategic asset allocation. It also includes the modelling of liabilities as well as the regulatory framework and capital requirements.
To me, the primary objective of ALM is that it helps the life insurance institution in question to cover its liabilities in an effective way and with a high degree of confidence. By doing so it will create a larger surplus that you could manage to maximize the future expected return. More realistically than maximizing, it helps the institution produce a “robust” portfolio which could be expected to deliver good returns at various probable future scenarios.
It is hard to specify a specific “tool” that has been most critical to creating an effective ALM process in a life insurance company. It has more to do with the mindset. Instead of managing each risk separately based on the type of risk involved, ALM is a coordinated process that looks at an institution's entire balance sheet. Since there are stark differences between different life insurance companies it is hard to specify a particular tool as most critical for all organizations.
However, if I could pick one tool that is important to all ALM work in a life insurance/pension company it would be a good functioning economic scenario generator (ESG). Since ALM is a long-term strategy, including forward-looking projections and datasets, the ESG is the heart of the ALM process.
This could be an extensive list, depending on what level of aggregation you choose to answer the question. At the highest level of aggregation, I would like to point out three main risks. These are the mismatch in duration between assets and liabilities, the total market risk in the asset portfolio and the currency risk stemming from holdings both on the asset and liability side.
The typical asset classes are government bonds, credits, and public equities. Other common asset classes are real estate, private equity, infrastructure, and catastrophe bonds.
The risk and benefits of asset classes usually go hand in hand. Higher expected risk gives higher expected returns for an asset class. For instance, the expected risk for government bonds is lower than for equities. However, that extra risk in equities is compensated for with a higher expected return.
On a portfolio level, one would try to choose a mix of asset classes that maximize expected return given a certain desired risk level in the balance sheet. If you mix assets that have low correlation with each other, the total return becomes more stable over time.
There are asset classes that are not dependent on financial markets. Due to that, these asset classes have a low correlation to the rest of the portfolio and could help you to increase the expected return for a given risk level. Examples of these types of asset classes are catastrophic bonds and mortality risk.
The most pressing constraints depend on the insurer in question. Since there is an immense difference between life insurance companies, no general framework could apply to all. The regulatory constraints must always be fulfilled, and the capital constraints must be met.
The choice of building an internal model or not depends on what you would gain in terms of lower capital requirements. If the benefits are low, you should use the standard model since it is much easier to implement. On the other hand, if the benefits are considerable, it is worthwhile building your own internal model. An internal model for market risk is more precise, but the extent of this precision varies significantly between different life insurers.
The increased demand from the regulatory side, as well as from the customers, has made it necessary for life insurance companies to integrate ESG factors in their ALM process. Up until now, it has been a challenging task to do due to lack of data, but that is about to change. New regulations, like the EU Taxonomy, will increase the data available on environmental issues and thus enable it to be used in ALM modelling. In the future, I expect that much more data will be produced which can be used to better model all ESG factors in an ALM model.