Amba Zeggen & Marta Swiderska: Survival of the fittest!

Amba Zeggen & Marta Swiderska: Survival of the fittest!

Inflatie Solvency II ECB
Amba Zeggen & Marta Swiderska (foto archief Probability & Partners).jpg

By Amba Zeggen and Marta Swiderska, respectively Sector Lead Insurance and Consultant at Probability & Partners

In the past two years, key ECB rates have been rising after more than a decade of central bank policy to stimulate the economy by decreasing interest rates. The current fight of the ECB and other central bankers against inflation fuelled the increase in interest rates across various maturities. However, as of 2024, with decreasing inflation, the market eagerly awaits potential actions by the ECB.

Amid the current volatile interest rate environment and uncertainties surrounding ECB policies, now is the perfect time to assess the adequacy of the standard model Solvency II (SII) interest rate shocks. Is SII fit to survive this new interest rate environment? This is an important question because inadequate interest rate shocks could lead to insufficient capital levels.

To answer this question, we have investigated the adequacy of the SII interest rate shock and explored alternative interest rate models. The detailed approach of our work and results were recently published in our white paper, with below the most relevant conclusions for insurance companies and other financial institutions.

Background info on SII interest rate shocks

SII is the EU Solvency Capital Regulation for insurance companies. The regulation includes a standard model approach on how to calculate interest rate risk (the standard model interest rate shock). This prescribed shock includes a percentage change in interest rates, which is supposed to describe a 1-in-200-year event.

Shortly after SII came into force, a serious drawback was raised concerning the SII interest shock: in a low-interest-rate environment with negative rates, SII underestimates interest rate risk. In 2018, as a part of EIOPA’s second set of advice to the European Commission, EIOPA suggested using an alternative method to derive the SII interest shocks for a 1-in-200-year event, called the shifted approach. Additionally, the data range was extended to include 2009–2016 data.

For our research, we have analysed both the current SII standard model interest shock and the proposed improvement, the shifted approach.

Analysing SII shocks

To assess the adequacy of the current SII shocks and the shifted approach, we have compared these shocks to the predicted spot rate range identical to a 1-in-200-year event using Monte Carlo simulation with three different underlying methods: principal component analysis (PCA) on an updated data set, Hull-White, and Nelson-Siegel. We chose these theoretical methods to:

  • capture both forward- and backward-looking perspectives on interest rates, and
  • maintain a balance between model sophistication and simplicity of calibration.

The criteria for evaluation of the SII shocks and shifted approach included: performance, usability, and stability over time in low and high interest rate environments.

Key results

The SII interest rate shocks perform poorly in a low interest rate environment, because SII neglects the possibility of a downward movement of the spot rate curve in a negative interest rate environment.

The shifted approach method presented in EIOPA's advice to the European Commission results in a significant improvement in performance and is of comparable complexity to the current approach. However, the parameter values are not always adequate, because they overestimate ranges for short-term rates and underestimate ranges for long-term rates, and therefore should be reassessed.

The predicted spot rate ranges based on Hull-White are very narrow, especially in low interest rate environments, resulting in the actual data being more volatile than predicted. Therefore, the confidence intervals underestimate possible movements of the spot rate curve.

Because the Nelson-Siegel ranges are wider, the simulated ranges with Nelson-Siegel perform considerably better than those derived with Hull-White. However, Nelson-Siegel sometimes overestimates possible movements of the spot rate curve, and the width of the range can be influenced by the chosen in-sample period.

The PCA method, which is similar to Nelson-Siegel, has narrower confidence intervals, resulting in less overestimation of capital reserves.

Among the compared methods, PCA on an updated data set produces the best predictions.

Implications for Financial Institutions

Given the interest rate sensitivity of long duration (life) insurance companies, it is important to further investigate potential underestimation of interest rate risk using SII, which could lead to inadequate capital estimation. In the Own Risk and Solvency Assessment (ORSA), insurers could analyse the impact of different methods and/or data to identify the appropriateness of solvency shocks and potential shortfalls.

Our findings could be of interest to other sectors as well. For example, in banking, interest rates driving prepayments are often modelled using the Hull-White model. Prepayment estimations can be under- or overestimated if the scenario set is not ‘wide’ enough. This could impact the valuation of mortgage portfolios, capital requirements, and interest rate risk management.

Pension funds with comparable long duration liabilities can also benefit from our research, challenging chosen interest rate methods and scenarios within the ERB (Eigen Risico Beoordeling).

This time it’s different

The phrase ‘This time it’s different’ is often used to blur risk management consciousness and assure stakeholders that nothing can go wrong and everyone will win. We would like to borrow this sentence to alert you that this time it indeed is different and therefore requires another approach to interest rate risk management.

Our colleague Rens Borsje highlighted in his column that current market conditions and geopolitical developments necessitate a reassessment of risk management strategies. We would like to add something to this narrative and point out that it is also important to reassess interest-risk models, methods, and assumptions, to assess the impact on capital reserves, and to adapt to new market dynamics influenced by central banks and market expectations.

Based on our results, adaptation is certainly relevant for SII interest rate risk. But it is also important for any other interest rate model. In the words of Darwin: ‘It is not the strongest of the species that survives, nor the most intelligent that survives. It is the one that is most adaptable to change.’