Best practice case study: Strengthening your understanding of balance sheet dynamics with fast and accurate what-if analysis, By Marco Hoogendijk, Managing Director Asia, Ortec Finance and Tessa Kuijl, Senior Specialist, Ortec Finance
Introduction
Shifting demographics, a persistent low interest rate environment and an increasingly volatile investment landscape are some of the key concerns for insurance companies. In this context, alternative assets are increasingly being considered across the globe as suitable diversifiers to increase returns and decrease overall volatility. Similarly, companies are considering a more dynamic approach to altering their Strategic Asset Allocation (SAA) dependent on capital buffers and market drivers. Understanding these new strategies, however, can take time, and as the market is increasingly showing signs of stress, it is imperative that the board acts swiftly.
In this paper, we discuss a best practice case study for insurance companies who face this challenge. What are the main solvency risk drivers? How severe are these, and what policy measures are the most effective to manage the risks in the current environment? For that purpose, we use realistic financial and economic scenarios in a fast calculating holistic balance sheet simulation platform that accommodates quick and robust insights to weather any upcoming stress situation.
Case study introduction
Running balance sheet simulations is time consuming and understanding the implications of various management actions is often limited by the technological infrastructure. Fast developing cloud solutions aimed at optimizing data and workflow automation enable insurance companies to produce high quality insights in an efficient way.
Supported by the right tools, the teams can easily evaluate the impact of numerous management actions – as outlined in Figure 1.These analyses will lead to a strengthened overall understanding of the dynamics within a certain product group or particular insurance fund and possibly even within different group entities.
In the subsequent sections, we illustrate such a journey for the following case:
- Insurance company: "with-profits" fund also known as a participating fund in the Asia region
- SAA: 30% Equity, 55% Fixed Income, 10% Property and 5% Cash
- Currency risks of foreign investments are fully hedged
- Healthy solvency position of ~240%.
- The duration gap is around 8 years.
Goal of the analysis is to understand the impact of a selective number of Management actions on overall capital generation via the payment of dividends to the shareholder fund or when the fund finds itself in stressed positions and hence requires capital injections.
For the analysis, we take a Monte Carlo engine that has the ability to simulate the full balance sheet, Solvency position, regulatory environment and cater to capital injections when the fund solvency position drops below a chosen threshold. All this with a run time under a half an hour to accommodate fast iterations and gain the desired insights along with the ability of the system to learn continuously. Fundamental to this analysis is having access to high quality real world scenarios that capture the stylized facts of the markets properly.
Our journey of discovery will cover the following steps:
- Analyze the current Investment portfolio
- Analyze the impact of changing the SAA based in chosen metrics
- Portfolio Optimization
Step 1: Analyze the current Investment portfolio
In figure 2, we see the relationship between cumulative annualized returns of the investment portfolio (on the horizontal axis) against total shareholder dividends paid over a period of 10 years (on the vertical axis) per scenario.
Figure 2 shows that for a chosen Return on Capital target there is wide variance in the associated investment returns. This very much highlights the path dependency of a participating business.
Now why would capital injections still be necessary even though the fund realizes an above average cumulative investment return? An obvious reason is the equity drawdown pushing the fund below the required regulatory solvency position.
A further investigation of the results by splitting the data into different cohorts provides additional insights. Figure 3 has split the scenario outcomes into 4 quadrants; each with its own economic circumstances.
Step 2: Analyze the impact of management actions
Given the results based on the current investment portfolio different management actions can be analyzed to optimize the Return on Capital against the risk of capital injections:
- Lowering the Equity allocation, replace equity with credits
- Hedging the equity tail risk or interest rate tail risk through derivatives
- Closing the duration gap
- Introducing new asset classes such as Infrastructure, Bank Loans or mortgages
Figure 4 captures the relationship between changes in the SAA with respect to a change in equity allocation and the impact on shareholder dividends, portfolio returns and related capital injections.
As the allocation of equity decreases, the variance in the different scenarios decreases as well. Although the average cumulative investment return is lower in this case, the number of capital injections is also diminished and the total dividend to shareholders is stabilized.
Changing the SAA is just one example of an option that is available to the management. The market is coming to grips with the fact that having the ability to analyze all these alternatives in detail requires organizations to model the business in a more efficient manner. Running analysis on a policy-by-policy basis does provide a granular understanding but at a real risk of losing significant time and missing the overall picture. The management and the board need to understand which action would lead to a robust and agile organization with the capabilities to respond to sudden market changes. For example having a Dynamic allocation as opposed to hedging your tail risk with equity options.
Step 3: Portfolio Optimization
One question remains: where do these portfolios lie with regards to the Efficient frontier? For this, one can rely on robust portfolio optimization algorithms, using consistent assumptions for risk and return and taking into account the path dependent nature of these calculations. For example, portfolios with different duration gaps and varying overall FX hedging percentages. This is in itself an elaborate study that is most certainly worth a dedicated article.
Conclusion
Tough times lay ahead as the industry transitions to a digital world.. Understanding your business and the inherent risks associated with different management actions is key and one that is expected by all stakeholders including policyholders. The foundation to this is having access to realistic scenarios, speed to generate quick and robust insights, and the ability to have proper what-if actions in place to weather any stress situations.
GLASS from Ortec Finance is a leading asset and liability management solution for institutional investors globally, providing simulation software to enhance strategic risk management and investment decision-making. Learn more about GLASS.
