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May 2014

At the heart of the Own Risk and Solvency Assessment (ORSA) is the forward-looking solvency assessment. This article discusses how insurers should look beyond the next year, and build a capability to project solvency capital requirements under a range of scenarios – helping link capital with strategic business decisions.

Over the past year it has been apparent how global the ORSA has become, as it has been embraced across Europe, Asia, and the US. Indeed, the ORSA concept will be implemented in many countries before Solvency II actually goes live. For example, in North America the National Association of Insurance Commissioners (NAIC) has mandated that insurers must have an ORSA in place by January 1, 2015. Another example is the South African Financial Services Board‘s (FSB) Solvency Assessment and Management (SAM) framework, which includes ORSA requirements based not only on European Insurance and Occupational Pensions Authority (EIOPA), but also experiences from the Canadian regulator (OSFI), the Australian Prudential Regulatory Authority (APRA), the Bermuda Monetary Authority (BMA), and International Association of Insurance Supervisors (IAIS) principles.

The ORSA is viewed as an absolutely critical part of an insurer’s risk management and is featured strongly in their model developments. The ORSA is a generic requirement – it is not intended to be a prescriptive regulatory calculation, but instead asks firms to describe how they manage risk and capital across their enterprise. The reference to “own” in the title highlights the fact that the ORSA is meant to reflect the unique risk management characteristics and profile of an insurer.

From looking at the guidance published by EIOPA, NAIC, and OSFI, it is clear that the ORSA should include:

  • Identification and assessment of all material risks
  • Sufficiency of capital to cover those risks on a forward-looking basis
  • A risk management framework to monitor and control risk
  • A risk management culture embedded within the business to support decision-making

The building blocks for the ORSA are typically the insurer’s existing risk and capital processes. The insurer should have a sound approach for risk identification and, alongside that, have a set of risk appetite statements consistent with this and approved by the board. The ORSA should encompass all material risks, including those not listed in the Solvency Capital Requirement (SCR). Insurers should consider additional risks types, such as model risk, strategic risk, reputational risk, and regulatory risk. Also, the risk identification should evaluate the major risks facing the company; not just now but over the business-planning period, which is typically a three to five year horizon.

Figure 1. Global map of ORSA regulations
Global map of ORSA regulations
Source: Moody's Analytics

Quantitative modeling capabilities needed

Many insurance firms have identified a range of quantitative modeling capabilities they will need to support the objectives of the ORSA. These might include:

  • Real-time monitoring of current regulatory capital requirements.
  • The firm’s own assessment of the economic capital requirements of the business. This could be calculated under a definition of capital that is specific to the business and different from regulatory capital requirements, such as Solvency II Pillar I’s one-year 99.5% VaR capital or the CTE 90 run-off capital used in the US principle-based approach to reserving and capital.
  • A capability to make a multi-year projection of the insurer’s business plan under a range of different financial and business scenarios, with an assessment of the solvency requirements generated in those scenarios.

The last point refers to a key principle of the ORSA, namely that it should look beyond developments in the next year – meaning the ORSA must be forward looking. This requires a capability to project solvency capital requirements under a range of scenarios. This helps link capital with the big strategic decisions on the running of a company. It also presents an interesting technical challenge – how to take the current “time zero” solvency calculations and develop them to understand how capital behaves not only today, but how it may evolve over time – across a range of different scenarios including, of course, potential adverse economic scenarios.

Depending on the nature of the assets and liabilities, the projection can be very complex. Typically, capital requirements are assessed using stochastic simulation. Whether the capital requirement is one-year VaR or CTE run-off, a similar technical challenge arises – a large number of stochastic simulations are in theory needed to measure the capital requirements not just in current conditions, but also at several future time steps in a number of scenarios.

Figure 2. Solvency Capital Requirements under a range of scenarios
Solvency Capital Requirements under a range of scenarios
Source: Moody's Analytics

This general projection process involves two distinct stages:

1. Determine the multi-year scenarios in which the business is to be projected. These could be stochastic scenarios, but are more likely to be a handful of deterministic scenarios. In both cases, the scenario model is typically done at a “macro” level. Even when the macroeconomic scenarios have been selected, mapping them to an insurer’s individual risk exposure is not always straightforward.

2. Calculate the capital requirements along the scenario paths (i.e., the capital metric). There may be more than one metric, such as the business may be interested in the regulatory, economic, and ratings capital.

This highlights an important rule in a well-functioning principle-based risk assessment – the risk management strategy must drive the risk measurement methodology, rather than vice versa.

Projection scenarios

For multi-year scenarios, insurers can decide whether to use thousands of stochastic scenarios or a handful of deterministic scenarios. The stochastic approach will allow firms to examine the robustness of their business across a wide range of future possible economic outcomes. However, it is a significant computational challenge to assess future capital levels in thousands of different scenario paths, particularly where the business includes complex liabilities that are valued stochastically. Firms are therefore using deterministic scenarios in the projection of their business for the ORSA. The difficulty is then how to select the scenarios. Firms will need to decide on the appropriate stress scenarios given the nature and risk exposure of the business. There is no standard set of stress conditions that all insurers should run. The scenarios could be:

  • Top-down macroeconomic scenarios capture the insurer’s systematic exposures to adverse economic and financial market outcomes; for example, stresses expressed as a fall in equity markets, movements in interest rates, and changes in credit spreads. These top-down macroeconomic scenarios are perhaps easiest to envisage. Identification of the scenarios may rely on the input of economists to help pinpoint the key risks to the global economy. These are likely to be thematic scenarios, such as a global depression, war in the Middle East, disorganized euro sovereign debt default, and so on. Typically, the modeling of such scenarios would first involve some expert economic judgment on what their impact would have on macroeconomic factors (e.g., inflation, GDP growth, or corporate profit margins). Then econometric models would be used to estimate how unexpected shocks to those macroeconomic variables could impact high-level financial market behavior, such as equity returns and yield curves.
  • Bottom-up scenarios are specific to the insurer based on their risk exposures, arising from their unique strategic and operational profile (e.g., unexpected legal liabilities or operational failures).
  • Systematic insurance risk scenarios could include unexpected increases in longevity or pandemics.

The specific risk exposures and risk management strategies of a firm may place particular demands on the type of scenarios that are required in the prospective solvency assessment process. This will naturally impact the relevant financial and economic variables that need to be projected, but it may also impact the level of detail of the modeling. For example, if a firm's risk management strategy involves the weekly rebalancing of a significant delta hedging strategy, its risk robustness will not be well assessed using annual projection time steps in its multi-year projection. Rather, it will be important to capture the specific risks left behind by their risk management strategy – in this case, it will have exposure to high volatility in weekly equity returns, rather than overall weakness in annual equity performance. This highlights an important rule in a well-functioning principle-based risk assessment: the risk management strategy must drive the risk measurement methodology, rather than vice versa.

Figure 3. Typical ORSA output
Typical ORSA output
Source: Moody's Analytics

Calculating future capital requirements

The ORSA requires the insurer to project its future position, including its projected economic and regulatory capital, to assess its ability to meet the regulatory capital requirement. This is often computationally challenging, particularly where the liabilities have embedded options and guarantees. Therefore, every valuation requires a full set of risk-neutral economic simulations. If a firm were to consider, say, five stress tests each with five annual steps, capital requirements would need to be reassessed in 25 different scenarios. The Standard Formula is also calculation-intensive as each capital requirement calculation will require multiple stressed market-consistent liability valuations. For Solvency II firms intending to calculate the capital requirement produced by their internal model SCR methodology, the complexity is compounded by the need to create an algorithmic description of how their internal model methodology and its implementation is applied in a wide range of different scenarios.

Proxy modeling is a possible solution to making the demanding problem of projecting capital requirements more manageable. The industry has been through a learning process in the area of proxy modeling as applied to the one-year VaR calculation. The advanced techniques that have been developed to solve the one-year VaR calculations, such as Least-Squares Monte Carlo (LSMC), are naturally extendable to calculating capital requirements over a longer horizon. Although technically challenging, we expect firms to start developing multi-period capital proxy functions that are capable of describing how capital requirements behave over multiple time horizons as a function of multiple risk factors.

ORSA output

If an insurer has established a methodology and process to implement the prospective requirements of the ORSA, then alongside a stress testing framework, the output from the ORSA can feature firmly in decision-making within the business. Figure 3 is a typical ORSA output.

Although it is time-consuming to derive and quantify the impact of the scenarios, it can be insightful for an insurer to go through the process of discussing possible scenarios, their financial impacts, and possible management actions. The output from stress testing may suggest that it is necessary to reduce specific risk exposures – possibly by hedging risk or potentially exiting product lines. Reverse stress testing should also be performed to identify and quantify those scenarios that could result in business failure, breach of economic solvency, breach of SCR and Minimum Capital Requirement (MCR), and other circumstances considered appropriate by senior management and the board.

It is correct to refer to the ORSA as a process, but it should be viewed as more than that. The output from the ORSA and the insights it provides should trigger management actions and decisions. For instance, the ORSA should play an important part in the running of the business. At the heart of the ORSA is the forward-looking solvency assessment. For this reason, we recommend that firms invest in the development of a robust methodology to project the balance sheet, supported by a forward-looking stress testing framework.

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