Consultants' Corner

Variable Annuity Hedging from an ERM Perspective

Brian Paton
Insurance Experts' Forum, October 4, 2012

Variable annuity (VA) risk management continues to be a critical issue for many insurers. Legacy books of business continue to provide significant risk exposure to many companies, and VA providers accordingly are trying to find a better balance among customer benefits, the risks companies have assumed, and expected returns for all parties.

The global financial crisis and subsequent economic developments have brought to light critical weaknesses in existing hedge programs. We describe below the opportunity to strengthen these hedge programs by placing them within a formal ERM framework.

Naturally, risk identification is the basis of established ERM frameworks. Accordingly, a VA hedge process should have as its foundation an identification of the underlying risks that VA guarantees introduce. Key fundamental aspects are as follows:

• There should be a clear articulation of the risks that the hedge program 1) is designed to mitigate and 2) does not intend to address (i.e., risks that remain unhedged). Market price performance (delta) and risk-free interest rate (rho) are typically important hedge program risks.

• However, many companies do not choose to hedge certain exposures (e.g., certain rho risk and certain funds such as real estate and emerging markets), although product design usually takes into account management of these risk exposures.

• Fund manager alpha is generally unhedgeable.

• Minimum withdrawal guarantees are subject to significant policyholder behaviour risk, which is unhedgeable in the capital markets. The assumptions companies make about when policyholders will make withdrawals and how this relates to moneyness of the option will have a significant impact on the effectiveness of the hedging program.

• Model risks related to variable annuity hedging can be significant, and a formal control process is necessary with model validation and testing.

In order to avoid any misinterpretation or expectation that the hedge will cover all losses, senior management needs to have a holistic understanding of the risks that 1) are being hedged, 2) impact the hedging process, and 3) are unhedged.

Clearly articulated risk management objective setting and measurement of performance is a vital component of applying an ERM framework to VA hedging. Key considerations include:

• Most companies’ design their hedge programs to reduce economic risk; statutory capital and GAAP earnings management tend to be secondary considerations. It is important that this measurement basis aligns with a company's risk appetite statement, and there should be clear articulation of any trade-offs (on other measurement approaches) when hedging economic risk.

• It is common to include statutory capital and GAAP earnings measures within risk appetite statements. If a hedge process mitigates economic risk, then the danger of the residual risk causing breaches in risk appetite on other metrics may remain. It is important to use stress and scenario testing to explore when the various appetite metrics could be breached. These breaches may occur when:

The impact on the appetite metric is different than the economic impact being hedged against;

   - Unhedged risks, such as policyholder behaviour, affect results;

   - Hedge inefficiency (e.g.,, basis risk), such as hedging emerging market funds with S&P futures; and,

   - Other areas, such as market discontinuity and liquidity issues.

• Insufficient stress testing of VA hedges undoubtedly contributed to underperformance during the financial crisis. In some cases, this resulted in under-hedging of vega risk and/or losses through intra-day market volatility.

• A robust hedge stress testing program considers the scenarios that introduce greatest hedge inefficiency to the risk appetite metrics and the likelihood of those scenarios.

• In order to quantify the impact of the hedge program on risk appetite, companies will need to consider their approach to risk measurement; if, as is highly likely, this uses a valuation model, then the valuation model itself introduces additional risk to the process.

Integrating the intended hedging process to the business planning cycle of the company is also an important consideration.

• Companies will need to take into account growing exposure through new sales, the impact of the product design on new guarantees, and likely future economic scenarios. Through such a process, an insurer can determine its ability to remain within its stated risk appetite over the planning period.

• If the plan breaches risk appetite, then the company can take actions in advance of assuming risks. These may include lowering future guarantees, reducing planned sales and/or restructuring of the annuity portfolio, and/or full or partial market withdrawal.

• In the past, variable annuity writers have seen how increasing guarantee levels, greater diversity of fund options, and increasing frequency of benefit ratchets have resulted in greater volatility and hedge inefficiencies. Therefore, promoting understanding of the impact of writing new variable annuity guarantees on the business plan is a crucial part of managing VA risk. Companies then can design guarantees that balance policyholder needs with pricing that reflects the risks issuers assume.

Variable annuity hedging is never complete and should continuously evolve.

• The hedging process should fit within the company’s wider risk management processes and corporate governance structure, and be well integrated within its business planning cycle. To do otherwise is to risk inconsistencies, unidentified exposures, and management misunderstandings (or even worse).

• Although insurers are paying close attention to VA hedging, many companies have not consistently aligned it with their wider risk management framework. This may reflect the fact that they are still in the process of developing and embedding VA hedging into their ERM initiatives.

We often find that when a VA hedge process needs strengthening, it indicates an underlying need to strengthen the overall risk management process. Thus, addressing gaps in VA hedge processes can be a catalyst to addressing risk management issues that may exist elsewhere within the ERM process. Rectifying these issues not only will benefit a company’s financial and risk profile, but also will help it better prepare for increasingly detailed rating agency discussions and regulatory examinations. 

Brian Paton is a director with PwC.

Readers are encouraged to respond to Brian using the “Add Your Comments” box below. He can also be reached at: brian.paton@us.pwc.com.

This blog was exclusively written for Insurance Networking News. It may not be reposted or reused without permission from Insurance Networking News.

The opinions of bloggers on www.insurancenetworking.com do not necessarily reflect those of Insurance Networking News.

 

Comments (0)

Be the first to comment on this post using the section below.

Add Your Comments...

Already Registered?

If you have already registered to Insurance Networking News, please use the form below to login. When completed you will immeditely be directed to post a comment.

Forgot your password?

Not Registered?

You must be registered to post a comment. Click here to register.

Blog Archive

A Cure for Analysis Paralysis

“Adaptive” analytics can help insurers keep up with the flood of real-time data.

To Quantify or Not — That is the Question with Modernization (Part II)

While the quantitative business case may be ingrained in many insurance operations, it often offers little practical use.

The Good, The Bad and The Ugly Of Enterprise BI

When IT can't deliver, business users build their own applications focusing on agility, flexibility and reaction times.

The IT-Savvy 10%

IBM survey reveals best practices of IT leaders.

The Software-Defined Health Insurer: Radical But Realistic?

Can a tech startup digitally assemble the pieces of a comprehensive, employer-provided health plan?

Data Governance in Insurance Carriers

As the insurance industry moves into a more data-centric world, data governance becomes more critical for ensuring the data is consistent, reliable and usable for analysis.