Is Your Portfolio Exposed to Unnecessary Risk?
July 31, 2018
Traditional approaches to portfolio design are based on the concept that for each level of risk or volatility, there is an optimal combination of asset classes that produces the highest rate of return.
But how can an insurer know what is truly the right level of risk, what asset classes should be considered for inclusion and what will happen to the portfolio under market stress scenarios? What happens to a company’s key ratios under a stressed market scenario or a crisis? Will the portfolio help weather the storm or damage outcomes further?
In order to determine the optimal portfolio mix, traditional asset allocation models are built on a number of critical assumptions — but failing to examine these premises with a critical eye can leave your portfolio open to unnecessary risk.
Here are five common assumptions applied to asset allocation (see more here) — along with important reasons why they may not apply for every investment scenario.
Assumption 1: Return, volatility and correlation expectations can be developed using historical data
A common approach assumes the past 20 years of results — the financial crisis, falling interest rates and an unprecedented bull market — are an accurate predictor of the future. In reality, backward-looking numbers don’t always paint a full picture. Historical data is only one part of a holistic approach to assessing volatility and correlation expectations.
Assumption 2: Volatility, or standard deviation, is a comprehensive measure of risk
Insurers are in the business of risk management and therefore are well-positioned to understand that there are myriad risks for their business lines. The approach for their portfolio should be the same. Volatility, value at risk, BCAR, stressed surplus growth and credit losses are all examples of risk measures that should be evaluated for every portfolio design. Volatility alone is not enough to paint a full picture.
Assumption 3: The investable asset class universe should be identified before modeling
In order to model asset allocation mixes, one of the constraints is to identify which asset classes are to be used. This mistakenly assumes from the very start that some asset classes are inappropriate. However, it is difficult, if not impossible, to fully understand the trade-offs of different investments without evaluating all potential opportunities. While some assets will be eliminated due to state code constraints or other considerations, starting with the full opportunity set is critical to identifying how to most effectively meet your company’s objectives.
Assumption 4: Correlations between asset classes are stable over time and tell a more complete story of diversification
Correlations matter, but the financial crisis taught investors that correlations between asset classes are dynamic relationships and assumptions need to evolve. Asset classes should not be added to the portfolio mix based solely on the correlation to other holdings. In addition to correlation, asset classes should be added for what they actually do to help meet the portfolio objectives.
Assumption 5: Investors can hold a portfolio on the efficient frontier, and industry frictions are minor
This concept is unlikely (see more at “Is Your Portfolio Prepared For Tomorrow’s Insurance Challenges”) for insurers due to the complexity of the regulatory environment and the broad range of potential company priorities. For example, in Kansas, equity and high-yield exposure are significantly constrained. And in California, insurers are being called on to divest from investments in the carbon economy. Setting an objective of achieving a stable surplus growth of 5 percent is significantly different from targeting an annualized total rate of return of 5 percent. These differences matter — and they affect insurers’ portfolios in significant ways.
Review your key assumptions today, and whether your asset allocation approach is truly insurance focused. Help your portfolio weather market disruptions while still achieving business priorities.
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