Portfolio managers have long understood that the best way to drive long-term performance is to implement asset allocation strategies with the best chance of maximizing returns for a given level of risk. And “fundamental risk models”—that focus on characteristics specific to individual companies, such as industry membership, market capitalization, and valuation measures—have become an increasingly popular tool to help investors understand and manage portfolio risks. These models are particularly useful for asset managers with portfolios consisting of a large number of disparate securities.
However, while fundamental models can be useful in practice, systems that measure risk solely based on firm-level characteristics can underestimate the macroeconomic risks inherent in asset classes and portfolios. Fiduciary Trust has identified seven macro-economic risk factors that can provide a valuable complement to traditional fundamental models. We believe that understanding asset class—and ultimately portfolio—exposure to these macroeconomic factors can help managers position their portfolios more effectively from a risk standpoint. Moreover, the FTC Macroeconomic Risk Model (FMRM), used in concert with fundamental risk models, can help investors and clients make better asset allocation decisions and enhance client portfolio returns.
The FTC Macroeconomic Risk Model Factors
We have identified seven macroeconomic risk factors that are important in explaining asset class returns. These factors are also intuitive and widely followed by market participants. Each is described below:
- Real Rate Factor – The average of U.S. real interest rates across maturities, calculated on a month-over-month basis to capture changes to that average rate over time
- Inflation Expectations Factor – The average of U.S. inflation expectations as implied by TIPS bonds across maturities, calculated on a month-over-month basis to capture changes to that average rate over time
- Commodity Factor – The average of monthly returns of the Dow Jones and Bloomberg commodity indices
- Term Factor – Month-over-month changes in the 10-year treasury rate minus the 2-year treasury rate
- Flight-to-Safety Factor – The average change of the U.S. Dollar, Japanese Yen, and Swiss Franc relative to global currencies
- GDP Factor – Month-over-month changes in the Atlanta Fed’s U.S. GDPNow series
- Market Factor – Monthly return of the MSCI All Country World Index
Example of the Model’s Risk Management Capability
The FTC Macroeconomic Risk Model calculates the relationships between dozens of asset classes and the seven macroeconomic factors highlighted above. Through linear regression analysis and other means, FMRM generates asset class risk estimates that can complement traditional fundamental risk models by offering greater context.
Consider the example in Exhibit A that compares the relative risks posed by two fixed-income ETFs. The bar charts on the left depict the projections from a fundamental risk model. They show the projected standard deviation of the iShares iBoxx $ High-Yield Corporate Bond ETF (ticker: HYG), which represents a portfolio of non-investment grade, high-yield corporate bonds, versus the iShares Core
U.S. Aggregate Bond ETF (ticker: AGG) designed to track the performance of the Barclays Aggregate Bond Index, which represents U.S. investment-grade bonds.
Most investors would contend that a high-yield bond portfolio is certainly riskier than a bond portfolio consisting of Treasuries and investment-grade corporate bonds and mortgages. Yet according to standard deviations projected by a well- known, widely used fundamental risk model, the opposite appears to be the case. We believe this might be because the bottom-up, fundamental model is overly sensitive to sector composition and maturities of the bonds underlying the portfolio. The average duration of HYG, for example, is roughly half that of the portfolio of AGG. Yet this fundamental analysis ignores the top-down credit risks associated with exposure to a non-investment grade high-yield portfolio. This is where the FTC Macroeconomic Risk Model comes into play, estimating a higher projected standard deviation for HYG than AGG and restoring the intuitive relationship.
A similar, but less-extreme example of this dynamic can be seen when comparing the risk projections for the iShares MSCI Emerging Markets ETF (ticker: EEM) with the iShares Russell 1000 ETF (ticker IWB), which invests in large-cap U.S. equities (Exhibit B). In this case, the fundamental risk model projects a higher risk level for EEM than IWB, which is in line with conventional wisdom. Yet the differential is relatively small, given that EEM represents companies based in the developing world while IWB is comprised of 1,000 of the largest U.S. stocks. The top-down-focused FMRM is able to detect exposures inherent in emerging market stocks that increase their riskiness, and those projected standard deviations are meaningfully higher than they are for large U.S. equities.
We do not believe one approach is inherently better than the other, but instead recommend that asset allocators use both risk model methodologies to generate portfolio statistics that provide a more complete picture of the risk exposures that exist in their portfolios.
The macroeconomic variables calculated in the FMRM, along with asset returns, can be used to create risk and tracking error estimates for major asset classes and most individual stocks. Additionally, the macroeconomic risk model comes with scenario analysis capabilities, including stress-testing portfolios in historical and hypothetical scenarios. When used in conjunction with other systems and methodologies, FMRM can help investors understand their portfolio risks and exposures more clearly and ultimately lead to better investment outcomes.
Investors have become increasingly interested in utilizing financial software to help them understand portfolio risks and market scenarios that could impact returns. We view this as a positive development. But as demonstrated above, many risk models and systems that are based on fundamental, firm-level characteristics can underestimate risks within a portfolio because they do not capture the macroeconomic exposures inherent in asset classes.
The FTC Macroeconomic Risk Model helps resolve this issue. Used in conjunction with more traditional fundamental risk models, FMRM can help investors and clients make better asset allocation decisions.
Please contact us if you think this type of analysis would be helpful to you.
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