Systematic Investing in Mortgage-Backed Securities
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We develop a key-rate-duration-matched (KRD) and option-adjusted-spread (OAS)-based expected return model for mortgage-backed securities (MBS) and use it to identify cross-sectional differences in MBS risk premiums.
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Our expected return model is tested in both physical MBS bonds and to-be-announced (TBA) forward contracts.
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Systematically constructed MBS and TBA portfolios overweighting securities with higher expected returns can reliably outperform the market.
The market for agency mortgage-backed securities (MBS), guaranteed by Fannie Mae, Freddie Mac, and Ginnie Mae, is one of the largest and most actively traded fixed income markets in the United States.1
A few features of MBS distinguish them from other asset classes within fixed income. First, as the agencies issuing agency MBS are either under US government conservatorship or fully backed by the US government, agency MBS are considered to have very low credit risk. However, MBS have meaningful embedded prepayment optionality since mortgage borrowers can pay back the underlying mortgage loans in full or a partial amount at any time. This optionality introduces uncertainty to the timing and size of MBS cash flows. For example, when interest rates and mortgage rates drop, a mortgage borrower can refinance at a lower rate to prepay the existing mortgage. As a result, MBS investors need to reinvest these earlier principal payments at lower rates. In addition to this prepayment risk, agency MBS, like other fixed income securities, are also exposed to interest rate risk. As interest rates rise, the value of MBS might drop as the expected future cash flows are discounted at higher rates.
Taking into consideration the embedded optionality and cash flow variability in MBS, we can decompose their expected return into the expected return of a key-rate-duration-matched (KRD-matched) Treasury portfolio and the option-adjusted spread (OAS). The OAS primarily reflects the prepayment risk premium, while the Treasury portfolio is likely to capture the duration risk premium.
Our model allows us to identify cross-sectional differences in MBS returns. Using constituent-level data of the Bloomberg US MBS Index, we find a positive and reliable relation between MBS expected returns and future realized returns.2
We also construct systematic portfolios that overweight segments of the MBS market with higher expected returns. Exhibit 1 shows portfolios targeting the top 60% to 90% of the MBS market with higher expected returns (denoted as 60%–90% market coverage) reliably outperform the overall market. On average, these portfolios have longer option-adjusted-duration (OAD) and wider OAS compared to the MBS market.
Systematic MBS Portfolios with Varying Levels of MBS Market Coverage
This exhibit reports simulated performance and characteristics of the MBS market and systematic MBS portfolios with different market coverage levels between June 2001 and June 2022. The MBS market is weighted by the market value of constituents in the Bloomberg US MBS Index. The X% coverage portfolio is constructed by sorting the MBS cohorts by expected return and selecting cohorts with the highest expected return such that their combined market value reaches X% of total MBS market value. The portfolio weights of the selected cohorts are rescaled by the cohorts’ market value to sum to 100%. Both the MBS market and systematic MBS portfolios are rebalanced monthly.
In addition to physical MBS, we also test our expected return framework using to-be-announced (TBA) forward contracts of MBS. TBA contracts account for over 90% of the agency MBS trading.3 By using TRACE TBA transaction data and rolling forward the monthly TBA contracts, we construct monthly returns for individual TBA cohorts and study their cross-sectional differences. For each TBA cohort, we also estimate TBA expected return as the market-value-weighted average expected return of the MBS constituents with the same issuing agency, loan term, and coupon rate.4
Using the TBA cohort-level data, we find a positive relation between MBS expected returns and TBA realized returns in the subsequent month, though the statistical reliability is weaker due to a shorter sample period and a smaller cross-section.5 Again, we can apply our expected return model to construct systematic portfolios that target segments of the TBA market with higher expected returns. Exhibit 2 shows that TBA portfolios with 60% to 90% market coverage reliably outperform the overall market.
Systematic TBA Portfolios with Varying Levels of TBA Market Coverage
This exhibit reports simulated performance and characteristics of the TBA market and systematic TBA portfolios with different market coverage levels between June 2011 and March 2022. The TBA market is weighted by the market value of MBS cohort mapped to TBA cohort by issuing agency, loan term, and coupon rate in the Bloomberg US MBS Index. The X%-coverage portfolio is constructed by sorting the TBA cohorts by expected return and selecting cohorts with the highest expected return such that their combined market value reaches X% of total TBA market value. The portfolio weights of the selected cohorts are rescaled by the cohorts’ market value to sum to 100%. The TBA returns are calculated using TBA data from TRACE, while OAD, OAS, and expected return are based on MBS cohorts that are mapped to TBA cohorts. Both the TBA market and systematic TBA portfolios are rebalanced monthly.
Last but not least, we test whether alternative variables such as MBS coupon rate and market state contain additional information about the cross-section of MBS expected returns. For example, high-coupon MBS expose investors to less duration risk but more prepayment risk compared to low-coupon MBS.6 The state of the MBS market, whether the majority of the MBS market is priced above par or below par, might also be related to the cross-sectional differences in MBS returns, due to investors’ preference for low-coupon or high-coupon MBS under different market environments.7 Accounting for the expected return differences between high- and low-coupon MBS, we do not observe a reliable relation between either the MBS coupon rate or current market state and future MBS returns.8 The information content about future differences in MBS returns is likely subsumed by market prices and expected returns using KRDs and OAS.
Overall, Dimensional’s research suggests that MBS investors can apply the KRD- and OAS-based expected return framework to identify cross-sectional differences in MBS term and prepayment risk premiums. We show that systematically constructed portfolios targeting higher expected returns reliably outperform the MBS market. This approach is also robust when we extend our expected return framework to the more liquid TBA market. Other MBS valuation metrics, such as coupon rate and market state, do not provide additional information about future differences in MBS returns beyond our expected return framework.
Footnotes
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1As of June 2022, MBS account for 28% of the value of securities in the Bloomberg US Aggregate Bond Index, with an average daily trading volume over $230 billion, second only to US Treasuries. Source: “US Mortgage Backed Securities Statistics,” SIFMA, June 2022.
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2See Section 5.1 and Table 3 in our MBS paper (“The Expected Returns of Agency MBS”, Dimensional Fund Advisors, 2023).
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3SIFMA, January 2020 through July 2022.
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4TBA also has the cheapest-to-deliver (W2D) feature, meaning the seller of TBA can deliver the cheapest MBS pool that matches the TBA criteria. Since we don’t have the historical W2D constituent data, we estimate TBA expected return as the market-value-weighted average expected return of matching MBS constituents.
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5See Section 5 and Table 3 in our MBS paper (“The Expected Returns of Agency MBS”, Dimensional Fund Advisors, 2023).
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6High-coupon MBS typically consist of mortgage loans with higher mortgage rates than the prevailing mortgage rate, giving the borrowers an incentive to refinance at lower rates (the prepayment option is “in the money”). All else equal, high-coupon MBS tend to have shorter durations than low-coupon MBS.
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7See Section 7 in our MBS paper (“The Expected Returns of Agency MBS”, Dimensional Fund Advisors, 2023), which has detailed examples such that an MBS investor may prefer lower-coupon MBS or higher-coupon MBS in different market states in order to manage the prepayment risk and its impact to overall portfolio duration.
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8For detailed regression specification and results, please refer to Section 7 and Table 10 in our MBS paper (“The Expected Returns of Agency MBS”, Dimensional Fund Advisors, 2023).
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