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1 August 2016 CFA Institute Journal Review

Factor Approach to Fixed Income Allocation (Digest Summary)

  1. Paras Gupta

A risk factor–based approach can be used for managing fixed-income portfolios. The authors show that a limited set of factors—rate, growth, and volatility—explain the return on fixed-income portfolios. Investors can use this approach in managing and analyzing their portfolios and in incorporating their macro views into their asset allocation decisions.

What’s Inside?

The authors assert that various assets earn risk premiums because they are exposed to risk factors and that understanding and carefully modeling these risk factors is critical to portfolio construction and risk management. They outline the application of a systematic factor approach to fixed-income investment and risk management. The authors show that a set of only a few factors explains the return on fixed-income portfolios. Using this set of factors, investors can diagnose the exposures in their portfolios in order to help them seek returns and reduce risk.

How Is This Research Useful to Practitioners?

The authors argue the merits of a risk factor–based framework for managing fixed-income portfolios. This approach helps reduce the dimensionality of the problem to a small set of factors that explain most of the risk and return of the different asset classes. The factor approach provides a framework for assessing portfolio risk and makes it possible to link the few key risk factors to economic variables, enabling portfolio managers to construct portfolios that reflect their economic views.
The authors classify these fixed-income risk factors into three broad areas: rate, growth, and volatility. The rate factor explains a substantial portion of the variation in returns across many fixed-income sectors. It also explains 60%–80% of the variation in returns for investment-grade credit as well as a smaller portion of the variation in returns for high-yield (HY) and emerging-market (EM) indexes.
Based primarily on expectations of future economic growth, the growth factor drives risk premiums in fixed-income portfolios. It links with default and liquidity risks, the key compensation drivers for corporate bonds, and drives returns on such fixed-income sectors as HY and EM much more than the rate factor does.
The volatility factor takes into account the volatility in risky assets and its impact on various fixed-income sectors, including HY and EM, which are more correlated with risky assets and exhibit negative returns in risk-off scenarios. Sovereign bonds become safe havens and provide protection during such risk-off scenarios.
The authors show that a combination of these three factors explains more than 70% of the variation in returns for most fixed-income indexes. Understanding the sensitivity of portfolios to these three factors allows investors to reposition their portfolios on the basis of their views/forecasts and the prevailing market conditions.

How Did the Authors Conduct This Research?

The authors use proxies for the three factors and run an exposure analysis for key fixed-income indexes. They proxy the rate factor with the Barclays 10-year bellwether Treasury, the growth factor with a 50/50 blend of the MSCI World Equity Index and the US HY Excess Returns Index, and the volatility factor with the monthly change in the CBOE Volatility Index (VIX).
The authors use five years of data (October 2010–September 2015) and run a standardized regression test to compute the exposures as a function of the standardized betas of the three factors.
The results show that EM and HY indexes are driven mainly by the growth factor, whereas US aggregate, global aggregate, and US Treasury Inflation-Protected Securities are driven mainly by the rate factor.

Abstractor’s Viewpoint

The authors offer a simplifying risk factor–based approach for evaluating fixed-income allocation. It is useful for creating a balance between return-seeking and risk-mitigating portfolio assets. The complexity of both financial markets and financial instruments is increasing, which means that evaluating factor exposures will become increasingly dynamic and will require regular evaluations to identify new exposures and to explain the power of existing ones.

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