Constructing Quantitative Credit Portfolios with Factors: Lower Turnover, Lower Trading Cost in Levered Long-Short Portfolios
Mentioned in this paper:
MSCI Multi-Asset Class (MAC) Model | MSCI USD High Yield Corporate Bond Index
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Factor-model-based mean-variance optimization is a long-established portfolio-construction technique, increasingly used in credit. Until recently, though, liquidity constraints made optimization impractical for credit. The advent of electronic credit trading, basket trading and credit ETFs has changed that, making such strategies more practical and considerably cheaper to run.
That makes the choice of risk model an important question. Our analysis shows that a factor risk model matters most inside credit-portfolio optimization through its impact on turnover and trading costs — an advantage that compounds for levered portfolios, where every unit of turnover is amplified.
This paper tested four risk models to build minimum-variance portfolios from the MSCI USD High Yield Corporate Bond Index constituents. While the four models delivered comparable realized volatility, the trading costs diverged significantly.
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Annualized net implementation cost decomposed into levered trading cost (red, upward) and leverage benefit (green, downward). Black line shows net cost. Variants sorted by ascending net cost. Assumptions: volatility target 3%, gross alpha 250 basis points, half-spread 50 basis points, no leverage cap.
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