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On the Efficacy of Shorting Corporate Bonds as a Tail Risk Hedging Solution

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  • Travis Cable
  • Amir Mani
  • Wei Qi
  • Georgios Sotiropoulos
  • Yiyuan Xiong

Abstract

United States (US) IG bonds typically trade at modest spreads over US Treasuries, reflecting the credit risk tied to a corporation's default potential. During market crises, IG spreads often widen and liquidity tends to decrease, likely due to increased credit risk (evidenced by higher IG Credit Default Index spreads) and the necessity for asset holders like mutual funds to liquidate assets, including IG credits, to manage margin calls, bolster cash reserves, or meet redemptions. These credit and liquidity premia occur during market drawdowns and tend to move non-linearly with the market. The research herein refers to this non-linearity (during periods of drawdown) as downside convexity, and shows that this market behavior can effectively be captured through a short position established in IG Exchange Traded Funds (ETFs). The following document details the construction of three signals: Momentum, Liquidity, and Credit, that can be used in combination to signal entries and exits into short IG positions to hedge a typical active bond portfolio (such as PIMIX). A dynamic hedge initiates the short when signals jointly correlate and point to significant future hedged return. The dynamic hedge removes when the short position's predicted hedged return begins to mean revert. This systematic hedge largely avoids IG Credit drawdowns, lowers absolute and downside risk, increases annualised returns and achieves higher Sortino ratios compared to the benchmark funds. The method is best suited to high carry, high active risk funds like PIMIX, though it also generalises to more conservative funds similar to DODIX.

Suggested Citation

  • Travis Cable & Amir Mani & Wei Qi & Georgios Sotiropoulos & Yiyuan Xiong, 2025. "On the Efficacy of Shorting Corporate Bonds as a Tail Risk Hedging Solution," Papers 2504.06289, arXiv.org.
  • Handle: RePEc:arx:papers:2504.06289
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    References listed on IDEAS

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    1. Daniele Bianchi & Matthias Büchner & Tobias Hoogteijling & Andrea Tamoni, 2021. "Corrigendum: Bond Risk Premiums with Machine Learning [Bond risk premiums with machine learning]," The Review of Financial Studies, Society for Financial Studies, vol. 34(2), pages 1090-1103.
    2. Manconi, Alberto & Massa, Massimo & Yasuda, Ayako, 2012. "The role of institutional investors in propagating the crisis of 2007–2008," Journal of Financial Economics, Elsevier, vol. 104(3), pages 491-518.
    3. Daniele Bianchi & Matthias Büchner & Andrea Tamoni, 2021. "Bond Risk Premiums with Machine Learning [Quadratic term structure models: Theory and evidence]," The Review of Financial Studies, Society for Financial Studies, vol. 34(2), pages 1046-1089.
    4. Bai, Jennie & Bali, Turan G. & Wen, Quan, 2019. "Common risk factors in the cross-section of corporate bond returns," Journal of Financial Economics, Elsevier, vol. 131(3), pages 619-642.
    5. Valentin Haddad & Alan Moreira & Tyler Muir, 2021. "When Selling Becomes Viral: Disruptions in Debt Markets in the COVID-19 Crisis and the Fed’s Response [Funding value adjustments]," The Review of Financial Studies, Society for Financial Studies, vol. 34(11), pages 5309-5351.
    6. Patrick Houweling & Jeroen van Zundert, 2017. "Factor Investing in the Corporate Bond Market," Financial Analysts Journal, Taylor & Francis Journals, vol. 73(2), pages 100-115, April.
    7. O'Hara, Maureen & Zhou, Xing (Alex), 2021. "Anatomy of a liquidity crisis: Corporate bonds in the COVID-19 crisis," Journal of Financial Economics, Elsevier, vol. 142(1), pages 46-68.
    8. David B. Brown & Bruce Ian Carlin & Miguel Sousa Lobo, 2009. "On the Scholes Liquidation Problem," NBER Working Papers 15381, National Bureau of Economic Research, Inc.
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