On the Efficacy of Shorting Corporate Bonds as a Tail Risk Hedging Solution
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- 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.
- 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.
- Alberto Manconi & Massimo Massa & Ayako Yasuda, 2010. "The Role of Institutional Investors in Propagating the Crisis of 2007–2008," NBER Chapters, in: Market Institutions and Financial Market Risk, National Bureau of Economic Research, Inc.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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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This paper has been announced in the following NEP Reports:- NEP-INV-2025-05-05 (Investment)
- NEP-RMG-2025-05-05 (Risk Management)
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