Optimal contract for asset trades: Collateralizing or selling?

https://doi.org/10.1016/j.finmar.2020.100590Get rights and content
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Highlights

  • Study the conditions under which assets are sold or used as collateral.

  • Secured loans reduce production of costly information about the assets.

  • Borrowers must compensate lenders for being exposed to a strategic default on secured loans.

  • Over-collateralization of secured loans reduces incentives of fraudulent asset appraisal.

Abstract

I study the conditions under which assets are sold or used as collateral. Secured loans can be optimal by reducing the lender's incentives to acquire costly information about the future value of collateral assets. Furthermore, when the borrower has incentives to falsify the assets' quality, the assets cannot be sold but can be used as collateral via over-collateralization, and secured loans are optimal. However, under secured debts, the borrower may default strategically. Thus, an asset sale can be optimal under some conditions. In the paper, I also provide a theoretic explanation for the negative correlations between interest rates and haircuts.

Keywords

Asymmetric information
Costly information acquisition
Fraud
Secured loan contracts

JEL classification

D8
D53
E0
E44
G12

Cited by (0)

1

I am especially grateful to Stephen Williamson for his advice and invaluable guidance. I would also like to thank Gaetano Antinolfi, Jason Donaldson, Fernando M. Martin, and Yongseok Shin for their valuable comments and suggestions. I have benefited from discussion with Costas Azariadis, Michele Boldrin, Narayan Bulusu, Armando Gomes, Christopher Waller, and Randall Wright, as well as with all seminar participants at 2018 Asian Meeting of the Econometric Society, 2018 Fall Midwest Macroeconomic Meetings, the 19th KEA International Conference, KAIST Business School, Korea University, Kyung Hee University, Seoul National University, Singapore Management University, Washington University in St. Louis, and Yonsei University. This work was supported (in part) by the Yonsei University Future-leading Research Initiative of 2017–22-0153 and Research Fund of 2018–22-0107.