Counterparty Risk: Implications for Network Linkages and Asset Prices
- Type of resource
- Stanford (Calif.) : Stanford Institute for Theoretical Economics, 2020
- Digital origin
- born digital
- 1 online resource
- online resource
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Since 1989, Stanford University's Department of Economics has hosted a series of workshop sessions in economic theory and mathematical economics. This program is known as the Stanford Institute for Theoretical Economics (SITE). Its purpose is to advance economic science for the benefit of society and to support cutting-edge work of economic theorists within specialized areas of research. The SITE Archives documents the workshop proceedings over time. Access to the presented papers is available in cases where the original material was provided by the author(s). This portion of the archive includes records describing papers where a copy of the original material is preserved and accessible.
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This paper studies the relation between trade credit, risk, and the dynamics of production network linkages. We find that firms that extend more trade credit earn 7% p.a. lower risk premia, and maintain longer relationships with their customers. We also document that suppliers with longerduration links to their customers command lower expected returns. We quantitatively explain these facts using a production-based model. Trade credit helps to hedge customers against liquidity risks, thereby reducing suppliers’ exposures to costs incurred in finding new customers. Overall, trade credit is informative about the lifespan of supplier-customer links, the production network’s density, and macroeconomic risk.
- Presented at SITE on July 16, 2020
- Session series
- Asset Pricing, Macro Finance, and Computation
- Organizer of meeting:
- Judd, Kenneth, Pohl, Walter, Schmedders, Karl, Wilms, Ole
- This session focuses on recent advances in asset pricing and macro finance as well as the use of computational techniques in these areas. Possible topics include but are not limited to the following: investor heterogeneity, learning and ambiguity, new preference structures for pricing models, or using machine learning to understand the cross-section of returns. As the analysis of such models often requires the use of computational methods, we encourage submissions that develop and make use of new numerical techniques.
- Stanford Institute for Theoretical Economics
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- This publication is open for research use. Copyright is retained by the author(s) or their heir(s).