P
Priyank Gandhi
Researcher at Rutgers University
Publications - 30
Citations - 1054
Priyank Gandhi is an academic researcher from Rutgers University. The author has contributed to research in topics: Stock (geology) & Tail risk. The author has an hindex of 10, co-authored 27 publications receiving 914 citations. Previous affiliations of Priyank Gandhi include University of Notre Dame & University of California, Los Angeles.
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Journal ArticleDOI
Counterparty Credit Risk and the Credit Default Swap Market
TL;DR: In this paper, the authors examined how counterparty credit risk is actually priced in the CDS market and found that the magnitude of the effect is vanishingly small and is consistent with a market structure in which participants require collateralization of swap liabilities by counterparties.
Journal ArticleDOI
Counterparty credit risk and the credit default swap market
TL;DR: In this article, the authors examined how counterparty credit risk is actually priced in the CDS market and found that the effect of counterparty risk is vanishingly small and consistent with a market structure in which participants require collateralization of swap liabilities by counterparties.
Journal ArticleDOI
Size Anomalies in U.S. Bank Stock Returns
Priyank Gandhi,Hanno Lustig +1 more
TL;DR: In this paper, the authors uncover a size factor in the component of bank returns that is orthogonal to the standard risk factors, including small minus big, which has the right covariance with bank returns to explain the average risk-adjusted returns.
Journal ArticleDOI
Size Anomalies in U.S. Bank Stock Returns
TL;DR: In this paper, the authors uncover a size factor in the component of bank returns that is orthogonal to the standard risk factors, including small-minus-big, which has the right covariance with bank returns to explain the average risk-adjusted returns.
Journal ArticleDOI
Using Annual Report Sentiment as a Proxy for Financial Distress in U.S. Banks
TL;DR: In this article, the authors argue that current measures of bank distress find marginal value in predictive variables beyond a capital adequacy ratio and tend to miss extreme events impacting the entire sector.