The Level and Quality of Value-at-Risk Disclosure by Commercial Banks

By Christophe Pérignon, Daniel R. Smith. 2010

368 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/81ecb94b20efe99922f1f8526cacc90d02f50f65

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Citations in the corpus

Consumer Credit Risk Models Via Machine-Learning Algorithms (2010, background): “The role of the banking sector in the current financial crisis has underscored the need for better risk measures among large financial institutions (see Pérignon and Smith (2010) for a recent critique of traditional measures reported by commercial banks).”
Developing a stress testing framework based on market risk models (2008, positive): “To capture variation in the bid-ask spread we refer to the analysis of Plerou et al. (2005) and Angelidis and Benos (2006) and assume a log-linear relationship between spread and volatility.”
Model uncertainty and VaR aggregation (2013, background): “A very informative overview on the use of VaR technology within the banking industry is Pérignon and Smith (2010).”
Do banks overstate their Value-at-Risk? (2008, methodology): “In a recent survey, Pérignon and Smith (2007) show that historical simulation is at the heart of most proprietary models currently used by commercial banks.”
Linear Algebra and its Applications (2010, background): “5 A recent survey found that, of the 65% of firms that disclose their value-at-risk methodology, 73% used historical simulation, see [25].”
Evaluating Value-at-Risk Models with Desk-Level Data (2011, neutral): “Following industry practice (see Perignon and Smith (2006)) and the approach used by the bank that provided us with the VaR data in Figure 1, we rely on Historical Simulation or “bootstrapping”.”