No CrossRef data available.
Article contents
Consistent Backtesting Systemic Risk Measures
Published online by Cambridge University Press: 12 November 2025
Abstract
This article offers two novel backtests to evaluate the adequacy of well-known systemic risk measures such as CoVaR, MES, SES, and SRISK. Both the new backtests are robust to estimation risk (i.e., their null distributions remain invariant in the presence of estimation risk). While existing backtest is consistent against divergence from the null hypothesis up to a finite order, the article shows that the new backtests are fully consistent. The real-world implications brought by the new backtests are economically significant as they reveal significantly more cases of inadequate systemic risk modeling among the major financial institutions.
Information
- Type
- Research Article
- Information
- Copyright
- © The Author(s), 2025. Published by Cambridge University Press on behalf of the Michael G. Foster School of Business, University of Washington
Footnotes
This article benefited from comments and feedback provided by the editor, George Pennacchi, and by the referees, Olivier Scaillet and an anonymous reviewer.
References
Acharya, V.; Engle, R.; and Richardson, M.. “Capital Shortfall: A New Approach to Ranking and Regulating Systemic Risks.” American Economic Review, 102 (2012), 59–64.Google Scholar
Acharya, V. V.; Pedersen, L. H.; Philippon, T.; and Richardson, M.. “Measuring Systemic Risk.” Review of Financial Studies, 30 (2017), 2–47.Google Scholar
Adrian, T., and Brunnermeier, M. K.. “CoVaR.” American Economic Review, 106 (2016), 1705–1741.Google Scholar
Aielli, G. P. “Dynamic Conditional Correlation: On Properties and Estimation.” Journal of Business & Economic Statistics, 31 (2013), 282–299.Google Scholar
Bajgrowicz, P.; Scaillet, O.; and Treccani, A.. “Jumps in High-Frequency Data: Spurious Detections, Dynamics, and News.” Management Science, 62 (2016), 2198–2217.Google Scholar
Banulescu-Radu, D.; Hurlin, C.; Leymarie, J.; and Scaillet, O.. “Backtesting Marginal Expected Shortfall and Related Systemic Risk Measures.” Management Science, 67 (2021), 5730–5754.Google Scholar
Barras, L.; Scaillet, O.; and Wermers, R.. “False Discoveries in Mutual Fund Performance: Measuring Luck in Estimated Alphas.” Journal of Finance, 65 (2010), 179–216.Google Scholar
Berkowitz, J.; Christoffersen, P.; and Pelletier, D.. “Evaluating Value-at-Risk Models with Desk-Level Data.” Management Science, 57 (2011), 2213–2227.Google Scholar
Box, G. E., and Pierce, D. A.. “Distribution of Residual Autocorrelations in Autoregressive-Integrated Moving Average Time Series Models.” Journal of the American Statistical Association, 65 (1970), 1509–1526.Google Scholar
Breusch, T. S., and Pagan, A. R.. “The Lagrange Multiplier Test and its Applications to Model Specification in Econometrics.” Review of Economic Studies, 47 (1980), 239–253.Google Scholar
Brownlees, C., and Engle, R. F.. “SRISK: A Conditional Capital Shortfall Measure of Systemic Risk.” Review of Financial Studies, 30 (2017), 48–79.Google Scholar
Christoffersen, P. F. “Evaluating Interval Forecasts.” International Economic Review, 39 (1998), 841–862.Google Scholar
Du, Z., and Escanciano, J. C.. “Backtesting Expected Shortfall: Accounting for Tail Risk.” Management Science, 63 (2017), 940–958.Google Scholar
Engle, R. “Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models.” Journal of Business & Economic Statistics, 20 (2002), 339–350.Google Scholar
Enria, A. (2022, October 4). Better Safe than Sorry: Banking Supervision in the Wake of Exogenous Shocks. https://www.bankingsupervision.europa.eu/press/speeches/date/2022/html/ssm.sp221004~9c9e9504c2.en.html.Google Scholar
Fiorentini, G.; Sentana, E.; and Calzolari, G.. “Maximum Likelihood Estimation and Inference in Multivariate Conditionally Heteroscedastic Dynamic Regression Models with Student t Innovations.” Journal of Business & Economic Statistics, 21 (2003), 532–546.Google Scholar
Girardi, G., and Ergün, A. T.. “Systemic Risk Measurement: Multivariate GARCH Estimation of CoVaR.” Journal of Banking & Finance, 37 (2013), 3169–3180.Google Scholar
Glosten, L. R.; Jagannathan, R.; and Runkle, D. E.. “On the Relation Between the Expected Value and the Volatility of the Nominal Excess Return on Stocks.” Journal of Finance, 48 (1993), 1779–1801.Google Scholar
Gourieroux, C., and Zakoïan, J.-M.. “Estimation Adjusted VaR.” Econometric Theory, 29 (2013), 735–770.Google Scholar
Hong, Y. “Consistent Testing for Serial Correlation of Unknown Form.” Econometrica, 64 (1996), 837–864.Google Scholar
Kupiec, P. “Techniques for Verifying the Accuracy of Risk Measurement Models.” Journal of Derivatives, 3 (1995), 73–94.Google Scholar
Lazar, E., and Zhang, N.. “Model Risk of Expected Shortfall.” Journal of Banking & Finance, 105 (2019), 74–93.Google Scholar
Newey, W. K., and West, K. D.. “A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix.” Econometrica, 55 (1987), 703–708.Google Scholar
Rehn, O. (2020, June 26). ECB Is Better Safe Than Sorry with Easy Policy, Rehn Says. Reuters https://www.reuters.com/article/business/ecb-is-better-safe-than-sorry-with-easy-policy-rehn-saysidUSKBN23X125/#:~:text=FRANKFURT%20(Reuters)%20%2D%20It%20is,Olli%20Rehn%20said%20on%20Friday.Google Scholar
Leong supplementary material
Leong supplementary material
File
333.2 KB