Multilevel and Tail Risk Management

Journal of Financial Econometrics

35 Pages Posted: 1 Dec 2020

See all articles by Lynda Khalaf

Lynda Khalaf

Université Laval - Département d'Économique

Arturo Leccadito

affiliation not provided to SSRN

Giovanni Urga

Centre for Econometric Analysis, Faculty of Finance, Bayes Business School (formerly Cass), London, UK

Date Written: August 31, 2020

Abstract

e introduce backtesting methods to assess Value-at-Risk (VaR) and Expected Shortfall (ES) that require no more than desktop VaR violations as inputs. Maintaining an integrated VaR perspective, our methodology relies on multiple testing to combine evidence on the frequency and dynamic evolution of violations, and to capture more information than a single threshold can provide about the magnitude of violations. Contributions include a formal finite sample analysis of the joint distribution of multi-threshold violations, and limiting results that unify discrete and continuous definitions of cumulative violations across thresholds. Simulation studies demonstrate the power advantages of the proposed tests, particularly with small samples and when underlying models are unavailable to assessors. Results also reinforce the usefulness of CaViaR approaches not just for VaR but also as ES backtests. Empirically, we assess desktop data by Bloomberg on exchange traded funds. We find that tail risk is not adequately reflected via a wide spectrum of models and available measures. Results provide useful prescriptions for empirical practice and, more generally, reinforce the recent arguments in favor of combined tests and forecasts in tail risk management.

Keywords: Value-at-Risk, Expected Shortfall, Backtesting, CaViaR, Exchange-Traded Funds, Multiple Testing

JEL Classification: C12, C15, C58, G17, G32

Suggested Citation

Khalaf, Lynda and Leccadito, Arturo and Urga, Giovanni, Multilevel and Tail Risk Management (August 31, 2020). Journal of Financial Econometrics, Available at SSRN: https://ssrn.com/abstract=3703086

Lynda Khalaf

Université Laval - Département d'Économique ( email )

2325 Rue de l'Université
Ste-Foy, Quebec G1K 7P4 G1K 7P4
Canada
418-656-2131-2409 (Phone)
418-656-7412 (Fax)

Arturo Leccadito

affiliation not provided to SSRN ( email )

Giovanni Urga (Contact Author)

Centre for Econometric Analysis, Faculty of Finance, Bayes Business School (formerly Cass), London, UK ( email )

108 Bunhill Row
London, EC1Y 8TZ
United Kingdom
+44 20 7040 8698 (Phone)
+44 20 7040 8881 (Fax)

HOME PAGE: http://www.bayes.city.ac.uk/faculties-and-research/experts/giovanni-urga

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