Evaluating Value-at-Risk forecasts: A new set of multivariate backtests

Dominik Wied, Gregor N.F. Weiß and Daniel Ziggel

Journal of Banking & Finance, 2016, vol. 72, issue C, 121-132

Abstract: We propose two new tests for detecting clustering in multivariate Value-at-Risk (VaR) forecasts. First, we consider CUSUM-tests to detect non-constant expectations in the matrix of VaR-violations. Second, we propose χ2-tests for detecting cross-sectional and serial dependence in the VaR-forecasts. Moreover, we combine our new backtests with a test of unconditional coverage to yield two new backtests of multivariate conditional coverage. Results from a simulation study underline the usefulness of our new backtests for controlling portfolio risks across a bank’s business lines. In an empirical study, we show how our multivariate backtests can be employed by regulators to backtest a banking system.

Keywords: Model risk; Multivariate backtesting; Value-at-Risk; Systemic risk (search for similar items in EconPapers)
JEL-codes: C52 C53 C58 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jbfina:v:72:y:2016:i:c:p:121-132

DOI: 10.1016/j.jbankfin.2016.07.014

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