Evaluating value-at-risk forecasts
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Date
2015
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Abstract
We propose two new tests for detecting clustering in multivariate Value-at-Risk (VaR) forecasts. First, we consider
CUSUM-tests to detect first-order instationarities in the matrix of VaR-violations. Second, we propose χ 2-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. In all cases, a bootstrap
approximation is possible, but not mandatory in terms of empirical size and power.
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Keywords
Model Risk, Value-at-Risk, Multivariate Backtesting