  A new risk measure, the lambda value at risk (Lambda VaR), has been recently
proposed from a theoretical point of view as a generalization of the value at
risk (VaR). The Lambda VaR appears attractive for its potential ability to
solve several problems of the VaR. In this paper we propose three nonparametric
backtesting methodologies for the Lambda VaR which exploit different features.
Two of these tests directly assess the correctness of the level of coverage
predicted by the model. One of these tests is bilateral and provides an
asymptotic result. A third test assess the accuracy of the Lambda VaR that
depends on the choice of the P&L distribution. However, this test requires the
storage of more information. Finally, we perform a backtesting exercise and we
compare our results with the ones from Hitaj and Peri (2015)

Risk Management;Statistical Finance