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This paper presents the first methodological proposal of estimation of the Λ V a R . Our approach is dynamic and calibrated to market extreme scenarios, incorporating the need of regulators and financial institutions in more sensitive risk measures. We also propose a simple backtesting methodology by extending the V a R hypothesis-testing framework. Hence, we test our Λ V a R proposals under extreme downward scenarios of the financial crisis and different assumptions on the profit and loss distribution. The findings show that our Λ V a R estimations are able to capture the tail risk and react to market fluctuations significantly faster than the V a R and expected shortfall. The backtesting exercise displays a higher level of accuracy for our Λ V a R estimations.

Lambda Value at Risk and Regulatory Capital: A Dynamic Approach to Tail Risk