@workingpaper{PS2018,
title = {Using the Extremal Index for Value-At-Risk Backtesting},
author = {Peter N Posch and Philipp Schmidtke and et al.},
url = {http://hdl.handle.net/2003/37201},
doi = {10.17877/DE290R-19196},
year = {2018},
date = {2018-02-15},
abstract = {We introduce a new value at risk forecasting backtest by establishing a connection between the independence property of value at risk forecasts and a general measure of extremal clustering of stationary sequences (extremal index). We introduce a sequence of relative excess returns whose extremal index has to be estimated. We compare our backtest to both popular and recent competitors using Monte-Carlo simulations and find considerable power. For example, the new test allows the rejection of the popular unconditional Historical Simulation method more frequent.},
keywords = {Extremal Index, VaR Backtest},
pubstate = {published},
tppubtype = {workingpaper}
}
We introduce a new value at risk forecasting backtest by establishing a connection between the independence property of value at risk forecasts and a general measure of extremal clustering of stationary sequences (extremal index). We introduce a sequence of relative excess returns whose extremal index has to be estimated. We compare our backtest to both popular and recent competitors using Monte-Carlo simulations and find considerable power. For example, the new test allows the rejection of the popular unconditional Historical Simulation method more frequent.
