Abstract
AbstractWe introduce a set of new Value-at-Risk independence backtests by establishing a connection between the independence property of Value-at-Risk forecasts and the extremal index, a general measure of extremal clustering of stationary sequences. For this purpose, we introduce a sequence of relative excess returns whose extremal index is to be estimated. We compare our backtest to both popular and recent competitors using Monte Carlo simulations and find considerable power in many scenarios. In an applied section, we perform realistic out-of-sample forecasts with common forecasting models and discuss advantages and pitfalls of our approach.
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