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This paper proposes a new integrated variance estimator based on orderstatistics within the framework of jump-diffusion models. Its ability todisentangle the integrated variance from the total process quadratic variationis confirmed by both simulated and empirical tests. For practical purposes, weintroduce an iterative algorithm to estimate the time-varying volatility andthe occurred jumps of log-return time series. Such estimates enable thedefinition of a new market risk model for the Value at Risk forecasting. Weshow empirically that this procedure outperforms the standard historicalsimulation method applying standard back-testing approach.

Jumping VaR: Order Statistics Volatility Estimator for Jumps Classification and Market Risk Modeling