  This paper proposes a new integrated variance estimator based on order
statistics within the framework of jump-diffusion models. Its ability to
disentangle the integrated variance from the total process quadratic variation
is confirmed by both simulated and empirical tests. For practical purposes, we
introduce an iterative algorithm to estimate the time-varying volatility and
the occurred jumps of log-return time series. Such estimates enable the
definition of a new market risk model for the Value at Risk forecasting. We
show empirically that this procedure outperforms the standard historical
simulation method applying standard back-testing approach.
