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

Luca Spadafora, Francesca Sivero and Nicola Picchiotti

Abstract: 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.

Date: 2018-03, Revised 2018-03
New Economics Papers: this item is included in nep-ecm and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1803.07021

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