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LARGE DEVIATIONS OF THE THRESHOLD ESTIMATOR OF INTEGRATED (CO-)VOLATILITY VECTOR IN THE PRESENCE OF JUMPS

Hacène Djellout () and Hui Jiang ()
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Hacène Djellout: LMBP - Laboratoire de Mathématiques Blaise Pascal - UCA [2017-2020] - Université Clermont Auvergne [2017-2020] - CNRS - Centre National de la Recherche Scientifique
Hui Jiang: Department of Mathematics [Nanjing] - NUAA - Nanjing University of Aeronautics and Astronautics [Nanjing]

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Abstract: Recently a considerable interest has been paid on the estimation problem of the realized volatility and covolatility by using high-frequency data of financial price processes in financial econometrics. Threshold estimation is one of the useful techniques in the inference for jump-type stochastic processes from discrete observations. In this paper, we adopt the threshold estimator introduced by Mancini where only the variations under a given threshold function are taken into account. The purpose of this work is to investigate large and moderate deviations for the threshold estimator of the integrated variance-covariance vector. This paper is an extension of the previous work in Djellout Guillin and Samoura where the problem has been studied in absence of the jump component. We will use the approximation lemma to prove the LDP. As the reader can expect we obtain the same results as in the case without jump.

Keywords: Quadratic variation; Large deviation principle; Threshold estimator; Jump Poisson (search for similar items in EconPapers)
Date: 2018
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-mst
Note: View the original document on HAL open archive server: https://hal.science/hal-01147189
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Published in Journal of Theoretical Probability, 2018, Journal of Theoretical Probability, 31 (3), pp.1606-1624

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