Local Parametric Estimation in High Frequency Data

Yoann Potiron and Per Mykland

Papers from arXiv.org

Abstract: In this paper, we give a general time-varying parameter model, where the multidimensional parameter possibly includes jumps. The quantity of interest is defined as the integrated value over time of the parameter process $\Theta = T^{-1} \int_0^T \theta_t^* dt$. We provide a local parametric estimator (LPE) of $\Theta$ and conditions under which we can show the central limit theorem. Roughly speaking those conditions correspond to some uniform limit theory in the parametric version of the problem. The framework is restricted to the specific convergence rate $n^{1/2}$. Several examples of LPE are studied: estimation of volatility, powers of volatility, volatility when incorporating trading information and time-varying MA(1).

Date: 2016-03, Revised 2018-08
New Economics Papers: this item is included in nep-ecm and nep-mst
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Citations: View citations in EconPapers (6)

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Journal Article: Local Parametric Estimation in High Frequency Data (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1603.05700

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