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Non-parametric news impact curve: a variational approach

Matthieu Garcin () and Clément Goulet ()
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Matthieu Garcin: Natixis Asset Management - SAMS, LABEX Refi - ESCP Europe
Clément Goulet: CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, LABEX Refi - ESCP Europe

Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) from HAL

Abstract: In this paper, we propose an innovative methodology for modelling the news impact curve. The news impact curve provides a non-linear relation between past returns and current volatility and thus enables to forecast volatility. Our news impact curve is the solution of a dynamic optimization problem based on variational calculus. Consequently, it is a non-parametric and smooth curve. To our knowledge, this is the first time that such a method is used for volatility modelling. Applications on simulated heteroskedastic processes as well as on financial data show a better accuracy in estimation and forecast for this approach than for standard parametric (symmetric or asymmetric ARCH) or non-parametric (Kernel-ARCH) econometric techniques.

Keywords: ARCH; Volatility modeling; news impact curve; calculus of variations; wavelet theory (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-for and nep-rmg
Date: 2017-02
Note: View the original document on HAL open archive server: https://halshs.archives-ouvertes.fr/halshs-01244292v3
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Published in 2017

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