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Non-parameteric news impact curve: a variational approach

Matthieu Garcin () and Clément Goulet ()
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Matthieu Garcin: Natixis Asset Management et LabEX ReFi
Clément Goulet: Centre d'Economie de la Sorbonne et LabEX ReFi, http://centredeconomiesorbonne.univ-paris1.fr

Documents de travail du Centre d'Economie de la Sorbonne from Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne

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: Volatility modeling; news impact curve; calculus of variations; wavelet theory; ARCH (search for similar items in EconPapers)
JEL-codes: C02 C14 C22 C51 C53 C58 C61 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-for and nep-ore
Date: 2015-09, Revised 2017-02
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Persistent link: https://EconPapers.repec.org/RePEc:mse:cesdoc:15086rr

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