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Non-parametric news impact curve: a variational approach

  • Matthieu GarcinEmail author
  • Clément Goulet
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Abstract

In this paper, we propose an innovative algorithm for modelling the news impact curve. The news impact curve provides a nonlinear 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. The technique we propose is directly inspired from noise removal techniques in signal theory. 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 modelling News impact curve Calculus of variations Wavelet theory ARCH 

Notes

Acknowledgements

This work was achieved through the Laboratory of Excellence on Financial Regulation (Labex ReFi) supported by PRES heSam under the reference ANR10LABX0095. It benefited from a French government support managed by the National Research Agency (ANR) within the project Investissements d’Avenir Paris Nouveaux Mondes (investments for the future Paris New Worlds) under the reference ANR11IDEX000602.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Human and animal rights

This article does not contain any studies with human participants or animals performed by any of the authors.

Supplementary material

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Léonard de Vinci Pôle Universitaire, Research centerParis La DéfenseFrance
  2. 2.LabEx ReFiParisFrance
  3. 3.University Paris 1ParisFrance

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