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
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)
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Published in 2017
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Persistent link: https://EconPapers.repec.org/RePEc:hal:cesptp:halshs-01244292
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