A fully non-parametric heteroskedastic model

Matthieu Garcin () and Clément Goulet ()
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Matthieu Garcin: Centre d'Economie de la Sorbonne & Natixis Asset Management, https://centredeconomiesorbonne.univ-paris1.fr
Clément Goulet: Centre d'Economie de la Sorbonne, https://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 a new model for estimating returns and volatility. Our approach is based both on the wavelet denoising technique and on the variational theory. We assess that the volatility can be expressed as a non-parametric functional form of past returns. Therefore, we are able to forecast both returns and volatility and to build confidence intervals for predicted returns. Our technique outperforms classical time series theory. Our model does not require the stationarity of the observed log-returns, it preserves the volatility stylised facts and it is based on a fully non-parametric form. This non-parametric form is obtained thanks to the multiplicative noise theory. To our knowledge, this is the first time that such a method is used for financial modelling. We propose an application to intraday and daily financial data

Keywords: Volatility modeling; non variational calculus; wavelet theory; trading strategy (search for similar items in EconPapers)
JEL-codes: C14 C51 C53 C58 (search for similar items in EconPapers)
Date: 2015-09
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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Persistent link: https://EconPapers.repec.org/RePEc:mse:cesdoc:15086

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