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A fully non-parametric heteroskedastic model

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 modeling. We propose an application to intraday and daily financial data.
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Contributor : Lucie Label <>
Submitted on : Tuesday, December 15, 2015 - 3:50:03 PM
Last modification on : Tuesday, November 17, 2020 - 11:18:17 AM
Long-term archiving on: : Saturday, April 29, 2017 - 3:15:33 PM


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  • HAL Id : halshs-01244292, version 1


Matthieu Garcin, Clément Goulet. A fully non-parametric heteroskedastic model. 2015. ⟨halshs-01244292v1⟩



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