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文章基本信息

  • 标题:A fully non-parametric heteroskedastic model
  • 作者:Matthieu Garcin ; Clément Goulet
  • 期刊名称:Documents de Travail du Centre d'Economie de la Sorbonne
  • 印刷版ISSN:1955-611X
  • 出版年度:2015
  • 出版社:Centre d'Economie de la Sorbonne
  • 摘要: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.
  • 关键词:Volatility modeling; non variational calculus; wavelet theory; trading strategy
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