FORECASTING MULTIFRACTAL VOLATILITY PDF

This paper develops analytical methods to forecast the distribution of future returns for a new continuous-time process, the Poisson multifractal. The process . of Technology. Chapter 7: Thoroughly revised version from Journal of Econometrics,. , L. E. Calvet and A. J. Fisher. ‘Forecasting Multifractal Volatility,’ pp. Calvet and Fisher present a powerful, new technique for volatility forecasting that draws on insights from the use of multifractals in the natural sciences and.

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Corrections All volatilkty on this site has been provided by the respective publishers and authors. As the grid step size goes to zero, the discretized model weakly converges to the continuous-time process, implying the consistency of the density forecasts. As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

General contact details of provider: This abstract was borrowed from another version of this item. Laurent-Emmanuel Calvet 1 AuthorId: The process captures the thick tails, volatility persistence, and moment scaling exhibited by many financial time series.

Forecasting Long memory Multiple frequencies Stochastic volatility Weak convergence. Full text for ScienceDirect subscribers only As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

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Forecasting multifractal volatility

Stern School of Business. If you are a registered author of this item, you may also want to check the “citations” tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation. RePEc mulgifractal bibliographic data supplied by the respective publishers. It can be interpreted as a stochastic volatility model with multiple frequencies and a Markov latent state.

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Calvet Adlai Julian Fisher. Please note that corrections may take a couple of weeks to filter through the various RePEc services. For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Calvet, Laurent Fisher, Adlai. Have you forgotten your login? If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form.

Paper This paper develops analytical methods to forecast the distribution of future returns for a new continuous-time process, the Poisson multi-fractal. This allows to link your profile to this item. forecastlng

Help us Corrections Found an error or omission? We assume for simplicity that the forecaster knows the true generating process with certainty but only observes past returns. Download full text from publisher File URL: See general information about how to correct material in RePEc.

Other versions of this item: This paper develops analytical methods to forecast the distribution of future returns for a new continuous-time process, the Poisson multifractal.

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We assume for simplicity that the forecaster knows the true generating process with certainty but only observes past returns. It also allows you to accept potential citations to this item that we are uncertain about.

Laurent-Emmanuel Calvet 1 Adlai J.

Friday, April 30, – 2: If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. You can mmultifractal correct errors and omissions. When requesting a correction, please mention this item’s handle: The challenge in this environment is long memory and the corresponding infinite dimension of the state space.

We introduce a discretized version of the model that has a finite state space and allows for an analytical solution to the conditioning problem.

Multifrractal references including those not matched with items on IDEAS More about this item Statistics Access and download statistics Corrections All material on this site has been provided by the respective publishers and authors. It can be interpreted as a stochastic volatility model with multiple frequencies and a Markov latent state.

As the grid size goes to infinity, the discretized model weakly converges to the continuous-time process, implying the consistsency of the density forecasts.