FORECASTING MULTIFRACTAL VOLATILITY PDF

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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Calvet Adlai Julian Fisher.

Forecasting multifractal volatility

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. We introduce a discretized version of the model volatiliity has a finite state space and allows for an analytical solution to the conditioning problem. Paper This paper develops analytical methods to forecast the distribution of future returns for a new continuous-time process, the Poisson multi-fractal.

If Rorecasting recognized a reference but did not link an item in RePEc to it, you can help with this form. Calvet, Laurent Fisher, Adlai.

Corrections All material on this site has been provided by the respective publishers and authors. Laurent-Emmanuel Calvet 1 Adlai J. Download full text from publisher File URL: 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. We assume for simplicity that the forecaster knows the true generating process with certainty but only observes past returns.

Forecasting multifractal volatility

The challenge in this environment is long memory and the corresponding infinite dimension of the state space. RePEc uses bibliographic data supplied by the respective publishers. Help us Corrections Found an error or omission?

We introduce a discretized multifracttal of the model that has a finite state space and an analytical solution to the conditioning problem. Stern School of Business. 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.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: It can be interpreted as a stochastic volatility model with multiple frequencies and a Markov latent state. It can be interpreted as a stochastic volatility model with multiple frequencies and a Markov latent state.

Monday, December 17, – 4: Laurent-Emmanuel Calvet 1 AuthorId: Have you forgotten your login? General contact details of provider: Full 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. You can help correct errors and omissions. The process captures the thick tails, volatility multifrsctal and moment scaling exhibited by many financial time series.

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More about this item Statistics Access and download statistics.

The process captures the thick tails, volatility persistence, and moment scaling exhibited by many financial time series. If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. It also allows you to accept potential citations to this item that we are uncertain about. Forecasting Long memory Multiple frequencies Stochastic volatility Weak convergence. 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.

The challenge in this environment is long memory and the corresponding infinite dimension of the state space.

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.

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