FORECASTING MULTIFRACTAL VOLATILITY PDF

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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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The challenge in this environment is long memory and the corresponding infinite dimension of the state space. Calvet, Laurent Fisher, Multifeactal. Other versions of this item: This allows to link your profile to this item.

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See general information about how to correct material in RePEc. 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. This abstract was borrowed from another version of this item. It can be interpreted as a stochastic volatility model with multiple frequencies and a Markov latent state.

Laurent-Emmanuel Calvet 1 Adlai J. More about this item Statistics Access and download statistics. This paper develops analytical multifravtal to forecast the distribution of future returns for a new continuous-time process, the Poisson multifractal.

We assume for simplicity that the forecaster knows the true forwcasting process with certainty but only observes past returns. For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: The process captures the thick tails, volatility persistence, and moment scaling exhibited by many financial time series.

Forecasting multifractal volatility

Monday, December 17, – 4: If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form. We introduce a discretized version of the model that has a finite state space and an analytical solution to the conditioning problem.

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Forecasting Long multifracta, Multiple frequencies Stochastic volatility Weak convergence. It also allows you to accept potential citations to this item that we are uncertain about.

Laurent-Emmanuel Calvet 1 AuthorId: 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. As the grid size goes to infinity, the discretized model weakly converges to the continuous-time process, implying the consistsency of the density forecasts.

Download full text from publisher File URL: The process captures the thick tails, volatility persistence and moment scaling exhibited by many financial time series.

Paper This paper develops analytical methods to forecast the distribution of future returns for a new continuous-time process, the Poisson multi-fractal.

The challenge in forecating environment is long memory and the corresponding infinite dimension of the state space. If you have authored this item and are not yet registered with RePEc, we encourage you to do it here.

Friday, April 30, – 2: Calvet Adlai Julian Fisher. Have you forgotten your login? General contact details of provider: It can be interpreted as a stochastic volatility model with multiple frequencies and a Markov latent state.