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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Forecasting multifractal volatility

We assume for simplicity that the forecaster knows the true generating process with certainty but only observes past returns. The challenge in this environment is long memory and the corresponding infinite dimension of the state space.

If you are a registered author of this item, you may also want to check the “citations” tab in your RePEc Author Service foreacsting, as there may be some citations waiting for confirmation. 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 forecasfing continuous-time process, implying the consistsency of the density forecasts.

Forecasting Long memory Multiple frequencies Stochastic volatility Weak convergence.

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Please note that corrections may take a couple of weeks to filter through the various RePEc services. Monday, December mulgifractal, – 4: Laurent-Emmanuel Calvet 1 AuthorId: If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form. Download full text forecastinf publisher File URL: 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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Laurent-Emmanuel Calvet 1 Adlai J. This paper develops analytical methods to forecast the distribution of future returns for a new continuous-time process, the Poisson multifractal. Stern School of Business. It also allows you to accept potential citations to this item that we are uncertain about.

We introduce a discretized version of the model that has a finite state space and 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 you know of missing items citing this one, you can help us creating forecastinv links by adding the relevant references in the same way as above, for each refering item.

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The process captures the thick tails, volatility persistence, and moment scaling exhibited by many financial time series. We assume for simplicity that the forecaster knows the true generating process with certainty but only observes past returns. The challenge in this environment is long memory and the corresponding infinite dimension of the state space.

Forecasting multifractal volatility

Corrections All material on this site has been provided by the respective publishers and authors. 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. Help us Corrections Found an error or omission? Calvet Adlai Julian Fisher. It can be interpreted as a stochastic volatility model with multiple frequencies and a Markov latent state.

We introduce a discretized version of the model that has a finite state space and allows for an analytical solution to the conditioning problem. Full text for ScienceDirect subscribers only As the access to this document is restricted, you forecassting want to look for a different version below or search for a different version of it.