Forecasting multifractal volatility

Laurent Calvet and Adlai Fisher

Post-Print from HAL

Abstract: This paper develops analytical methods to forecast the distribution of future returns for a new continuous-time process, the Poisson multifractal. The process captures the thick tails, volatility persistence, and moment scaling exhibited by many financial time series. It can be interpreted as a stochastic volatility model with multiple frequencies and a Markov latent state. 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. We introduce a discretized version of the model that has a finite state space and an analytical solution to the conditioning problem. 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.

Keywords: Forecasting; Long memory; Multiple frequencies; Stochastic volatility; Weak convergence (search for similar items in EconPapers)
Date: 2001-11
References: Add references at CitEc
Citations: View citations in EconPapers (139)

Published in Econometrics, 2001, Vol.105,n°1, pp.27-58. ⟨10.1016/S0304-4076(01)00069-0⟩

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
Journal Article: Forecasting multifractal volatility (2001)
Working Paper: Forecasting Multifractal Volatility (2000)
Working Paper: Forecasting Multifractal Volatility (1999)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-00477952

DOI: 10.1016/S0304-4076(01)00069-0

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().