Forecasting Multifractal Volatility

Laurent Calvet and Adlai Fisher

New York University, Leonard N. Stern School Finance Department Working Paper Seires from New York University, Leonard N. Stern School of Business-

Abstract: This paper develops analytical methods to forecast the distribution of future returns for a new continuous-time process, the Poisson multifractal. Out model captures the thick tails and volatility persistence exhibited by many financial time series. We assume 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 show that a discretized version of the model has a finite state space, which allows an analytical solution to the conditioning problem. Further, the discrete model converges to the continuous-time model as time scale goes to zero, so that forecasts are consistent. The methodology is implemented on simulated data calibrated to the Deutschemark/US Dollar exchange rate. Applying these results to option pricing, we find that the model captures both volatility smiles and long-memory in the term structure of implied volatilities.

Date: 1999-02
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Related works:
Journal Article: Forecasting multifractal volatility (2001)
Working Paper: Forecasting multifractal volatility (2001)
Working Paper: Forecasting Multifractal Volatility (2000)
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Persistent link: https://EconPapers.repec.org/RePEc:fth:nystfi:99-017

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