Forecasting Multifractal Volatility

Laurent Calvet

Harvard Institute of Economic Research Working Papers from Harvard - Institute of Economic Research

Abstract: | Paper

This paper develops analytical methods to forecast the distribution of future returns for a new continuous-time process, the Poisson multi-fractal. 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 allows for an analytical solution to the conditioning problem. As the grid size goes to infinity, the discretized model weakly converges to the continuous-time process, implying the consistsency of the density forecasts.

Date: 2000
New Economics Papers: this item is included in nep-ecm and nep-fmk
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Related works:
Journal Article: Forecasting multifractal volatility (2001)
Working Paper: Forecasting multifractal volatility (2001)
Working Paper: Forecasting Multifractal Volatility (1999)
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