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Title:The Markov-switching multi-fractal model of asset returns: GMM estimation and linear forecasting of volatility PDF Logo
Authors:Lux, Thomas
Issue Date:2004
Series/Report no.:Economics working paper / Christian-Albrechts-Universität Kiel, Department of Economics 2004,11
Abstract:Multi-fractal processes have recently been proposed as a new formalism for modelling the time series of returns in finance. The major attraction of these processes is their ability to generate various degrees of long memory in different powers of returns - a feature that has been found in virtually all financial data. Initial difficulties stemming from non-stationarity and the combinatorial nature of the original model have been overcome by the introduction of an iterative Markov-switching multi-fractal model in Calvet and Fisher (2001) which allows for estimation of its parameters via maximum likelihood and Bayesian forecasting of volatility. However, applicability of MLE is restricted to cases with a discrete distribution of volatility components. From a practical point of view, ML also becomes computationally unfeasible for large numbers of components even if they are drawn from a discrete distribution. Here we propose an alternative GMM estimator together with linear forecasts which in principle is applicable for any continuous distribution with any number of volatility components. Monte Carlo studies show that GMM performs reasonably well for the popular Binomial and Lognormal models and that the loss incured with linear compared to optimal forecasts is small. Extending the number of volatility components beyond what is feasible with MLE leads to gains in forecasting accuracy for some time series.
Subjects:Markov-switching
Multifractal
Forecasting
Volatility
GMM estimation
JEL:C20
G12
Document Type:Working Paper
Appears in Collections:Economics Working Papers, Department of Economics, CAU Kiel
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