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The Markov-Switching Multifractal Model of asset returns: GMM estimation and linear forecasting of volatility

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dc.creator Lux, Thomas
dc.date 2006
dc.date.accessioned 2013-10-16T06:22:02Z
dc.date.available 2013-10-16T06:22:02Z
dc.date.issued 2013-10-16
dc.identifier http://hdl.handle.net/10419/3927
dc.identifier ppn:520844750
dc.identifier RePEc:zbw:cauewp:5164
dc.identifier.uri http://koha.mediu.edu.my:8181/xmlui/handle/10419/3927
dc.description Multifractal processes have recently been proposed as a new formalism for modelling the time series of returns in insurance. 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 multifractal 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 incurred 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.
dc.language eng
dc.publisher Institut für Volkswirtschaftslehre, Kiel
dc.relation Economics working paper / Christian-Albrechts-Universität Kiel, Department of Economics 2006,17
dc.rights http://www.econstor.eu/dspace/Nutzungsbedingungen
dc.subject G12
dc.subject C20
dc.subject ddc:330
dc.subject Markov-switching
dc.subject Multifractal
dc.subject Forecasting
dc.subject Volatility
dc.subject GMM estimation
dc.subject Kapitalertrag
dc.subject Börsenkurs
dc.subject Volatilität
dc.subject Prognoseverfahren
dc.subject Physik
dc.subject Markovscher Prozess
dc.subject Zeitreihenanalyse
dc.subject Theorie
dc.title The Markov-Switching Multifractal Model of asset returns: GMM estimation and linear forecasting of volatility
dc.type doc-type:workingPaper


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