Resource title

A symbolic dynamics approach to volatility prediction

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Resource description

We consider the problem of predicting the direction of daily volatility changes in the Dow Jones Industrial Average (DJIA). This is accomplished by quantizing a series of historic volatility changes into a symbolic stream over 2 or 4 symbols. We compare predictive performance of the classical fixed-order Markov models with that of a novel approach to variable memory length prediction (called prediction fractal machine, or PFM) which is able to select very specific deep prediction contexts (whenever there is a sufficient support for such contexts in the training data). We learn that daily volatility changes of the DJIA only exhibit rather shallow finite memory structure. On the other hand, a careful selection of quantization cut values can strongly enhance predictive power of symbolic schemes. Results on 12 non-overlapping epochs of the DJIA strongly suggest that PFMs can outperform both traditional Markov models and (continuous-valued) GARCH models in the task of predicting volatility one time-step ahead. (author's abstract) ; Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

Resource author

Peter Tino, Christian Schittenkopf, Georg Dorffner, Engelbert J. Dockner

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Resource language

en

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application/pdf

Resource resource URL

http://epub.wu.ac.at/1142/1/document.pdf

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