A symbolic dynamics approach to volatility prediction

Tino, Peter and Schittenkopf, Christian and Dorffner, Georg and Dockner, Engelbert J. (1998) A symbolic dynamics approach to volatility prediction. Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science", 18. SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, Vienna.


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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)

Item Type: Paper
Keywords: Aktienindex / Volatilität / Optionspreis / Prognose / Symbolische Dynamik
Divisions: Departments > Informationsverarbeitung u Prozessmanag. > Produktionsmanagement > Taudes
Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Departments > Marketing > Service Marketing und Tourismus
Departments > Informationsverarbeitung u Prozessmanag. > Informationswirtschaft
Depositing User: Repository Administrator
Date Deposited: 22 Mar 2002 11:36
Last Modified: 22 Oct 2019 00:41
URI: https://epub.wu.ac.at/id/eprint/1142


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