1998/01/01 by Peter Tiňo, Tino, Peter, Christian Schittenkopf +5
Biochemistry, Genetics and Molecular Biology · Computer Science · Economics, Econometrics and Finance · #Complex Systems and Time Series Analysis #Fractal and DNA sequence analysis #Neural Networks and Applications
paper · pdf · doi:10.57938/dd7bb953-8446-472f-8ca4-2ff6bc96b69d
openalex publication_date 1998/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04
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)