Towards a framework for combining stochastic and deterministic descriptions of nonstationary financial time series


We present in this paper ideas to tackle the problem of analysing and forecasting nonstationary time series within the financial domain. Accepting the stochastic nature of the underlying data generator we assume that the evolution of the generator's parameters is restricted on a deterministic manifold. Therefore we propose methods for determining the characteristics of the time-localised distribution. Starting with the assumption of a static normal distribution we refine this hypothesis according to the empirical results obtained with the methods anc conclude with the indication of a dynamic non-Gaussian behaviour with varying dependency for the time series under consideration.

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Divisions: College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Additional Information: ©1998 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Event Title: Neural Networks for Signal Processing
Event Type: Other
Event Dates: 1998-09-02 - 1998-09-02
Uncontrolled Keywords: forecasting,non-stationary,time series,financial domain,stochastic nature,data generator,deterministic manifold,time-localised distribution,non-Gaussian behaviour
ISBN: 078035060
Last Modified: 02 Jan 2024 08:25
Date Deposited: 17 Sep 2009 09:47
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Related URLs: http://ieeexplo ... &isnumber=15338 (Publisher URL)
PURE Output Type: Chapter
Published Date: 1998-09-02
Authors: Lesch, Ragnar H.
Lowe, David



Version: Published Version

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