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Determinism in Financial Time Series

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  • Small Michael

    ()
    (Hong Kong Polytechnic University)

  • Tse Chi K.

    (Hong Kong Polytechnic University)

Abstract

The attractive possibility that financial indices may be chaotic has been the subject of much study. In this paper we address two specific questions: "Masked by stochasticity, do financial data exhibit deterministic nonlinearity?", and "If so, so what?". We examine daily returns from three financial indicators: the Dow Jones Industrial Average, the London gold fixings, and the USD-JPY exchange rate. For each data set we apply surrogate data methods and nonlinearity tests to quantify determinism over a wide range of time scales (from 100 to 20,000 days). We find that all three time series are distinct from linear noise or conditional heteroskedastic models and that there therefore exists detectable deterministic nonlinearity that can potentially be exploited for prediction.

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Bibliographic Info

Article provided by De Gruyter in its journal Studies in Nonlinear Dynamics & Econometrics.

Volume (Year): 7 (2003)
Issue (Month): 3 (October)
Pages: 1-31

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Handle: RePEc:bpj:sndecm:v:7:y:2003:i:3:n:5

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Cited by:
  1. Leontitsis, Alexandros & Vorlow, Constantinos E., 2006. "Accounting for outliers and calendar effects in surrogate simulations of stock return sequences," Physica A: Statistical Mechanics and its Applications, Elsevier, Elsevier, vol. 368(2), pages 522-530.
  2. Webel, Karsten, 2012. "Chaos in German stock returns — New evidence from the 0–1 test," Economics Letters, Elsevier, Elsevier, vol. 115(3), pages 487-489.

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