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.

Recommended Citation

Michael Small and Chi K. Tse (2003) "Determinism in Financial Time Series", Studies in Nonlinear Dynamics & Econometrics: Vol. 7: No. 3, Article 5.
http://www.bepress.com/snde/vol7/iss3/art5

Related Files

small_datacode.zip (26335 kB)
Data and code