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Prediction of cyclic and trend frequencies in time series using the Hilbert-Huang transform

Author

Listed:
  • Hugh L. Christensen
  • Simon J. Godsill

Abstract

Prediction of future security returns is possible by decomposing a securities price into weighted superpositions of underlying basis states, given stationary distributions of the basis states. The (ensemble) Hilbert-Huang transform (HHT) is an empirical two-step online methodology which carries out such a decomposition from a multi-component noisy time series. HHT allows estimation of each component's instantaneous phase, period and amplitude. A hypothesis is presented where markets exist in the binary states of trend or cycle. Switching between states is based on phase-shifting in a dyadic filter bank. A trading algorithm is presented which exploits this model by combining intra-day predictions for trend and cycle components along with a much lower frequency drift component. The algorithm is simulated on e-mini S%P 500 futures data from CME GLOBEX at one minute sampling frequency. Results are presented which show a combined strategy Sharpe ratio in excess of 3.

Suggested Citation

  • Hugh L. Christensen & Simon J. Godsill, 2014. "Prediction of cyclic and trend frequencies in time series using the Hilbert-Huang transform," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 4(3/4), pages 372-412.
    • Handle: RePEc:ids:ijcome:v:4:y:2014:i:3/4:p:372-412
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