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Econometrics 2018, 6(1), 3; doi:10.3390/econometrics6010003

Spurious Seasonality Detection: A Non-Parametric Test Proposal

1
Department of Business, Universitat Rovira i Virgili, Av. Universitat 1, 43204 Reus, Spain
2
IFLP-CONICET-UNLP, C. C. 727, 1900 La Plata, Argentina
3
SThAR-EPFL Innovation Park, 1015 Lausanne, Switzerland
4
Graduate College, 207 Giannini Hall, University of California Berkeley, Berkeley, CA 94720, USA
*
Author to whom correspondence should be addressed.
Received: 3 September 2017 / Revised: 6 January 2018 / Accepted: 12 January 2018 / Published: 19 January 2018
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Abstract

This paper offers a general and comprehensive definition of the day-of-the-week effect. Using symbolic dynamics, we develop a unique test based on ordinal patterns in order to detect it. This test uncovers the fact that the so-called “day-of-the-week” effect is partly an artifact of the hidden correlation structure of the data. We present simulations based on artificial time series as well. While time series generated with long memory are prone to exhibit daily seasonality, pure white noise signals exhibit no pattern preference. Since ours is a non-parametric test, it requires no assumptions about the distribution of returns, so that it could be a practical alternative to conventional econometric tests. We also made an exhaustive application of the here-proposed technique to 83 stock indexes around the world. Finally, the paper highlights the relevance of symbolic analysis in economic time series studies. View Full-Text
Keywords: daily seasonality; ordinal patterns; stock market; symbolic analysis daily seasonality; ordinal patterns; stock market; symbolic analysis
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Bariviera, A.F.; Plastino, A.; Judge, G. Spurious Seasonality Detection: A Non-Parametric Test Proposal. Econometrics 2018, 6, 3.

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