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Erschienen in: Journal of Economics and Finance 3/2022

24.04.2022

Evaluating measures of dependence for linearly generated nonlinear time series along with spurious correlation

verfasst von: Christos Agiakloglou, Anil Bera, Emmanouil Deligiannakis

Erschienen in: Journal of Economics and Finance | Ausgabe 3/2022

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Abstract

The issue of determining dependence between two series is typically one of the most important aspects in any quantitative analysis. This study, using a Monte Carlo analysis, investigates the performance of several dependence measures for linearly generated nonlinear time series based on the family of AR(1) – ARCH(1) in variable models presented by Bera et al. (1992 and 1996) and it finds that copulas capture the concept of dependence better than the correlation coefficient. In addition, this study examines the performance of the test for zero association and it discovers that the spurious behavior can be eliminated asymptotically for this type on nonlinear processes, although the power of the test remains relatively low.

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Fußnoten
1
The Monte Carlo analysis is conducted in Python.
 
2
The results for values of ρ = 0.5 and 0.9 are not included, simply because they are not adding value to the general picture obtained from this analysis, but they are available upon request.
 
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Metadaten
Titel
Evaluating measures of dependence for linearly generated nonlinear time series along with spurious correlation
verfasst von
Christos Agiakloglou
Anil Bera
Emmanouil Deligiannakis
Publikationsdatum
24.04.2022
Verlag
Springer US
Erschienen in
Journal of Economics and Finance / Ausgabe 3/2022
Print ISSN: 1055-0925
Elektronische ISSN: 1938-9744
DOI
https://doi.org/10.1007/s12197-022-09579-7

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