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Time–frequency varying estimations: comparison of discrete and continuous wavelets in the market line framework

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Abstract

This paper focus on comparison between three wavelets methodologies to estimate a time–frequency varying parameter. In the discrete case, we oppose the intuitive application of the rolling regression on wavelets frequency bands to the time–frequency rolling window. We compare if we have to use the time rolling window directly on the wavelet’s frequency bands or apply the time–frequency rolling window on the series realizing the wavelet decomposition at each step of the process. A time–frequency varying estimator by continuous wavelets is also considerate in the comparison. Our objective is to show that the time–frequency rolling window and the Continuous estimates are more suitable than the intuitive way. We use in first time simulated data and also the daily returns of AXA and the CAC 40 index from 2005 to 2015 as empirical application. We show that the differences between discrete methods are more important at low-frequencies. Moreover, the continuous time–frequency Betas are closer to the time–frequency windows estimates.

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Notes

  1. See Mestre and Terraza [17, 18].

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Authors and Affiliations

  1. MRE Université de Montpellier, UFR d’économie Avenue Raymond Dugrand, Site de Richter C.S., 79606 34960, Montpellier cedex 2, France

    Roman Mestre & Michel Terraza

Authors
  1. Roman Mestre
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  2. Michel Terraza
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Correspondence to Roman Mestre.

Appendix

Appendix

See Tables 5, 67 and 8.

Table 5 Frequency bands and time horizon
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Table 6 Betas estimation of 3 simulations: OLS static betas
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Table 7 Betas estimation of 3 simulations: wavelets Betas estimation for the 3 simulations
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Table 8 Stationary tests
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Mestre, R., Terraza, M. Time–frequency varying estimations: comparison of discrete and continuous wavelets in the market line framework. J BANK FINANC TECHNOL 3, 97–111 (2019). https://doi.org/10.1007/s42786-019-00008-8

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  • DOI: https://doi.org/10.1007/s42786-019-00008-8

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