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Regression Forward avec fenêtres Tempo-Frequentielles roulantes par ondelettes discretes et continues -Une application à la Droite de Marché -
[Forward Regression with Discrete and Continuous Wavelets Time-Frequency Window -An application to the Market Line-]

Author

Listed:
  • MESTRE, Roman
  • Terraza, Michel

Abstract

The Rolling-Regression are currently used to study the parameters stability over time. In finance, we can analyse the time evolutions of systematic risk relaxing the constant-Beta hypothesis. This method can be associated with a wavelet decomposition of the variables in order to the parameters stability of frequency regression. Then, we compare continuous and discrete wavelets methodologies of rolling regression with the standard rolling regression. The discrete methods are based on time-frequency window but we compare if we have to use it on the wavelets filter output or directly on the series and realize the wavelet decomposition at each step of the window. The continuous method is based on wavelets coherence-phase. We use daily data of AXA returns and the CAC 40 index from 2005 to 2015. We show that the differences between discrete methods are more important at Low-Frequencies and we compare the results with the Continuous Time-Frequency Betas.

Suggested Citation

  • MESTRE, Roman & Terraza, Michel, 2018. "Regression Forward avec fenêtres Tempo-Frequentielles roulantes par ondelettes discretes et continues -Une application à la Droite de Marché - [Forward Regression with Discrete and Continuous Wavelets Time-Frequency Window -An application to the M," MPRA Paper 89682, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:89682
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    References listed on IDEAS

    as
    1. Robert D. Brooks & Robert W. Faff & Michael D. McKenzie, 1998. "Time†Varying Beta Risk of Australian Industry Portfolios: A Comparison of Modelling Techniques," Australian Journal of Management, Australian School of Business, vol. 23(1), pages 1-22, June.
    2. R.W. Faff & R.D. Brooks, 1998. "Time‐varying Beta Risk for Australian Industry Portfolios: An Exploratory Analysis," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 25(5‐6), pages 721-745, June.
    3. Roman Mestre & Michel Terraza, 2018. "Time-Frequency Analysis of capm: Application to the cac 40," Managing Global Transitions, University of Primorska, Faculty of Management Koper, vol. 16(2 (Summer), pages 141-157.
    4. Fama, Eugene F & MacBeth, James D, 1973. "Risk, Return, and Equilibrium: Empirical Tests," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 607-636, May-June.
    5. Bekiros, Stelios & Nguyen, Duc Khuong & Uddin, Gazi Salah & Sjö, Bo, 2016. "On the time scale behavior of equity-commodity links: Implications for portfolio management," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 41(C), pages 30-46.
    6. Roman Mestre & Michel Terraza, 2018. "Time-Frequency varying beta estimation -a continuous wavelets approach-," Economics Bulletin, AccessEcon, vol. 38(4), pages 1796-1810.
    7. William F. Sharpe, 1964. "Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk," Journal of Finance, American Finance Association, vol. 19(3), pages 425-442, September.
    Full references (including those not matched with items on IDEAS)

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    Keywords

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    JEL classification:

    • G00 - Financial Economics - - General - - - General
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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