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Time-Frequency varying beta estimation - a continuous wavelets approach

Author

Listed:
  • Roman Mestre

    (MRE - Montpellier Recherche en Economie - UM - Université de Montpellier)

  • Michel Terraza

    (MRE - Montpellier Recherche en Economie - UM - Université de Montpellier)

Abstract

The Beta coefficient theorized by the CAPM is estimated by the Market Line. By hypothesis, the Beta is stable over time but empirical studies on it volatility don't confirm this fact. One of them is related to with agent heterogeneity hypothesis. In this paper; we study this hypothesis by continuous wavelets decomposition of the market line components. We use the wavelet Coherence to calculate a time-frequency Beta. We apply this methodology on three French listed stocks (AXA-LVMH-ORANGE) with different OLS beta for the daily period from 2005 to 2015. We show that the coherence and the time-frequency Betas improve our understanding of the equity characteristics and nature according to their time and frequency dynamics. AXA and LVMH have globally an high coherence with the market whereas ORANGE coherence is low (whatever frequencies). These results can affect the time-frequency betas values. By analyzing the betas we see different evolutions and dynamics which can be considered by portfolio managers to optimize their investment horizon. The continuous wavelets is a powerful tool for emphasize the timefrequency instabilities of betas. The hypothesis of heterogeneity of agents have an impact on systematic risk estimations and need to be considered in financial calculations.

Suggested Citation

  • Roman Mestre & Michel Terraza, 2018. "Time-Frequency varying beta estimation - a continuous wavelets approach," Post-Print hal-03195193, HAL.
    • Handle: RePEc:hal:journl:hal-03195193
    Note: View the original document on HAL open archive server: https://hal.science/hal-03195193
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    References listed on IDEAS

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    1. 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.
    2. 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.
    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. Gencay, Ramazan & Selcuk, Faruk & Whitcher, Brandon, 2005. "Multiscale systematic risk," Journal of International Money and Finance, Elsevier, vol. 24(1), pages 55-70, February.
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    Cited by:

    1. Rémi Odry & Roman Mestre, 2021. "Monetary Policy and Business Cycle Synchronization in Europe," EconomiX Working Papers 2021-19, University of Paris Nanterre, EconomiX.
    2. Pankaj Agrrawal & Faye W. Gilbert & Jason Harkins, 2022. "Time Dependence of CAPM Betas on the Choice of Interval Frequency and Return Timeframes: Is There an Optimum?," JRFM, MDPI, vol. 15(11), pages 1-18, November.
    3. 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 Wavel," MPRA Paper 89682, University Library of Munich, Germany.
    4. Yang Mestre Zhou & Roman Mestre, 2024. "A continuous wavelets approach of China opening reforms effects on relationships between mainland Chinese stock exchanges and Hong Kong," Economics Bulletin, AccessEcon, vol. 44(1), pages 430-465.

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    More about this item

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • G1 - Financial Economics - - General Financial Markets

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