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A wavelet approach of investing behaviors and their effects on risk exposures

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  • Roman Mestre

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

Abstract

Exposure to market risk is a core objective of the Capital Asset Pricing Model (CAPM) with a focus on systematic risk. However, traditional OLS Beta model estimations (Ordinary Least Squares) are plagued with several statistical issues. Moreover, the CAPM considers only one source of risk and supposes that investors only engage in similar behaviors. In order to analyze short and long exposures to diferent sources of risk, we developed a Time–Frequency Multi-Betas Model with ARMA-EGARCH errors (Auto Regressive Moving Average Exponential AutoRegressive Conditional Heteroskedasticity). Our model considers gold, oil, and Fama–French factors as supplementary sources of risk and wavelets decompositions. We used 30 French stocks listed on the CAC40 (Cotations Assistées Continues 40) within a daily period from 2005 to 2015. The conjugation of the wavelet decompositions and the parameters estimates constitutes decision-making support for managers by multiplying the interpretive possibilities. In the short-run, ("Noise Trader" and "High-Frequency Trader") only a few equities are insensitive to Oil and Gold fuctuations, and the estimated Market Betas parameters are scant diferent compared to the Model without wavelets. Oppositely, in the long-run, (fundamentalists investors), Oil and Gold afect all stocks but their impact varies according to the Beta (sensitivity to the market). We also observed signifcant diferences between parameters estimated with and without wavelets.

Suggested Citation

  • Roman Mestre, 2021. "A wavelet approach of investing behaviors and their effects on risk exposures," Post-Print hal-03195190, HAL.
    • Handle: RePEc:hal:journl:hal-03195190
    DOI: 10.1186/s40854-021-00239-z
    Note: View the original document on HAL open archive server: https://hal.science/hal-03195190v1
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    2. Majid Mirzaee Ghazani & Ali Akbar Momeni Malekshah & Reza Khosravi, 2024. "Analyzing time–frequency connectedness between cryptocurrencies, stock indices, and benchmark crude oils during the COVID-19 pandemic," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-28, December.
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    More about this item

    Keywords

    Risk exposures; CAPM; Multi-betas model; Time–frequency analysis; MODWT; Oil; Gold;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G40 - Financial Economics - - Behavioral Finance - - - General

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