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A time-scale analysis of systematic risk: wavelet-based approach

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  • Khalfaoui Rabeh, K
  • Boutahar Mohamed, B

Abstract

The paper studies the impact of different time-scales on the market risk of individual stock market returns and of a given portfolio in Paris Stock Market by applying the wavelet analysis. To investigate the scaling properties of stock market returns and the lead/lag relationship between them at different scales, wavelet variance and crosscorrelations analyses are used. According to wavelet variance, stock returns exhibit long memory dynamics. The wavelet cross-correlation analysis shows that comovements between stock returns are stronger at higher scales (lower frequencies); scales corresponding to period of 4 months and longer, i.e. scales 7 and 8. The wavelet analysis of systematic risk shows that all individual assets and the diversified portfolio have a multi-scale behavior, which indicates that the systematic risk measured by Beta in the market model is not stable over time. The analysis of VaR at different time scales shows that risk is more concentrated at higher frequencies dynamics (lower time scales) of the data.

Suggested Citation

  • Khalfaoui Rabeh, K & Boutahar Mohamed, B, 2011. "A time-scale analysis of systematic risk: wavelet-based approach," MPRA Paper 31938, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:31938
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    References listed on IDEAS

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    15. Fernandez, Viviana, 2006. "The CAPM and value at risk at different time-scales," International Review of Financial Analysis, Elsevier, vol. 15(3), pages 203-219.
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    Cited by:

    1. Antonios K. Alexandridis & Mohammad S. Hasan, 2020. "Global financial crisis and multiscale systematic risk: Evidence from selected European stock markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 25(4), pages 518-546, October.
    2. Kamaruzdin, Thaqif & Masih, Mansur, 2014. "An inquiry into the stability of Islamic Financial Services Institutions in terms of volatility, risk and correlations: A case study of Malaysia employing M-GARCH t-DCC and MODWT Wavelet approaches," MPRA Paper 60248, University Library of Munich, Germany.
    3. Alaabed, Alaa & Masih, Mansur, 2014. "Size and Volatility: new evidence from an application of wavelet approach to the emerging Islamic mutual funds’ industry," MPRA Paper 62991, University Library of Munich, Germany.
    4. Tiwari, Aviral Kumar & Khalfaoui, Rabeh & Solarin, Sakiru Adebola & Shahbaz, Muhammad, 2018. "Analyzing the time-frequency lead–lag relationship between oil and agricultural commodities," Energy Economics, Elsevier, vol. 76(C), pages 470-494.
    5. Izani Ibrahim & Kamilah Kamaludin & Sheela Sundarasen, 2020. "COVID-19, Government Response, and Market Volatility: Evidence from the Asia-Pacific Developed and Developing Markets," Economies, MDPI, vol. 8(4), pages 1-22, November.

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    More about this item

    Keywords

    Wavelets; Systematic risk; Value-at-Risk;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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