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Multiscale SUR Estimation of Systematic Risk

Author

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  • Michis Antonis A.

    (87194 Central Bank of Cyprus , 8 Kennedy Avenue, Nicosia 1076, Cyprus)

Abstract

We propose a multiscale version of the seemingly unrelated regressions model, based on wavelet transform-based time series observations. Each regression equation refers to a different time scale, which enables the use of across-scale error covariances in the feasible GLS estimation procedure for efficiency gains. We demonstrate the advantages of the proposed method over OLS with two studies: an empirical study using stock market returns for the main US industrial sectors and a detailed Monte Carlo simulation study with alternative wavelet filters. We also provide explanations for the suitability of the proposed method for estimating long-term systematic risk.

Suggested Citation

  • Michis Antonis A., 2025. "Multiscale SUR Estimation of Systematic Risk," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 29(2), pages 129-145.
    • Handle: RePEc:bpj:sndecm:v:29:y:2025:i:2:p:129-145:n:1003
    DOI: 10.1515/snde-2023-0017
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    More about this item

    Keywords

    seemingly unrelated regressions; wavelets; error covariance; systematic risk;
    All these keywords.

    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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