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Conditional spectral methods

Author

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  • Bandi, Federico M.
  • Su, Yinan

Abstract

We model predictive scale-specific cycles. By employing suitable matrix representations, we express the forecast errors of covariance-stationary multivariate time series in terms of conditionally orthonormal scale-specific bases. The representations yield conditionally orthogonal decompositions of these forecast errors. They also provide decompositions of their variances and betas in terms of scale-specific variances and betas capturing predictive variability and co-variability over cycles of alternative lengths without spillovers across cycles. Making use of the proposed representations within the classical family of time-varying conditional volatility models, we document the role of time-varying volatility forecasts in generating orthogonal predictive scale-specific cycles in returns. We conclude by providing suggestive evidence that the conditional variances of the predictive return cycles (i) may be priced over short-to-medium horizons and (ii) may offer economically-relevant trading signals over these same horizons.

Suggested Citation

  • Bandi, Federico M. & Su, Yinan, 2025. "Conditional spectral methods," Journal of Econometrics, Elsevier, vol. 248(C).
  • Handle: RePEc:eee:econom:v:248:y:2025:i:c:s0304407624002082
    DOI: 10.1016/j.jeconom.2024.105863
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    More about this item

    Keywords

    Time/scale representations; Spectral predictive cycles; Portfolio allocation;
    All these keywords.

    JEL classification:

    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • 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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