Conditional Spectral Methods

50 Pages Posted: 14 Dec 2022 Last revised: 30 May 2024

See all articles by Federico M. Bandi

Federico M. Bandi

Johns Hopkins University - Carey Business School

Yinan Su

Johns Hopkins University - Carey Business School

Date Written: March 31, 2024

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 basis. 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 may (i) be priced over short-to-medium horizons and (ii) offer economically-relevant trading signals over these same horizons.

Keywords: Time/scale decompositions, spectral predictive cycles, portfolio allocation. JEL codes. C3, G11, G12

JEL Classification: C3, G11, G12

Suggested Citation

Bandi, Federico Maria and Su, Yinan, Conditional Spectral Methods (March 31, 2024). Johns Hopkins Carey Business School Research Paper No. 23-02, Available at SSRN: https://ssrn.com/abstract=4284240 or http://dx.doi.org/10.2139/ssrn.4284240

Federico Maria Bandi

Johns Hopkins University - Carey Business School ( email )

100 International Drive
Baltimore, MD 21202
United States

Yinan Su (Contact Author)

Johns Hopkins University - Carey Business School ( email )

100 International Drive
Baltimore, MD 21202
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
489
Abstract Views
899
Rank
110,416
PlumX Metrics