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Multivariate Fractional Components Analysis

Author

Listed:
  • Tobias Hartl
  • Roland Jucknewitz

Abstract

We propose a setup for fractionally cointegrated time series which is formulated in terms of latent integrated and short-memory components. It accommodates nonstationary processes with different fractional orders and cointegration of different strengths and is applicable in high-dimensional settings. In an application to realized covariance matrices, we find that orthogonal short- and long-memory components provide a reasonable fit and competitive out-of-sample performance compared with several competing methods.

Suggested Citation

  • Tobias Hartl & Roland Jucknewitz, 2023. "Multivariate Fractional Components Analysis," Journal of Financial Econometrics, Oxford University Press, vol. 21(3), pages 880-914.
    • Handle: RePEc:oup:jfinec:v:21:y:2023:i:3:p:880-914.
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbab022
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    More about this item

    Keywords

    factor model; fractional cointegration; long memory; realized covariance matrix; state space; unobserved components;
    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
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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