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An adaptive long memory conditional correlation model

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  • Dark, Jonathan

Abstract

We propose a conditional correlation model with long memory dependence and smooth structural change. Previous literature has considered correlation and covariance models with structural change or long memory, but this is the first paper to jointly model both features. The correlation matrix is decomposed into long and short run components. Short run correlations converge hypergeometrically towards a slow moving long run correlation matrix that evolves according to one or more flexible Fourier forms. The model is applied to two data sets: a US equity portfolio; and a US equity, bond, gold and oil portfolio. Model fit and out of sample forecasts over 1 to 60 day horizons support the proposed approach.

Suggested Citation

  • Dark, Jonathan, 2024. "An adaptive long memory conditional correlation model," Journal of Empirical Finance, Elsevier, vol. 75(C).
    • Handle: RePEc:eee:empfin:v:75:y:2024:i:c:s0927539823001305
    DOI: 10.1016/j.jempfin.2023.101463
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    More about this item

    Keywords

    Long memory; Dynamic conditional correlation; Smooth structural change; Flexible Fourier form; Forecasting; Penalised MLE;
    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
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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