Large dynamic covariance matrices and portfolio selection with a heterogeneous autoregressive model

Igor Honig and Felix Kircher

Journal of Banking & Finance, 2025, vol. 178, issue C

Abstract: We propose a novel framework for modeling large dynamic covariance matrices via heterogeneous autoregressive volatility and correlation components. Our model provides direct forecasts of monthly covariance matrices and is flexible, parsimonious and simple to estimate using standard least squares methods. We address the problem of parameter estimation risks by employing nonlinear shrinkage methods, making our framework applicable in high dimensions. We perform a comprehensive empirical out-of-sample analysis and find significant statistical and economic improvements over common benchmark models. For minimum variance portfolios with over a thousand stocks, the annualized portfolio standard deviation improves to 8.92% compared to 9.75–10.43% for DCC-type models.

Keywords: Time-varying covariance matrix; High dimensions; Heterogeneous autoregressive model; Minimum variance portfolio; Markowitz portfolio optimization (search for similar items in EconPapers)
JEL-codes: C22 C51 C58 G11 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jbfina:v:178:y:2025:i:c:s0378426625001256

DOI: 10.1016/j.jbankfin.2025.107505

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