Honig, Igor and Kircher, Felix (2025) Large dynamic covariance matrices and portfolio selection with a heterogeneous autoregressive model. JOURNAL OF BANKING & FINANCE, 178: 107505. ISSN 0378-4266, 1872-6372
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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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | NONLINEAR SHRINKAGE; ECONOMETRIC-ANALYSIS; REALIZED VOLATILITY; RISK; Time-varying covariance matrix; High dimensions; Heterogeneous autoregressive model; Minimum variance portfolio; Markowitz portfolio optimization |
| Subjects: | 300 Social sciences > 330 Economics |
| Divisions: | Business, Economics and Information Systems > Institut für Betriebswirtschaftslehre > Lehrstuhl für Statistik und Risikomanagement (Prof. Dr. Rösch) |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 17 Jun 2026 08:55 |
| Last Modified: | 17 Jun 2026 08:55 |
| URI: | https://pred.uni-regensburg.de/id/eprint/66346 |
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