On Unified Adaptive Black-Litterman Mean-Variance Portfolio Management
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866910274090434560 |
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| author | Li, Chi-Lin Hsieh, Chung-Han |
| author_facet | Li, Chi-Lin Hsieh, Chung-Han |
| contents | This paper proposes a unified adaptive portfolio-management framework that combines factor-based view generation, Black-Litterman (BL) posterior estimation, EWMA covariance estimation, and mean-variance optimization. The key mechanism is a dynamic sliding window that adjusts the estimation horizon according to realized portfolio volatility, thereby updating factor estimates, BL posterior expected returns, and portfolio weights over time. In a ten-year empirical study of the top 100 market-capitalization constituents of the S&P 500 with turnover transaction costs, the proposed method outperforms dynamic mean-variance optimization without BL views and provides stronger downside risk control, while its relative performance remains benchmark-dependent. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_03391 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | On Unified Adaptive Black-Litterman Mean-Variance Portfolio Management Li, Chi-Lin Hsieh, Chung-Han Portfolio Management Optimization and Control Computational Finance Risk Management This paper proposes a unified adaptive portfolio-management framework that combines factor-based view generation, Black-Litterman (BL) posterior estimation, EWMA covariance estimation, and mean-variance optimization. The key mechanism is a dynamic sliding window that adjusts the estimation horizon according to realized portfolio volatility, thereby updating factor estimates, BL posterior expected returns, and portfolio weights over time. In a ten-year empirical study of the top 100 market-capitalization constituents of the S&P 500 with turnover transaction costs, the proposed method outperforms dynamic mean-variance optimization without BL views and provides stronger downside risk control, while its relative performance remains benchmark-dependent. |
| title | On Unified Adaptive Black-Litterman Mean-Variance Portfolio Management |
| topic | Portfolio Management Optimization and Control Computational Finance Risk Management |
| url | https://arxiv.org/abs/2307.03391 |