{"title":"波动性约束下的结构化协方差矩阵估计","authors":"Yongqiang Wu , Jun Zhang , Wei Lan","doi":"10.1016/j.frl.2025.108047","DOIUrl":null,"url":null,"abstract":"<div><div>This paper proposes a novel structured covariance estimation method with block correlation structure and volatility constraint. We establish the estimator’s asymptotic properties under mild regularity conditions. Empirical validation using S&P 500 constituent stocks confirms its efficacy. The results reveal that the inclusion of volatility constraints significantly improves the out-of-sample Sharpe ratio of constructed portfolios, outperforming traditional factor models and shrinkage estimation techniques. Overall, these findings highlight the robustness and practical utility of the proposed method in enhancing portfolio performance.</div></div>","PeriodicalId":12167,"journal":{"name":"Finance Research Letters","volume":"85 ","pages":"Article 108047"},"PeriodicalIF":6.9000,"publicationDate":"2025-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Structured covariance matrix estimation under volatility constraint\",\"authors\":\"Yongqiang Wu , Jun Zhang , Wei Lan\",\"doi\":\"10.1016/j.frl.2025.108047\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This paper proposes a novel structured covariance estimation method with block correlation structure and volatility constraint. We establish the estimator’s asymptotic properties under mild regularity conditions. Empirical validation using S&P 500 constituent stocks confirms its efficacy. The results reveal that the inclusion of volatility constraints significantly improves the out-of-sample Sharpe ratio of constructed portfolios, outperforming traditional factor models and shrinkage estimation techniques. Overall, these findings highlight the robustness and practical utility of the proposed method in enhancing portfolio performance.</div></div>\",\"PeriodicalId\":12167,\"journal\":{\"name\":\"Finance Research Letters\",\"volume\":\"85 \",\"pages\":\"Article 108047\"},\"PeriodicalIF\":6.9000,\"publicationDate\":\"2025-08-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Finance Research Letters\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1544612325013054\",\"RegionNum\":2,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BUSINESS, FINANCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Finance Research Letters","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1544612325013054","RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
Structured covariance matrix estimation under volatility constraint
This paper proposes a novel structured covariance estimation method with block correlation structure and volatility constraint. We establish the estimator’s asymptotic properties under mild regularity conditions. Empirical validation using S&P 500 constituent stocks confirms its efficacy. The results reveal that the inclusion of volatility constraints significantly improves the out-of-sample Sharpe ratio of constructed portfolios, outperforming traditional factor models and shrinkage estimation techniques. Overall, these findings highlight the robustness and practical utility of the proposed method in enhancing portfolio performance.
期刊介绍:
Finance Research Letters welcomes submissions across all areas of finance, aiming for rapid publication of significant new findings. The journal particularly encourages papers that provide insight into the replicability of established results, examine the cross-national applicability of previous findings, challenge existing methodologies, or demonstrate methodological contingencies.
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