将 MGHyp 分布与非线性收缩相结合，建立金融资产收益模型

IF 2.1 2区经济学 Q2 BUSINESS, FINANCE

Journal of Empirical Finance Pub Date : 2024-03-15 DOI:10.1016/j.jempfin.2024.101489

Simon Hediger , Jeffrey Näf

{"title":"将 MGHyp 分布与非线性收缩相结合，建立金融资产收益模型","authors":"Simon Hediger , Jeffrey Näf","doi":"10.1016/j.jempfin.2024.101489","DOIUrl":null,"url":null,"abstract":"<div><p>The present paper combines nonlinear shrinkage with the multivariate generalized hyperbolic (MGHyp) distribution, thereby extending a flexible parametric model to high dimensions. An expectation–maximization (EM) algorithm is developed that is fast, stable, and applicable in high dimensions. Theoretical arguments for the monotonicity of the proposed algorithm are provided and it is shown in simulations that it is able to accurately retrieve parameter estimates. Finally, in an extensive Markowitz portfolio optimization analysis, the approach is compared to state-of-the-art benchmark models. The proposed model excels with a strong out-of-sample portfolio performance combined with a comparably low turnover.</p></div>","PeriodicalId":15704,"journal":{"name":"Journal of Empirical Finance","volume":"77 ","pages":"Article 101489"},"PeriodicalIF":2.1000,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0927539824000240/pdfft?md5=84b1b3630884c1e067c4a40a2255ecc7&pid=1-s2.0-S0927539824000240-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Combining the MGHyp distribution with nonlinear shrinkage in modeling financial asset returns\",\"authors\":\"Simon Hediger , Jeffrey Näf\",\"doi\":\"10.1016/j.jempfin.2024.101489\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The present paper combines nonlinear shrinkage with the multivariate generalized hyperbolic (MGHyp) distribution, thereby extending a flexible parametric model to high dimensions. An expectation–maximization (EM) algorithm is developed that is fast, stable, and applicable in high dimensions. Theoretical arguments for the monotonicity of the proposed algorithm are provided and it is shown in simulations that it is able to accurately retrieve parameter estimates. Finally, in an extensive Markowitz portfolio optimization analysis, the approach is compared to state-of-the-art benchmark models. The proposed model excels with a strong out-of-sample portfolio performance combined with a comparably low turnover.</p></div>\",\"PeriodicalId\":15704,\"journal\":{\"name\":\"Journal of Empirical Finance\",\"volume\":\"77 \",\"pages\":\"Article 101489\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2024-03-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0927539824000240/pdfft?md5=84b1b3630884c1e067c4a40a2255ecc7&pid=1-s2.0-S0927539824000240-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Empirical Finance\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0927539824000240\",\"RegionNum\":2,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BUSINESS, FINANCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Empirical Finance","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0927539824000240","RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}

引用次数: 0

摘要

本文将非线性收缩与多元广义双曲（MGHyp）分布相结合，从而将灵活的参数模型扩展到高维度。本文开发了一种快速、稳定且适用于高维度的期望最大化（EM）算法。本文提供了所提算法单调性的理论依据，并通过仿真表明，该算法能够准确检索参数估计。最后，在广泛的马科维茨投资组合优化分析中，该方法与最先进的基准模型进行了比较。所提出的模型在样本外投资组合方面表现出色，同时具有相当低的周转率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Combining the MGHyp distribution with nonlinear shrinkage in modeling financial asset returns

The present paper combines nonlinear shrinkage with the multivariate generalized hyperbolic (MGHyp) distribution, thereby extending a flexible parametric model to high dimensions. An expectation–maximization (EM) algorithm is developed that is fast, stable, and applicable in high dimensions. Theoretical arguments for the monotonicity of the proposed algorithm are provided and it is shown in simulations that it is able to accurately retrieve parameter estimates. Finally, in an extensive Markowitz portfolio optimization analysis, the approach is compared to state-of-the-art benchmark models. The proposed model excels with a strong out-of-sample portfolio performance combined with a comparably low turnover.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Journal of Empirical Finance Multiple-

CiteScore

3.40

自引率

3.80%

发文量

期刊介绍： The Journal of Empirical Finance is a financial economics journal whose aim is to publish high quality articles in empirical finance. Empirical finance is interpreted broadly to include any type of empirical work in financial economics, financial econometrics, and also theoretical work with clear empirical implications, even when there is no empirical analysis. The Journal welcomes articles in all fields of finance, such as asset pricing, corporate finance, financial econometrics, banking, international finance, microstructure, behavioural finance, etc. The Editorial Team is willing to take risks on innovative research, controversial papers, and unusual approaches. We are also particularly interested in work produced by young scholars. The composition of the editorial board reflects such goals.