{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/portfolio-allocation-under-asymmetric","title":"Portfolio Allocation under Asymmetric Dependence in Asset Returns using Local Gaussian Correlations","arxiv_id":"2106.12425","date":"2021-06-03","proceeding":null,"authors":["Anders D. Sleire","Bård Støve","Håkon Otneim","Geir Drage Berentsen","Dag Tjøstheim","Sverre Hauso Haugen"],"abstract":"It is well known that there are asymmetric dependence structures between financial returns. In this paper we use a new nonparametric measure of local dependence, the local Gaussian correlation, to improve portfolio allocation. We extend the classical mean-variance framework, and show that the portfolio optimization is straightforward using our new approach, only relying on a tuning parameter (the bandwidth). The new method is shown to outperform the equally weighted (1/N) portfolio and the classical Markowitz portfolio for monthly asset returns data.","url_abs":"https://arxiv.org/abs/2106.12425v1","url_pdf":"https://arxiv.org/pdf/2106.12425v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"portfolio-allocation-under-asymmetric","repo_url":"https://gitlab.com/sleire/lgportf","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"portfolio-optimization","task_name":"Portfolio Optimization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}