{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T05:50:57Z","timestamp":1791525057336,"version":"4.3.4"},"posted":{"date-parts":[[2026]]},"group-title":"SSRN","reference-count":0,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.uspto.gov\/ip-policy\/copyright-policy\/copyright-basics"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>Dynamically evolving co-movement in asset prices poses many challenges for asset allocation and risk management, and especially so for highly correlated assets. In this paper, we apply dynamic network analysis to identify and compare patterns in asset return co-movement structure for a set of exchange traded funds (ETFs). This asset class choice is made since common holdings may result in high correlation among ETFs. Using a portfolio allocation formulation in conjunction with the co-movement network analysis, we demonstrate the implications of co-movement structure at different time windows in a planning period, which includes the stressed COVID years. The formulation helps identify epochs when idiosyncratic risk is pronounced and create a trade-off in portfolio allocation between systematic and idiosyncratic risk, while maintaining the target returns. In periods of low or high idiosyncratic risk, the diversification objectives are better served, especially reducing idiosyncratic risk when it is pronounced. This asset allocation approach is particularly beneficial for asset classes like ETFs, which may have high and varying degree of co-movement.<\/jats:p>","DOI":"10.2139\/ssrn.7581162","type":"posted-content","created":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T05:15:30Z","timestamp":1791522930000},"source":"Crossref","is-referenced-by-count":0,"title":["Dynamic Network Analysis for Optimal Asset Allocation in Exchange Traded Funds"],"prefix":"10.2139","author":[{"given":"Aparna","family":"Gupta","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zezhong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammed A.","family":"Hasan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","container-title":[],"original-title":[],"deposited":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T05:15:30Z","timestamp":1791522930000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ssrn.com\/abstract=7581162"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":0,"URL":"https:\/\/doi.org\/10.2139\/ssrn.7581162","relation":{},"subject":[],"published":{"date-parts":[[2026]]},"subtype":"preprint"}}