Is a correlation-based investment strategy beneficial for long-term international portfolio investors?

被引:0
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作者
Seema Wati Narayan
Mobeen Ur Rehman
Yi-Shuai Ren
Chaoqun Ma
机构
[1] Asia Pacific Applied Economics Association,School of Public Administration
[2] Shaheed Zulfikar Ali Bhutto Institute of Science and Technology (SZABIST),Research Institute of Digital Society and Blockchain
[3] South Ural State University,Centre for Resource and Environmental Management
[4] Hunan University,The Energy Centre
[5] Hunan University,Business School
[6] Hunan University,undefined
[7] University of Auckland,undefined
[8] Hunan University,undefined
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关键词
Portfolio diversification; Portfolio mix; Asia; Central and Eastern Europe; Middle East North Africa; Latin America; G11; G15; F3; F65;
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摘要
Using negative to low-correlated assets to manage short-term portfolio risk is not uncommon among investors, although the long-term benefits of this strategy remain unclear. This study examines the long-term benefits of the correlation strategy for portfolios based on the stock market in Asia, Central and Eastern Europe, the Middle East and North Africa, and Latin America from 2000 to 2016. Our strategy is as follows. We develop five portfolios based on the average unconditional correlation between domestic and foreign assets from 2000 to 2016. This yields five regional portfolios based on low to high correlations. In the presence of selected economic and financial conditions, long-term diversification gains for each regional portfolio are evaluated using a panel cointegration-based testing method. Consistent across all portfolios and regions, our key cointegration results suggest that selecting a low-correlated portfolio to maximize diversification gains does not necessarily result in long-term diversification gains. Our empirical method, which also permits the estimation of cointegrating regressions, provides the opportunity to evaluate the impact of oil prices, U.S. stock market fluctuations, and investor sentiments on regional portfolios, as well as to hedge against these fluctuations. Finally, we extend our data to cover the years 2017–2022 and find that our main findings are robust.
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