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作 者:BAI Wei ZHANG Junting LIU Haifei LIU Kai
机构地区:[1]School of Management and Engineering,Nanjing University,Nanjing 210093,China [2]Postdoctoral Workstation of Bank of Jiangsu Co.,Ltd,Nanjing 210006,China [3]College of Finance,Nanjing Agricultural University,Nanjing 210095,China [4]University of Prince Edward Island,550 University Ave,Charlottetown,PE C1A4P3,Canada
出 处:《Journal of Systems Science & Complexity》2024年第3期1052-1079,共28页系统科学与复杂性学报(英文版)
基 金:supported by the Chinese National Science Foundation under Grant Nos. U1811462,71771116;the Ministry of Education;Late-stage Subsidy Project for Philosophical and Social Sciences Research Foundation under Grant No. 18JHQ058。
摘 要:In order to build a low-risk Fund of Funds(FOF), from the perspective of correlation, the principal component factor is used to improve the traditional risk parity model. Principal component analysis is used to decompose the underlying assets and generate unrelated principal component factors,and then the authors can construct a principal component risk parity portfolio. The proposed empirical results based on China’s mutual fund market show that the performance of principal component risk parity portfolio(PCRPP) is better than that of equal weight portfolio(EWP) and traditional risk parity portfolio(RPP). That is to say, not only the PCRPP in this paper has much lower risk than EWP and RPP, but also slightly better than EWP and RPP in terms of average return. Moreover, the study of dividing the underlying assets shows that the PCRPP in this paper is not sensitive to the underlying assets. The PCRPP in this paper is better than EWP and RPP for both the better performing funds and the worse performing funds. In addition, the empirical results on dynamic portfolio adjustments show that it is not appropriate to adjust asset allocation too frequently when the expected rate of return is calculated using the arithmetic mean.
关 键 词:Fund of funds(FOF) mutual funds portfolio risk principal component analysis risk parity portfolio
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