本文修正了Simone Brands et al(2004)研究中采用的股票配置和行业配置集中度指标,并采用两阶段回归方法,首先基于日数据回归得到基金不同季度收益,然后应用非平衡paneldata模型对30只样本基金季度超额收益与资产配置集中度数据进行研...本文修正了Simone Brands et al(2004)研究中采用的股票配置和行业配置集中度指标,并采用两阶段回归方法,首先基于日数据回归得到基金不同季度收益,然后应用非平衡paneldata模型对30只样本基金季度超额收益与资产配置集中度数据进行研究。研究结果显示,股票选择上的资产配置集中度给基金带来了超额收益,而行业资产配置过度集中给基金带来了损失大于收益,且较大的资产规模并没有给基金带来过高的收益。展开更多
We introduce a new approach for optimal portfolio choice under model ambiguity by incorporating predictable forward preferences in the framework of Angoshtari et al.[2].The investor reassesses and revises the model am...We introduce a new approach for optimal portfolio choice under model ambiguity by incorporating predictable forward preferences in the framework of Angoshtari et al.[2].The investor reassesses and revises the model ambiguity set incrementally in time while,also,updating his risk preferences forward in time.This dynamic alignment of preferences and ambiguity updating results in time-consistent policies and provides a richer,more accurate learning setting.For each investment period,the investor solves a worst-case portfolio optimization over possible market models,which are represented via a Wasserstein neighborhood centered at a binomial distribution.Duality methods from Gao and Kleywegt[10];Blanchet and Murthy[8]are used to solve the optimization problem over a suitable set of measures,yielding an explicit optimal portfolio in the linear case.We analyze the case of linear and quadratic utilities,and provide numerical results.展开更多
文摘本文修正了Simone Brands et al(2004)研究中采用的股票配置和行业配置集中度指标,并采用两阶段回归方法,首先基于日数据回归得到基金不同季度收益,然后应用非平衡paneldata模型对30只样本基金季度超额收益与资产配置集中度数据进行研究。研究结果显示,股票选择上的资产配置集中度给基金带来了超额收益,而行业资产配置过度集中给基金带来了损失大于收益,且较大的资产规模并没有给基金带来过高的收益。
文摘We introduce a new approach for optimal portfolio choice under model ambiguity by incorporating predictable forward preferences in the framework of Angoshtari et al.[2].The investor reassesses and revises the model ambiguity set incrementally in time while,also,updating his risk preferences forward in time.This dynamic alignment of preferences and ambiguity updating results in time-consistent policies and provides a richer,more accurate learning setting.For each investment period,the investor solves a worst-case portfolio optimization over possible market models,which are represented via a Wasserstein neighborhood centered at a binomial distribution.Duality methods from Gao and Kleywegt[10];Blanchet and Murthy[8]are used to solve the optimization problem over a suitable set of measures,yielding an explicit optimal portfolio in the linear case.We analyze the case of linear and quadratic utilities,and provide numerical results.
