考虑到传统投资组合理论的局限, 为了解决参数不确定问题, 提高投资组合效用, 应用贝叶斯理论, 在多维有偏分布基础上讨论了考虑偏度的投资组合问题, 通过对期望效用函数的Taylor展开, 分析了各阶矩风险与期望效用函数的关系. 应用MCMC方法和数值优化方法对有偏正态分布进行了参数估计和投资组合权重的计算. 研究结果表明, 应用贝叶斯理论解决参数不确定问题, 可以提高投资者期望效用, 考虑期望收益, 方差以及偏度不确定性会对投资组合策略产生重要的影响.
Abstract
Given the limitation of traditional portfolio theory, portfolio model using a Bayesian decision theory and skew-normal distributions was proposed to solve the problem of parameter uncertainty and improve utility of portfolio. Then, based on Taylor series expansion of expected utility, we analysed the relationship between the higher moment risk and utility, and employed the MCMC and numerical optimization algorithm which estimated the parameters of skew-normal distributions and the weights of portfolio. Our results suggest that expected utility can be improved using Bayesian Theory which solves the problem of parameter uncertainty. Further, it is important to incorporate mean, variance and skewness in portfolio selection strategy.
关键词
参数不确定 /
偏度 /
投资组合 /
多维偏正态分布
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Key words
parameter uncertainty /
skewness /
portfolio /
multivariate skew-normal distributions
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中图分类号:
F830.91
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