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基于对数先验的协方差矩阵的参数估计
Parameter Estimation of Covariance Matrix Based on Logarithmic Priors

DOI: 10.12677/pm.2024.145202, PP. 479-488

Keywords: 协方差矩阵估计,Black-Litterman模型,投资者情绪,对数先验
Covariance Matrix Estimation
, Black-Litterman Model, Investor Sentiment, Logarithmic Prior

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Abstract:

为了更好地估计股票收益协方差矩阵,提出了一种基于Black-Litterman思想的协方差矩阵估计方法。不同于传统方法对协方差矩阵添加Wishart先验分布的方法,考虑将先验信息概念应用在协方差矩阵的对数上,通过贝叶斯方法,得到协方差矩阵的参数估计。
In order to better estimate the covariance matrix of stock return, a method of covariance matrix estimation based on Black-Litterman idea is proposed. Different from the traditional method of adding Wishart prior distribution to the covariance matrix, the concept of prior information is applied to the logarithm of the covariance matrix, and the parameter estimation of the covariance matrix is obtained by Bayesian method.

References

[1]  Markowitz, H.M. (1952) Portfolio Selection. The Journal of Finance, 7, 77-91.
https://doi.org/10.1111/j.1540-6261.1952.tb01525.x
[2]  Black, F. and Litterman, R. (1992) Global Portfolio Optimization. Financial Analysts, 48, 28-43.
https://doi.org/10.2469/faj.v48.n5.28
[3]  Andrei, M.S. and Hsu, J. (2020) A Bayesian Approach for Asset Allocation. International Journal of Statistics and Probability, 9,1-14.
https://doi.org/10.5539/ijsp.v9n4p1
[4]  Leonard, T. and Hsu, J. (1992) Bayesian Inference for a Covariance Matrix. The Annals of Statistics, 20, 1669-1696.
https://doi.org/10.1214/aos/1176348885
[5]  Bellman, R. (1997) Introduction to Matrix Analysis. Society for Industrial and Applied Mathematics.
https://doi.org/10.1137/1.9781611971170
[6]  Satchell, S. and Scowcorft, A. (2007) A Demystification of the Black-Litterman Model: Managing Quantitative and Traditional Portfolio Construction. Forecasting Expected Returns in the Financial Markets, 39-53.
https://doi.org/10.1016/B978-075068321-0.50004-2

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