Tangency portfolio weights under a skew-normal model in small and large dimensions
(2023) In Journal of the Operational Research Society- Abstract
- In this paper, we investigate the distributional properties of the estimated tangency portfolio (TP) weights assuming that the asset returns follow a matrix variate closed skew-normal distribution. We establish a stochastic representation of the linear combination of the estimated TP weights that fully characterizes its distribution. Using the stochastic representation we derive the mean and variance of the estimated weights of TP which are of key importance in portfolio analysis. Furthermore, we provide the asymptotic distribution of the linear combination of the estimated TP weights under the high-dimensional asymptotic regime, i.e., the dimension of the portfolio p and the sample size n tend to infinity such that p/n→c∈(0,1). A good... (More)
- In this paper, we investigate the distributional properties of the estimated tangency portfolio (TP) weights assuming that the asset returns follow a matrix variate closed skew-normal distribution. We establish a stochastic representation of the linear combination of the estimated TP weights that fully characterizes its distribution. Using the stochastic representation we derive the mean and variance of the estimated weights of TP which are of key importance in portfolio analysis. Furthermore, we provide the asymptotic distribution of the linear combination of the estimated TP weights under the high-dimensional asymptotic regime, i.e., the dimension of the portfolio p and the sample size n tend to infinity such that p/n→c∈(0,1). A good performance of the theoretical findings is documented in the simulation study. In an empirical study, we apply the theoretical results to real data of the stocks included in the S&P 500 index. (Less)
- Abstract (Swedish)
- In this paper, we investigate the distributional properties of the estimated tangency portfolio (TP) weights assuming that the asset returns follow a matrix variate closed skew-normal distribution. We establish a stochastic representation of the linear combination of the estimated TP weights that fully characterizes its distribution. Using the stochastic representation we derive the mean and variance of the estimated weights of TP which are of key importance in portfolio analysis. Furthermore, we provide the asymptotic distribution of the linear combination of the estimated TP weights under the high-dimensional asymptotic regime, i.e., the dimension of the portfolio p and the sample size n tend to infinity such that p/n→c∈(0,1). A good... (More)
- In this paper, we investigate the distributional properties of the estimated tangency portfolio (TP) weights assuming that the asset returns follow a matrix variate closed skew-normal distribution. We establish a stochastic representation of the linear combination of the estimated TP weights that fully characterizes its distribution. Using the stochastic representation we derive the mean and variance of the estimated weights of TP which are of key importance in portfolio analysis. Furthermore, we provide the asymptotic distribution of the linear combination of the estimated TP weights under the high-dimensional asymptotic regime, i.e., the dimension of the portfolio p and the sample size n tend to infinity such that p/n→c∈(0,1). A good performance of the theoretical findings is documented in the simulation study. In an empirical study, we apply the theoretical results to real data of the stocks included in the S&P 500 index. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/06d3abd3-15f1-493c-8fbb-393613ea3499
- author
- Javed, Farrukh LU ; Mazur, Stepan and Thorsén, Erik
- organization
- publishing date
- 2023-09-06
- type
- Contribution to journal
- publication status
- epub
- subject
- keywords
- Asset allocation, tangency portfolio, matrix variate skew-normal distribution, stochastic representation, high-dimensional asymptotics
- in
- Journal of the Operational Research Society
- publisher
- Palgrave Macmillan
- external identifiers
-
- scopus:85169887404
- ISSN
- 0160-5682
- DOI
- 10.1080/01605682.2023.2249935
- language
- English
- LU publication?
- yes
- id
- 06d3abd3-15f1-493c-8fbb-393613ea3499
- date added to LUP
- 2023-10-30 22:18:14
- date last changed
- 2023-10-31 11:14:02
@article{06d3abd3-15f1-493c-8fbb-393613ea3499,
abstract = {{In this paper, we investigate the distributional properties of the estimated tangency portfolio (TP) weights assuming that the asset returns follow a matrix variate closed skew-normal distribution. We establish a stochastic representation of the linear combination of the estimated TP weights that fully characterizes its distribution. Using the stochastic representation we derive the mean and variance of the estimated weights of TP which are of key importance in portfolio analysis. Furthermore, we provide the asymptotic distribution of the linear combination of the estimated TP weights under the high-dimensional asymptotic regime, i.e., the dimension of the portfolio p and the sample size n tend to infinity such that p/n→c∈(0,1). A good performance of the theoretical findings is documented in the simulation study. In an empirical study, we apply the theoretical results to real data of the stocks included in the S&P 500 index.}},
author = {{Javed, Farrukh and Mazur, Stepan and Thorsén, Erik}},
issn = {{0160-5682}},
keywords = {{Asset allocation; tangency portfolio; matrix variate skew-normal distribution; stochastic representation; high-dimensional asymptotics}},
language = {{eng}},
month = {{09}},
publisher = {{Palgrave Macmillan}},
series = {{Journal of the Operational Research Society}},
title = {{Tangency portfolio weights under a skew-normal model in small and large dimensions}},
url = {{http://dx.doi.org/10.1080/01605682.2023.2249935}},
doi = {{10.1080/01605682.2023.2249935}},
year = {{2023}},
}