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Low Volatility Stock Portfolio Through High Dimensional Bayesian Cointegration

Parley R Yang, Alexander Y Shestopaloff

arXiv 14 Jul 2024 · Statistics — Applications

arXiv:2407.10175 · PDF · DOI · OpenAlex · Extracted main text

Abstract

We employ a Bayesian modelling technique for high dimensional cointegration estimation to construct low volatility portfolios from a large number of stocks. The proposed Bayesian framework effectively identifies sparse and important cointegration relationships amongst large baskets of stocks across various asset spaces, resulting in portfolios with reduced volatility. Such cointegration relationships persist well over the out-of-sample testing time, providing practical benefits in portfolio construction and optimization. Further studies on drawdown and volatility minimization also highlight the benefits of including cointegrated portfolios as risk management instruments.

Citation extraction

13
references
18
in-text mentions
13
distinct cited
1
self-citations
5,680
main-text words

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Parley Ruogu Yang and Alexander Y Shestopaloff (2023) Bayesian Analysis of High Dimensional Vector Error Correction Model self0.64422100%
2Gabriel Francisco Borrageiro, Nick Firoozye, and Paolo Barucca (2022) Sequential asset ranking in nonstationary time series. Association for Computing Machinery, New York, NY, USA0.51121100%
3Yiming Peng and Vadim Linetsky (2022) Portfolio Selection: A Statistical Learning Approach. Association for Computing Machinery, New York, NY, USA0.51121100%
4Edward Turner and Mihai Cucuringu (2023) Graph Denoising Networks: A Deep Learning Framework for Equity Portfolio Construction. Association for Computing Machinery, New…0.51121100%
5Liu Ziyin, Kentaro Minami, and Kentaro Imajo (2022) Theoretically Motivated Data Augmentation and Regularization for Portfolio Construction. Association for Computing Machinery, Ne…0.51121100%
6Joshua Brodie, Ingrid Daubechies, Christine De Mol, Domenico Giannon… (2009) Sparse and stable Markowitz portfolios0.40511100%
7Robert F. Engle and C. W. J. Granger (1987) Co-Integration and Error Correction: Representation, Estimation, and Testing0.40511100%
8John Van Der Hoek Robert J. Elliott and William P. Malcolm (2005) Pairs trading0.40511100%
9Chong Liang and Melanie Schienle (2019) Determination of vector error correction models in high dimensions0.40511100%
10Harry Markowitz (1952) Portfolio Selection0.40511100%

Showing the top 10 of 13 scored citations.