EconBase
← All papers

Tensor dynamic conditional correlation model: A new way to pursuit "Holy Grail of investing"

Cheng Yu, Zhoufan Zhu, Ke Zhu

arXiv 19 Feb 2025 · Finance — Portfolio Management

arXiv:2502.13461 · PDF · DOI · OpenAlex · Extracted main text

Abstract

Style investing creates asset classes (or the so-called "styles") with low correlations, aligning well with the principle of "Holy Grail of investing" in terms of portfolio selection. The returns of styles naturally form a tensor-valued time series, which requires new tools for studying the dynamics of the conditional correlation matrix to facilitate the aforementioned principle. Towards this goal, we introduce a new tensor dynamic conditional correlation (TDCC) model, which is based on two novel treatments: trace-normalization and dimension-normalization. These two normalizations adapt to the tensor nature of the data, and they are necessary except when the tensor data reduce to vector data. Moreover, we provide an easy-to-implement estimation procedure for the TDCC model, and examine its finite sample performance by simulations. Finally, we assess the usefulness of the TDCC model in international portfolio selection across ten global markets and in large portfolio selection for 1800 stocks from the Chinese stock market.

Citation extraction

33
references
64
in-text mentions
33
distinct cited
1
self-citations
12,170
main-text words

appendix boundary found by none_found · 100% of the source is main text. Read the extracted text to check this.

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
1Engle, R. F., Ledoit, O., Wolf, M (2019) Large dynamic covariance matrices1.00095100%
2Engle, R. F (2002) Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models1.00074100%
3Yu, C., Li, D., Jiang, F., Zhu, K (2024) Matrix GARCH model: Inference and application self0.87462100%
4Bollerslev, T (1986) Generalized autoregressive conditional heteroskedasticity0.81142100%
5Li, Z., Xiao, H (2021) Multi-linear tensor autoregressive models0.73732100%
6Engle, R. F., Kroner, K. F (1995) Multivariate simultaneous generalized ARCH0.64422100%
7Ledoit, O., Wolf, M (2004) A well-conditioned estimator for large-dimensional covariance matrices0.64422100%
8Ledoit, O., Wolf, M (2012) Nonlinear shrinkage estimation of large-dimensional covariance matrices0.64422100%
9Barberis, N., Shleifer, A (2003) Style investing0.51121100%
10Dalio, R (2017) Principles: Life and Work (1st Edition)0.51121100%

Showing the top 10 of 33 scored citations.