On Asymmetric Correlations and Their Applications in Financial Markets

Linyu Cao, Ruili Sun (), Tiefeng Ma and Conan Liu
Additional contact information
Linyu Cao: College of Mathematics and Information Science, Zhengzhou University of Light Industry, Zhengzhou 450001, China
Ruili Sun: College of Mathematics and Information Science, Zhengzhou University of Light Industry, Zhengzhou 450001, China
Tiefeng Ma: School of Statistics, Southwestern University of Finance and Economics, Chengdu 611130, China
Conan Liu: UNSW Business School, University of New South Wales, Sydney, NSW 2052, Australia

JRFM, 2023, vol. 16, issue 3, 1-18

Abstract: Progress on asymmetric correlations of asset returns has recently advanced considerably. Asymmetric correlations can cause problems in hedging effectiveness and overstate the value of diversification. Furthermore, considering the asymmetric correlations in portfolio construction significantly enhances performance. The purpose of this paper is to trace developments and identify areas that require further research. We examine three aspects of asymmetric correlations: first, the existence of asymmetric correlations between asset returns and their significance tests; second, the test on the existence of asymmetric correlations between different markets and financial assets; and third, the root cause analysis of asymmetric correlations. In the first part, the contents of extreme value theory, the H statistic and a model-free test are covered. In the second part, commonly used models such as copula and GARCH are included. In addition to the GARCH and copula formulations, many other methods are included, such as regime switching, the Markov switching model, and the multifractal asymmetric detrend cross-correlation analysis method. In addition, we compare the advantages and differences between the models. In the third part, the causes of asymmetry are discussed, for example, higher common fundamental risk, correlation of individual fundamental risk, and so on.

Keywords: asymmetric correlation; statistical test; copula; GARCH (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1911-8074/16/3/187/pdf (application/pdf)
https://www.mdpi.com/1911-8074/16/3/187/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jjrfmx:v:16:y:2023:i:3:p:187-:d:1092699

Access Statistics for this article

JRFM is currently edited by Ms. Chelthy Cheng

More articles in JRFM from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().