Open Access
June 2016 Asymmetric conditional correlations in stock returns
Hui Jiang, Patrick W. Saart, Yingcun Xia
Ann. Appl. Stat. 10(2): 989-1018 (June 2016). DOI: 10.1214/16-AOAS924


Modeling and estimation of correlation coefficients is a fundamental step in risk management, especially with the aftermath of the financial crisis in 2008, which challenged the traditional measuring of dependence in the financial market. Because of the serial dependence and small signal-to-noise ratio, patterns of the dependence in the data cannot be easily detected and modeled. This paper introduces a common factor analysis into the conditional correlation coefficients to extract the features of dependence. While statistical properties are thoroughly derived, extensive empirical analysis provides us with common patterns for the conditional correlation coefficients that give new insight into a number of important questions in financial data, especially the asymmetry of cross-correlations and the factors that drive the cross-correlations.


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Hui Jiang. Patrick W. Saart. Yingcun Xia. "Asymmetric conditional correlations in stock returns." Ann. Appl. Stat. 10 (2) 989 - 1018, June 2016.


Received: 1 May 2015; Revised: 1 March 2016; Published: June 2016
First available in Project Euclid: 22 July 2016

zbMATH: 06625678
MathSciNet: MR3528369
Digital Object Identifier: 10.1214/16-AOAS924

Keywords: Conditional cross-correlation coefficient , kernel smoothing , reduced rank model , semiparametric models

Rights: Copyright © 2016 Institute of Mathematical Statistics

Vol.10 • No. 2 • June 2016
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