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Sequential conditional correlations: Inference and evaluation

[journal article]

Palandri, Alessandro

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Abstract This paper presents a new approach to the modeling of conditional correlation matrices within the multivariate GARCH framework. The procedure, which consists in breaking the matrix into the product of a sequence of matrices with desirable characteristics, in effect converts a highly dimensional and intractable optimization problem into a series of simple and feasible estimations. This in turn allows for richer parameterizations and complex functional forms for the single components. An empirical application involving the conditional second moments of 69 selected stocks from the NASDAQ100 shows how the new procedure results in strikingly accurate measures of the conditional correlations.
Classification Political Economy; Economic Statistics, Econometrics, Business Informatics
Free Keywords C51; C52; C61; G1; Multivariate GARCH; High Dimensional GARCH models; Conditional correlations; Sequential estimation
Document language English
Publication Year 2009
Page/Pages p. 122-132
Journal Journal of Econometrics, 153 (2009) 2
DOI http://dx.doi.org/10.1016/j.jeconom.2009.05.002
Status Postprint; reviewed
Licence PEER Licence Agreement (applicable only to documents from PEER project)
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