 Modelling multivariate moments in European Stock MarketsThe European Journal of Finance, 2006, vol. 12, issue 3, 241-263 Abstract: This research extends the results of Mauleon and Perote, and derives analytically a general framework for the multivariate Edgeworth Sargan (ES) density. Its capability to account for multivariate moments beyond correlation is shown-mainly, co-skewness, co-kurtosis and co-volatility. The multivariate ES is then fitted to the residuals of a VAR model applied to three European stock market series of daily data (FTSE, DAX, CAC40), accounting for univariate as well as multivariate departures from normality. The complete model - with nearly 60 parameters - is set up and estimated jointly by maximum likelihood. Two alternative multivariate probability density functions, student's t and the normal skewed, are also estimated and compared to the ES. The empirical results show: (1) in spite of the high nonlinearity and complexity of the model, it is feasible to fit it to empirical data; (2) statistically significant multivariate effects, other than correlations, are found, and (3) the tail fit of the ES is significantly better. Keywords: Multivariate ES density; co-skewness; co-kurtosis and co-volatility; European stock markets (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:eurjfi:v:12:y:2006:i:3:p:241-263 Ordering information: This journal article can be ordered from DOI: 10.1080/13518470500249233 Access Statistics for this article The European Journal of Finance is currently edited by Chris Adcock More articles in The European Journal of Finance from Taylor & Francis Journals |
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