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Modelling multivariate moments in European Stock Markets

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  • Ignacio Mauleon

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.

Suggested Citation

  • Ignacio Mauleon, 2006. "Modelling multivariate moments in European Stock Markets," The European Journal of Finance, Taylor & Francis Journals, vol. 12(3), pages 241-263.
    • Handle: RePEc:taf:eurjfi:v:12:y:2006:i:3:p:241-263
    DOI: 10.1080/13518470500249233
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    References listed on IDEAS

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    1. Kee-Hong Bae & G. Andrew Karolyi & René M. Stulz, 2003. "A New Approach to Measuring Financial Contagion," The Review of Financial Studies, Society for Financial Studies, vol. 16(3), pages 717-763, July.
    2. Fenton, Victor M & Gallant, A Ronald, 1996. "Erratum [Convergence Rates of SNP Density Estimators]," Econometrica, Econometric Society, vol. 64(6), pages 1493-1493, November.
    3. Hwang, Soosung & Satchell, Stephen E, 1999. "Modelling Emerging Market Risk Premia Using Higher Moments," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 4(4), pages 271-296, October.
    4. Fenton, Victor M & Gallant, A Ronald, 1996. "Convergence Rates of SNP Density Estimators," Econometrica, Econometric Society, vol. 64(3), pages 719-727, May.
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    Cited by:

    1. Inés Jiménez & Andrés Mora-Valencia & Javier Perote, 2022. "Dynamic selection of Gram–Charlier expansions with risk targets: an application to cryptocurrencies," Risk Management, Palgrave Macmillan, vol. 24(1), pages 81-99, March.
    2. Adcock, C J & Meade, N, 2017. "Using parametric classification trees for model selection with applications to financial risk management," European Journal of Operational Research, Elsevier, vol. 259(2), pages 746-765.
    3. Jiménez, Inés & Mora-Valencia, Andrés & Perote, Javier, 2023. "Multivariate dynamics between emerging markets and digital asset markets: An application of the SNP-DCC model," Emerging Markets Review, Elsevier, vol. 56(C).

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