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L’effet d’intervalle sur le marché à terme de la Bourse de Bruxelles

Published online by Cambridge University Press:  17 August 2016

Natacha Defrère
Natacha Defrère*
Affiliation:
Université de Liège
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Résumé

Cet article traite de l’effet d’intervalle sur le marché à terme de la Bourse de Bruxelles. L’analyse empirique est menée sur les actions belges cotées sur ce marché pour une période allant de l’introduction du Computer Assisted Trading System (C.A.T.S.), début 1989, au 31 décembre 1992. La présence d’un effet d’intervalle est mise en évidence, l’estimation du risque systématique des 19 actions composant l’échantillon dépend de la longueur de l’intervalle de temps choisi pour calculer les rendements. L’estimation des betas à l’aide d’un modèle GARCH ne donne pas de meilleurs résultats que la régression linéaire simple et les valeurs des betas dépendent toujours de la longueur de l’intervalle de temps. L’effet d’intervalle semble donc ne pas être dû à la présence d’hétéroscédasticité conditionnelle. Il ressort également que, sur base de données journalières, les modèles proposés par Scholes et Williams et par Dimson qui ajustent le beta pour tenir compte de la faible fréquence des échanges, donnent aux coefficients une valeur plus proche de leurs valeurs asymptotiques que le fait la régression linéaire simple.

Summary

Summary

In this paper, we analyse the intervalling effect on the Brussels Forward Market. The empirical study is carried on nineteen Belgian stocks quoted on this Market from the introduction of the Computer Assisted Trading System (C.A.T.S.), beginning of 1989, until the 31st of December 1992. The existence of an intervalling effect on the Brussels Forward Market is highlighted, the estimated systematic risks depend on the length of the interval chosen to calculate the returns. As far as the Generalized AutoRegressive Conditional Heteroscedasticity (GARCH) model is concerned, the results show that the beta coefficients still depend on the differencing interval and do not perform better than the Ordinary Least Square estimated betas. Thus the intervalling effect doesn’t seem to be due to conditional heteroscedasticity. It also appears that the Scholes and Williams’ and the Dimson’s models produce beta coefficients for a one-day interval which are closer to the asymptotic betas than the Ordinary Least Square estimated betas based on daily observations. We may therefore conclude that models such as Scholes and Williams’ and Dimson’s can “improve” the estimated beta coefficients.

Keywords

G14.G14.
Type
Research Article
Information
Recherches Économiques de Louvain/ Louvain Economic Review , Volume 61 , Issue 4 , 1995 , pp. 509 - 526
Copyright
Copyright © Université catholique de Louvain, Institut de recherches économiques et sociales 1995 

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References

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