Hostname: page-component-78c5997874-t5tsf Total loading time: 0 Render date: 2024-11-19T04:11:41.409Z Has data issue: false hasContentIssue false
  • English
  • Français

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
Get access

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 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bibliographie

Bollerslev, T. [1986], Generalized Auto-Regressive Conditional Heteroscedasticity, Journal of Econometrics, 31(3), pp. 307–327.Google Scholar
Bollerslev, T. [1987], A Conditionally Heteroskedastic Time Series Model for Security Prices and Rates of Return Data, Review of Economic and Statistics, 59(3), pp. 542–547.Google Scholar
Cohen, K.J., Hawawini, G.A. Maier, G.A. Schwarz, R.A. et Withcomb, D.K. [1983a], Estimating and Adjusting for the Intervalling Effect Bias in Beta, Management Science, 29(1), pp. 135–148.Google Scholar
Cohen, K.J., Hawawini, G.A. Maier, S.F. Schwarz, R.A. et Withcomb, D.K. [1983b], Friction in the Trading Process and the Estimation of Systematic Risk, Journal of Financial Economics, 12(2), pp. 263–278.Google Scholar
Corhay, A. [1992], The intervalling effect bias in beta: A note, Journal of Banking and Finance, 16(1), pp. 61–73.Google Scholar
Corhay, A. et Tourani Rad, A. [1992], Return interval, firm size and systematic risk on the Dutch Stock Market, Research Memorandum n° 28, Rijksuniversiteit Limburg, Maastricht.Google Scholar
Corhay, A. et Tourani Rad, A. [1994], Statistical Properties of Daily Returns : Evidence from European Stock Market, Journal of Business Finance and Accounting, 21(2), pp. 271–282.Google Scholar
Dimson, E. [1979], Risk Measurement when Shares are subject to Infrequent Trading, Journal of Financial Economics, 7(2), pp. 197–226.Google Scholar
Engle, R. [1982], Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of UK Inflation, Econometrica, 50(4), pp. 987–1008.Google Scholar
Hawawini, G.A. [1980], Intertemporal Cross-Dependence in Securities Daily Returns and the Short-Run Intervalling Effect on Systematic Risk, Journal of Finance and Quantitative Analysis, 15(1), pp. 139–146.Google Scholar
Pogue, G.A. et Solnik, B.H. [1974], The Market Model Applied to European Common Stocks: Some Empirical Results, Journal of Finance and Quantitative Analysis, 9(6), pp. 917–944.Google Scholar
Scholes, M. et Williams, J. [1977], Estimating betas from Non-Synchronous Data, Journal of Financial Economics, 5(3), pp. 309–327.Google Scholar