CHIKHI, Mohamed (2011): Analyse du choc informationnel et de l’hétéroscédasticité conditionnelle dans les flux de trésorerie. Published in: Recherches Economiques et Managériales , Vol. 9, (June 2011): pp. 1-15.
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Abstract
Résumé: Cet article analyse le comportement cyclique des flux de trésorerie et notamment ses propriétés statistiques à travers une classe de modèles ARMA avec erreur GARCH, notée ARIMA-GARCH ; cette classe inclut une tendance stochastique, la dépendance à court terme ainsi que le terme d’erreur hétéroscédastique à mémoire courte. Nous étudions les flux hebdomadaires de trésorerie de 2006 à 2008. Les résultats prédictifs montrent que les chocs informationnels ont des conséquences transitoires sur la volatilité et que le modèle ARIMA-GARCH montre une supériorité évidente sur le modèle de marche aléatoire. Une des conclusions est que l’hypothèse de prévisibilité est acceptée pour la série des flux de trésorerie étudiée sur une période toute historique.
Abstract: This article analyzes the cyclical behavior of cash flows and especially its statistical properties through a class of ARMA model with GARCH errors, denoted ARIMA-GARCH; This class includes a stochastic trend, short-term dependence, and short-term heteroskedastic error. We study the weekly cash flows from 2006 to 2008. The predictive results show that informational shocks have transitory consequences on volatility and the predictions from the ARIMA-GARCH model are also better than the predictions of the random walk model. Accordingly, the predictability hypothesis is accepted for the series of cash flows studied over a historical period.
Item Type: | MPRA Paper |
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Original Title: | Analyse du choc informationnel et de l’hétéroscédasticité conditionnelle dans les flux de trésorerie |
English Title: | Analysis of informational shock and conditional heteroscedasticity in cash flows |
Language: | French |
Keywords: | Mots-clé: Modèles ARMA, modèles GARCH, flux de trésorerie, chocs informationnels, marche aléatoire. Keywords: ARIMA models, GARCH models, cash flows, informational shocks, random walk |
Subjects: | C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C12 - Hypothesis Testing: General C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and Estimation C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C58 - Financial Econometrics |
Item ID: | 77269 |
Depositing User: | Mohamed CHIKHI |
Date Deposited: | 04 Mar 2017 01:52 |
Last Modified: | 02 Oct 2019 04:31 |
References: | Akaike, H. (1970), Statistical Predictor Identification, Annals of Institute of Statistical Mathematics, 22, 203 217. Andersen, T. G., T. Bollerslev, P. F. Christo®ersen, and F. X. Diebold. (2006), Volatility and correlation forecasting. In G. Elliott, C. W. J. Granger, and A. Timmermann (eds.), Handbook of Economic Forecasting, Amsterdam: North-Holland, 778{878). Beran, J. (1994). Statistics for long-memory processes. Chapman & Hall, New York. Beran, J., Bhansali, R.J., Ocker, D. (1998). On unified model selection for stationary and nonstationary short-and long-memory autoregressive processes. Biometrika, 85, 921–934. Bollerslev, T. (1986). Generalized Autoregressive Conditional Hetreoskedasticity. J. Econometrics, 31, 307-327. Brock, W. A., Dechert, W. D., and Scheinkman, J. (1987). “A test for independence based on the correlation dimension.” Discussion Paper 8702, University of Wisconsin-Madison. Chen, M. and An, H.Z. (1998). A note on the stationarity and the existence of moments of the GARCH model. Statistica Sinica, 8, 505–510. Davidson, J., Terasvirta, T.T. (Eds.), (2002), Long Memory and Nonlinear Time Series, Journal of Econometrics, 110 (2) 105–437. Engle, R.F. (1982). Autoregressive conditional heteroskedasticity with estimation of U.K. inflation. Econometrica, 50 987–1008. Feng, Y. (2004). Non- and Semiparametric Regression with Fractional Time Series Errors – 22 Theory and Applications to Financial Data. Habilitation Monograph, University of Konstanz. Geweke, J., Porter-Hudak,S., (1983), The estimation and application of long-memory time series models, Journal of Time Series Analysis, 4, 221-238. Granger, C. W. J. and Joyeux, R. (1980). An introduction to long-memory time series models and fractional differencing. J. Time Ser. Anal, 1, 15-30. Guy, M. (1990), Méthodes de prévision à court terme, Bruxelles : Edition Ellipses Kwiatkowski, D.; Phillips, P.; Schmidt, P.; Shin, Y.: “Testing the Null Hypothesis of Stationary Against the Alternative of a Unit Root: How Sure are we that Economic Time Series have a Unit Root?”, Journal of Econometrics, 54, 1992, pp. 159-178. Lavoyer, J.C et Ternisien, M. (1989), Le tableau des flux de trésorerie. La ville EGUERIN Editions, Paris. Nelson, D.B. (1991), Conditional herteroskedasticity in Asset Returns: A new Approach. Econometrica, 59, 347–370. Phillips, P.C.B. and P. Perron (1988). “Testing for Unit Roots in Time Series Regression,” Biometrika, 75, 335-346. Robinson, P.M. (1991). Testing for strong serial correlation and dynamic conditional heteroskedasticity in multiple regression. J. Econometr., 47, 6784. Schwarz, G. (1978), Estimating the dimension of a Model. Annals of Statistics, 6,461-464. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/77269 |