Volatility and duration models for financial intaday data: formulation, estimation and evaluation
DOI:
https://doi.org/10.14738/abr.66.4216Abstract
This paper develops and tests empirically counting models for high frequency data: BIN (1,1) model with Poisson process, to check if this model allows to capture the clusturing phenomenon in the case of high frequence data, concening stocks intaday data. The process of estimation of the model using data generating process (DGP), then using the acutal data coming from three stocks of NYSE place (BOEING, DISNEY, and AWK), involves good results that validate model for generalisation to BIN(n,n) and for works on density forcasting. In this paper we studie the issue of adequacy of BIN models to capture the activities of financial markets about stocks intraday data (volume, quote, prices), and help to forecast the evolution of financial markets activities.
References
Aman, U. and David, E. A. G. (1998) “Handbook of applied economic statistics.” Marcel Dekker, inc. New York.
Bauwens, L. (1999) “Recent developments in the econometrics of financial markets Using intra-day data.” CORE discussion paper 1403, Université Catholique de Louvain (UCL).
Bauwens, L. and Giot, P. (1999) “The logarithmic ACD model: an application to the bid-ask quote process of three NYSE stocks.” CORE discussion paper 9789, (UCL).
Bauwens, L. and Veredas, D. (1999) “The stochastic conditional duration model: a latent factor model for the analysis of financial duration.” CORE discussion paper 9958, UCL Bera, A. K., and Higgins, M. L. “ARCH models: properties, estimation and testing.” Journal of Economic Survey, vol. 7, No. 4.
Colin Cameron and Pravin K. Trivedi (1998) “Regression analysis of count data.” Cambridge University Press.
Dawid, P. A. (1979) “Some misleading arguments involving conditional independence.” JRSS B 41, No. 2, pp. 249-252.
Devolder, P. (1993) “Finance stochastique.” Edition de l’Université de Bruxelles.
Droesbeke, J. Fichet B. and Tassi P. (1994) “Modelisation ARCH: Theorie statistique et application dans le domaine de la Finance.” Editions de l’Université de Bruxelles, Bruxelles.
Dyckman T. and Thomas L. (1977) “Fundamental Statistics for Business and Economics.” Prentice-Hall, Inc., Englewood Cliffs, New Jersey.
Engle, R. F. (1995), “ARCH selected readings”. Oxford University Press.
Engle, R. F., and Granger, C. W. J. (1991) “Long run economic relationships: Readings in cointegration”. Oxford University Press.
Engle, R. and Russell, J.(1998) “Autoregressive conditional duration, a new model for for irregularly spaced transaction data.” Econometrica 66, 1127-1162.
Hassane, M. (2018). Volatility and duration models for financial intaday data: formulation, estimation and evaluation. Archives of Business
GAUSS Applications (1995) “Constrained Maximum Likelihood.” Aptech Systems, Inc. Maple Valey, USA.
Giot, P. (1999) “Time transformations, intra-day data and volatility models.” CORE discussion paper 9944, UCL.
Greene, W. H. (1997), “Econometrics Analysis.” Prentice-Hall Inc. New York.
Harvey, A. C. (1990) “The Econometric Analysis of Time Series.” Second edition, Harvester Wheatsheaf. ------------------(1993) “Time Series Models.” Second Edition, Harvester Wheatsheaf.
Hendry, D. F. and ali (1993) “Cointegration, error correction, and the econometric analysis of non-stationary data.” Oxford University Press.
Laure, B. and Gervais, J. P. (1998) “L’Analyse des séries chronologiques: spécification et estimation des modèles univariés et multivariés.” CODESRIA, document spécial n° 9.
Lewis P. and Orav E. (1989) “Simulation methodology for Statisticians operations Analysts and Engineers.” Vol. 1 Wadsworth, Inc., Belmont, California.
Mills, T. C. (1999) “The Econometric Modeling of financial Time Series.” Second edition, Cambridge University Press.
Nerlove M., Grether D. M. and Carvalho (1979) “Analysis of Economic Time Series: A Synthesis.” Academic Press, inc. New York.
Randolph, N. (1995) “Probability, Stochastic Processes, and Queuing Theory: The Mathematics of computer performance Modeling.” Springer-Verlag New York, Inc.
Ridberg, T. and Shephard, N. (1999) “BIN models for trade-by-trade data. Modelling the number of trades in a fixed interval.” Nuffield College, Oxford, working paper series 1999-w14.
Rubinstein R. Y. (1981) “Simulation and the Monte Carlo method.” John Wiley & Sons, Inc., USA.
Sheldon M. Ross (2000) “Introduction to Probability Models.” Seventh Edition, Academic Press, San Diego, USA. Sigles ACD: Autoregressive Conditional Durations ARCH: Autoregressive Conditional Heteroscedasticity ARMA: Autoregressive Moving Average DGP: Data Generating Process GARCH: General ARCH MLE: Maximum Likelihood Estimation
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