Semiparametric Duration Models
AbstractIn this article we consider semiparametric duration models and efficient estimation of the parameters in a non-iid environment. In contrast to classical time series models where innovations are assumed to be iid we show that in, for example, the often-used autoregressive conditional duration (ACD) model, the assumption of independent innovations is too restrictive to describe financial durations accurately. Therefore, we consider semiparametric extensions of the standard specification that allow for arbitrary kinds of dependencies between the innovations. The exact nonparametric specification of these dependencies determines the flexibility of the semiparametric model. We calculate semiparametric efficiency bounds for the ACD parameters, discuss the construction of efficient estimators, and study the efficiency loss of the exponential pseudolikelihood procedure. This efficiency loss proves to be sizeable in applications. For durations observed on the Paris Bourse for the Alcatel stock in July and August 1996, the proposed semiparametric procedures clearly outperform pseudolikelihood procedures. We analyze these efficiency gains using a simulation study confirming that, at least at the Paris Bourse, dependencies among rescaled durations can be exploited.
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Bibliographic InfoPaper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 2001-11.
Date of creation: 2001
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Web page: http://center.uvt.nl
adaptiveness; durations; one-step improvement; semiparametric efficiency;
Other versions of this item:
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
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- Newey, Whitney K, 1990. "Semiparametric Efficiency Bounds," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 5(2), pages 99-135, April-Jun.
- Gloria Gonzalez-Rivera, 1997.
"A note on adaptation in garch models,"
Taylor & Francis Journals, vol. 16(1), pages 55-68.
- Gonzalez-Rivera, G., 1995. "A Note on Adaptation in Garch Models," The A. Gary Anderson Graduate School of Management 95-1, The A. Gary Anderson Graduate School of Management. University of California Riverside.
- Ghysels, Eric & Gourieroux, Christian & Jasiak, Joann, 2004.
"Stochastic volatility duration models,"
Journal of Econometrics,
Elsevier, vol. 119(2), pages 413-433, April.
- repec:cup:etheor:v:9:y:1993:i:4:p:539-69 is not listed on IDEAS
- repec:cup:cbooks:9780521496032 is not listed on IDEAS
- Robert F. Engle & Jeffrey R. Russell, 1998. "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data," Econometrica, Econometric Society, vol. 66(5), pages 1127-1162, September.
- Oliver Linton, 1993.
"Adaptive Estimation in ARCH Models,"
Cowles Foundation Discussion Papers
1054, Cowles Foundation for Research in Economics, Yale University.
- Robert F. Engle, 2000.
"The Econometrics of Ultra-High Frequency Data,"
Econometric Society, vol. 68(1), pages 1-22, January.
- Drost, Feike C. & Klaassen, Chris A. J., 1997.
"Efficient estimation in semiparametric GARCH models,"
Journal of Econometrics,
Elsevier, vol. 81(1), pages 193-221, November.
- Drost, F.C. & Klaassen, C.A.J., 1996. "Efficient Estimation in Semiparametric GARCH Models," Discussion Paper 1996-38, Tilburg University, Center for Economic Research.
- Ghysels, E. & Harvey, A. & Renault, E., 1996.
Cahiers de recherche
9613, Universite de Montreal, Departement de sciences economiques.
- Eric Ghysels & Andrew Harvey & Éric Renault, 1995. "Stochastic Volatility," CIRANO Working Papers 95s-49, CIRANO.
- Ghysels, E. & Harvey, A. & Renault, E., 1995. "Stochastic Volatility," Papers 95.400, Toulouse - GREMAQ.
- Ghysels, E. & Harvey, A. & Renault, E., 1996. "Stochastic Volatility," Cahiers de recherche 9613, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
- GHYSELS, Eric & HARVEY, Andrew & RENAULT, Eric, 1995. "Stochastic Volatility," CORE Discussion Papers 1995069, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Christian Gourieroux & Joanna Jasiak, 1998. "Nonlinear Autocorrelograms : An Application to Intra-Trade Durations," Working Papers 98-41, Centre de Recherche en Economie et Statistique.
- Steigerwald, Douglas G., 1992. "Adaptive estimation in time series regression models," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 251-275.
- Drost, F.C. & Klaassen, C.A.J., 1997. "Efficient estimation in semiparametric GARCH models," Open Access publications from Tilburg University urn:nbn:nl:ui:12-74146, Tilburg University.
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