Duration time-series models with proportional hazard

Journal of Time Series Analysis

Volumn 29, Issue 1, 2008, Pages 74-124

  Gagliardini, P ^a   Gourieroux, C ^b  

^a UNIVERSITY OF LUGANO   (Switzerland)

^b UNIVERSITY OF TORONTO   (Canada)

Author keywords

ACD model; Copula; Duration; Nonparametric efficiency; Nonparametric estimation; Proportional hazard

Indexed keywords

EID: 36849089165     PISSN: 01439782     EISSN: 14679892     Source Type: Journal    
DOI: 10.1111/j.1467-9892.2007.00546.x     Document Type: Article

References (48)

1. Abramowitz, M. Stegun, I. (1970) Handbook of Mathematical Functions. New-York : Dover.
2. Ané, T. Geman, H. (2000) Order Flow, Transaction Clock, and Normality of Asset Returns. Journal of Finance 55, 2259 84.
3. Barlow, R. E. Proschan, F. (1975) Statistical Theory of Reliability and Life Testing Probability Models. New York : Holt, Reinhart and Winston.
4. Bauwens, L. Giot, P. (2000) The logarithmic ACD model: An application to the bid-ask quote process of three NYSE stocks. Annales d'Economie et Statistique 60, 117 49.
5. Bickel, P. J., Klaassen, C. A. J., Ritov, Y. Wellner, J. A. (1993) Efficient and Adaptive Estimation in Semiparametric Models. Baltimore : John Hopkins University Press.
6. Bosq, D. (1998) Nonparametric Statistics for Stochastic Processes. Estimation and Prediction. New York : Springer.
7. Bouyé, E., Gaussel, N. Salmon, M. (2002) Investigating dynamic dependence using copulae. Working Paper, Financial Econometrics Research Center.
8. Capéraà, P. Genest, C. (1990) Concepts de dépendance et ordres stochastiques pour des lois bidimensionnelles. The Canadian Journal of Statistics 18, 315 26.
9. Carrasco, M. Chen, X. (2002) Mixing and moment properties of various GARCH and stochastic volatility models. Econometric Theory 18, 17 39.
10. Clark, P. (1973) A subordinated stochastic process model with finite variance for speculative prices. Econometrica 41, 135 56.
11. Cox, D. R. (1955) Some statistical models connected with series of events (with discussion). Journal of the Royal Statistical Society Series B 17, 129 64.
12. Cox, D. R. (1972) The statistical analysis of dependences in point processes. In Symposium on Point Processes (ed. P. A. W. Lewis). New York : John Wiley, 55 66.
13. Darolles, S., Gourieroux, C. Jasiak, J. (2006) Structural Laplace transform and compound autoregressive models. Journal of Time Series Analysis 27, 477 504.
14. Darsow, W., Nguyen, B. Olsen, E. (1992) Copulas and Markov processes. Illinois Journal of Mathematics 36, 600 42.
15. Davydov, Y. (1973) Mixing Conditions for Markov Chains. Stochastic Processes and their Applications 18, 312 28.
16. Doukhan, P. (1994) Mixing. Properties and Examples. New-York : Springer.
17. Engle, R. (2000) The Econometrics of ultra-high-frequency data. Econometrica 68, 1 22.
18. Engle, R. Russell, J. R. (1998) Autoregressive conditional duration: A new model for irregularly spaced transaction data. Econometrica 66, 1127 62.
19. Fang, Z., Hu, T. Joe, H. (1994) On the decrease in dependence with lag for stationary Markov chains. Probability in the Engineering and Informational Sciences 8, 385 401.
20. Feigin, P. D. Tweedie, R. L. (1985) Random coefficients autoregressive processes: A Markov chain analysis of stationarity and finiteness of moments. Journal of Time Series Analysis 6, 1 14.
21. Feller, W. (1971) An Introduction to Probability Theory an Its Applications (2nd edn). New York : Wiley.
22. Gagliardini, P. Gourieroux, C. (2007) An efficient nonparametric estimator for models with nonlinear dependence. Journal of Econometrics 137, 189 229.
23. Genest, C. Werker, B. J. M. (2002) Conditions for the asymptotic semiparametric efficiency of an omnibus estimator of dependence parameter in copula models. In Proceedings of the Conference on Distributions with Given Marginals and Statistical Modelling (eds C. M. Cuadras J. A. Rodríguez Lallena). Dordrecht : Kluwer.
24. Ghysels, E., Gourieroux, C. Jasiak, J. (1998) Market time and asset price movements. theory and estimation. In Statistics in Finance (eds D. Hand S. Jacka). London : Arnold.
25. Ghysels, E., Gourieroux, C. Jasiak, J. (2004) Stochastic volatility duration models. Journal of Econometrics 119, 413 33.
26. Ghysels, E. Jasiak, J. (1994) Stochastic volatility and time deformation: An application to trading volume and leverage effects. Working Paper, C.R.D.E. and Université de Montreal.
27. Giot, P. (2001) Time deformations, intra-day data and volatility models. Journal of Computational Finance 4, 31 62.
28. Gourieroux, C. Jasiak, J. (2001a) Financial Econometrics. Princeton : Princeton University Press.
29. Gourieroux, C. Jasiak, J. (2001b) Nonlinear autocorrelograms: An application to inter-trade durations. Journal of Time Series Analysis 23, 127 54.
30. Gourieroux, C. Robert, C. Y. (2006) Stochastic unit root models. Econometric Theory 22, 1052 90.
31. Hautsch, N. (1999) Analyzing the time between trades with a Gamma Compounded Hazard Model. An application to LIFFE bund future transactions. Working Paper 99.03, Center of Finance and Econometrics, University of Konstanz.
32. Jasiak, J. (1998) Persistence in intertrade durations. Finance 19, 166 95.
33. Joe, H. (1993) Parametric family of multivariate distributions with given margins. Journal of Multivariate Analysis 46, 262 82.
34. Joe, H. (1997) Multivariate Models and Dependence Concepts. London : Chapman & Hall.
35. Jondeau, E. Rockinger, M. (2006) The Copula-GARCH model of conditional dependencies: An international stock market application. Journal of International Money and Finance 25, 827 53.
36. Jones, M. C. (1993) Simple boundary correction for kernel density estimation. Statistics and Computing 3, 135 46.
37. Kimeldorf, G. Sampson, A. R. (1987) Positive dependence orderings. Annals of the Institute of Statistical Mathematics 39, 113 28.
38. Kimeldorf, G. Sampson, A. R. (1989) A framework for positive dependence. Annals of the Institute of Statistical Mathematics 341, 31 45.
39. Lehmann, E. L. (1966) Some Concepts of Dependence. Annals of Mathematical Statistics 37, 1137 53.
40. Meyn, S. P. Tweedie, R. L. (1993) Markov Chains and Stochastic Stability. New York : Springer.
41. Nelsen, R. B. (1999) An Introduction to Copulas. New York : Springer.
42. Rice, J. (1984) Boundary modification for kernel regression. Communications in Statistics. Theory and Methods 13, 893 900.
43. Severini, T. A. Tripathi, G. (2001) A simplified approach to computing efficiency bounds in semiparametric models. Journal of Econometrics 102, 23 66.
44. Shaked, M. (1977) A family of concepts of dependence for bivariate distributions. Journal of the American Statistical Association 72, 642 50.
45. Stock, J. (1988) Estimating continuous time processes subject to time deformation. Journal of the American Statistical Association 83, 77 85.
46. Tong, H. (1990) Non-linear Time Series: A Dynamical System Approach. Oxford : Oxford University Press.
47. Yanagimoto, T. Okamoto, M. (1969) Partial orderings of permutations and monotonicity of a rank correlation statistic. Annals of the Institute of Statistical Mathematics 21, 489 506.
48. Zhang, M. Y., Russell, J. R. Tsay, R. S. (2001) A nonlinear autoregressive conditional duration model with applications to financial transaction data. Journal of Econometrics 104, 179 207.