A nonlinear autoregressive conditional duration model with applications to financial transaction data

Journal of Econometrics

Volumn 104, Issue 1, 2001, Pages 179-207

  Zhang, Michael Yuanjie ^a   Russell, Jeffrey R ^b   Tsay, Ruey S ^b  

^a AXA Rosenberg Group   (United States)

^b UNIVERSITY OF CHICAGO   (United States)

Author keywords

Autoregressive conditional duration; Duration models; Market microstructure; Nonlinear time series; Structural break

Indexed keywords

EID: 0011247415     PISSN: 03044076     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0304-4076(01)00063-X     Document Type: Article

References (37)

1. Andersen, T.G., Bollerslev, T., 1998. Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. International Economic Review 39, 885-905.
2. Andrews, D.W.K., 1993. Tests for parametric instability and structural change with unknown change point. Econometrica 61 (4), 821-856.
3. Andrews, D.W.K., Ploberger, W., 1994. Optimal tests when a nuisance parameters is present only under the alternative. Econometrica 62 (6), 1383-1414.
4. Bai, X., Russell, J.R., Tiao, G.C., 1999. Beyond Merton's Utopia; effects of non-normality and dependence on the precision of variance estimates using high-frequency financial data. Working Paper, Graduate School of Business, University of Chicago.
5. Berndt, E., Hall, B., Hall, R., Hausman, J., 1974. Estimation and inference in nonlinear structural models. Annals of Economic and Social Measurement 3, 653-665.
6. Bollerslev, T., 1986. Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics 31, 307-327.
7. Bougerol, P., Picard, N., 1992. Stationarity of GARCH processes and of some nonnegative time series. Journal of Econometrics 52, 115-127.
8. Brockwell, P.J., Liu, J., Tweedie, R.L., 1992. On the existence of stationary threshold autoregressive moving-average processes. Journal of Time Series Analysis 13 (2), 95-107.
9. Chan, K.S., Petruccelli, J.D., Tong, H., Woolford, S.W., 1985. A multiple-threshold AR(1) model. Journal of Applied Probability 22, 267-279.
10. Chen, R., Tsay, R.S., 1991. On the ergodocity of TAR(1) processes. Annals of Applied Probability 1 (4), 613-634.
11. Easley, D., O'Hara, M., 1987. Price, trade size, and information in securities markets. Journal of Financial Economics 19 (1), 69-90.
12. Easley, D., O'Hara, M., 1992a. Adverse selection and large trade volume. Journal of Financial and Quantitative Analysis 27 (2), 185-208.
13. Easley, D., O'Hara, M., 1992b. Time and the process of security price adjustment. Journal of Finance 47, 577-606.
14. Engle, R.F., 1982. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50 (4), 987-1006.
15. Engle, R.F., 2000. The econometrics of ultra-high frequency data. Econometrica 68 (1), 1-22.
16. Engle, R.F., Russell, J.R., 1995. Autoregressive conditional duration: A new model for irregular spaced data. Unpublished manuscript, University of California, San Diego.
17. Engle, R.F., Russell, J.R., 1998. Autoregressive conditional duration: A new model for irregular spaced transaction data. Econometrica 66 (5), 1127-1162.
18. Friedman, J.H., 1984. A variable span smoother. Technical Report No. 5, Laboratory for Computational Statistics, Department of Statistics, Stanford University.
19. Glosten, L.R., Jagannathan, R., Runkle, D.E., 1993. On the relation between the expected value and volatility of the nominal excess return on stocks. Journal of Finance 48, 1779-1801.
20. Glosten, L.R., Milgrom, P.R., 1985. Bid, ask and transaction prices in a specialist market with heterogeneously informed traders. Journal of Financial Economics 14, 71-100.
21. Goodhart, C.A.E., O'Hara, M., 1997. High frequency data in financial markets: Issues and applications. Journal of Empirical Finance 4, 73-114.
22. Grammig, J., Maurer, K.-O., 2000. Non-monotonic hazard functions and the autoregressive conditional duration model. Econometric Journal 3(1), 16-38.
23. Koop, G., Potter, S., 1997. Nonlinearity, structural breaks or outliers in economic time series. Working Paper, University of Edinburgh.
24. Lancaster, T., 1990. The Econometric Analysis of Transition Data. Cambridge University Press, Cambridge.
25. Lee, C.M.C., Mucklow, B., Ready, M.J., 1993. Spreads, depths and the impact of earnings information: an intraday analysis. Review of Financial Studies 6 (2), 345-374.
26. Lunde, A., 1999. A generalized Gamma autoregressive conditional duration model. Working Paper, Department of Economics, Politics and Public Administration, Aalborg University, Denmark.
27. Meyn, S., Tweedie, R.L., 1993. Markov Chains and Stochastic Stability. Springer, London.
28. Moeanaddin, R., Tong, H., 1990. Numerical evaluation of distributions in non-linear autoregression. Journal of Time Series Analysis 11 (1), 33-48.
29. Olsen and Associates, 1995. First International Conference on High Frequency Data in Finance (HFDF-I). Olsen and Associates, Zurich, Switzerland.
30. Olsen and Associates, 1998. Second International Conference on High Frequency Data in Finance (HFDF-II). Olsen and Associates, Zurich, Switzerland.
31. Petruccelli, J., Woolford, S.W., 1984. A threshold AR(1) model. Journal of Applied Probability 21, 270-286.
32. Rabemananjara, R., Zakoian, J.M., 1993. Threshod ARCH models and asymmetries in volatility. Journal of Applied Econometrics 8, 31-49.
33. Tong, H., 1990. Non-linear Time Series: A Dynamical System Approach. Oxford University Press, Oxford, UK.
34. Tsay, R.S., 1989. Testing and modeling threshold autoregressive processes. Journal of American Statistical Association 84 (405), 231-240.
35. Tsay, R.S., 1992. Model checking via parametric bootstraps in time series analysis. Applied Statistics 41 (1), 1-15.
36. Tsay, R.S., 1998. Testing and modeling multivariate threshold models. Journal of American Statistical Association 93 (442), 478-493.
37. Zakoian, J.-M., 1994. Threshold heteroskedastic models. Journal of Economic Dynamics and Control 18, 931-955.