Birnbaum-Saunders and lognormal kernel estimators for modelling durations in high frequency financial data

Annals of Economics and Finance

Volumn 4, Issue 1, 2003, Pages 103-124

  Jin, Xiaodong ^a   Kawczak, Janusz ^a  

^a UNIVERSITY OF NORTH CAROLINA AT CHARLOTTE   (United States)

Author keywords

ACD model; Birnbaum Saunders kernel; Durations; High frequency; Lognormal kernel

Indexed keywords

EID: 34147196807     PISSN: 15297373     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (16)

1. Birnbaum, Z. W. and S. C. Saunders, 1969, A new family of life distributions. Journal of Applied Probability 6, 319-327.
2. Brown, B. M. and S.-X. Chen, 1999, Beta-Bernstein smoothing for regression curves with compact supports. Scand. J. Statist. 26, 47-59.
3. Chen, S.-X., 2000, Probability density function estimation using gamma kernels. Ann. Inst. Statist. Math. 52, No.3, 471-480.
4. Chiu, S.-T., 1996, A comparative review of bandwidth selection for kernel density estimation. Statistica Sinica 6, 129-145.
5. Cowling, A. and P. Hall, 1996, On pseudodata methods for removing boundary effects in kernel density estimation. J. Roy. Statist. Soc. Ser. B 58, 551-563.
6. Engle, R. F. and J. R. Russell, 1998, Autoregressive conditional duration: A new model for irregularly-spaced transaction data. Econometrica 66, 1127-1162.
7. Eubank, R. L. and P. Speckman, 1990, Curve fitting by polynomial-trigonometric regression. Biometrika 77, 1-9.
8. Fernandes, M. and J. Grammig, 2000, Non-parametric specification tests for conditional duration models. C.O.R.E Discussion Paper 2000-4, European University Institute.
9. Fernandes, M. and J. Grammig, 2001, A family of autoregressive conditional duration models. ECO Working Paper 2001-36, European University Institute.
10. Giot, P., 2000, Time transformations, intraday data, and volatility models. Journal of Computational Finance 4, 31-62.
11. Hosking, J. R. M., 1990, L-moments: Analysis and estimation of distributions using linear combinations of order statistics. J. R. Statist. Soc. B 52, No.1, 105-124.
12. Katkovnik, V. and I. Shmulevich, 2000, Nonparametric density estimation with adaptive varying window size. Conference on Image and Signal Proessing for Remote Sensing VI, European Symposium on Remote Sensing Barcelona, Spain, Septermber 25-29, 2000.
13. Scaillet, O., 2001, Density estimation using inverse and reciprocal inverse gaussian kernels. IRES Discussion Paper, No. 2001-17, Univ. Catholique de Louvain.
14. Schuster, E. F., 1985, Incorporating support constraints into nonparametric estimation of densites. Commun. Statist. Theory Meth. 14, 1123-1136.
15. Silverman, B. W., 1986, Density Estimation for Statistics and Data Analysis. Chapman and Hall, London.
16. Turlach, B. A., 1993, Bandwidth selection in kernel density estimation: A review. C.O.R.E and Institut de Statistique, Univ. Catholique de Louvain.