Bayesian analysis of the stochastic conditional duration model

Computational Statistics and Data Analysis

Volumn 50, Issue 9, 2006, Pages 2247-2267

  Strickland, Chris M ^a   Forbes, Catherine S ^a   Martin, Gael M ^a  

^a MONASH UNIVERSITY   (Australia)

Author keywords

Kalman filter and simulation smoother; Latent variable model; Markov chain Monte Carlo; Non Gaussian state space model; Transaction data

Indexed keywords

COMPUTER SIMULATION; KALMAN FILTERING; MARKOV PROCESSES; MATHEMATICAL MODELS; MONTE CARLO METHODS; REGRESSION ANALYSIS; VECTORS;

KALMAN FILTER AND SIMULATION SMOOTHER; LATENT VARIABLE MODEL; MARKOV CHAIN MONTE CARLO; NON-GAUSSIAN STATE SPACE MODEL; TRANSACTION DATA;

STOCHASTIC CONTROL SYSTEMS;

EID: 33644658912     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2005.07.005     Document Type: Article

References (34)

1. D.W.K. Andrews Heteroscedasticity and autocorrelation consistent covariance matrix estimation Econometrica 59 1991 817 858
2. L. Bauwens, and D. Veredas The stochastic conditional duration model: a latent variable model for the analysis of financial durations J. Econometrics 199 2004 381 412
3. L. Bauwens, P. Giot, J. Grammig, and D. Veredas A comparison of financial duration models via density forecasts Internat. J. Forecast. 20 2004 589 609
4. C.K. Carter, and R. Kohn On Gibbs sampling for state space models Biometrica 81 1994 541 553
5. S. Chib, and E. Greenberg Markov chain Monte Carlo simulation methods in econometrics Econometric Theory 12 1996 407 431
6. J. Daniellson Stochastic volatility in asset prices estimation with simulated maximum likelihood J. Econometrics 64 1994 375 400
7. P. De Jong, and N. Shephard The simulation smoother for time series models Biometrica 82 1995 339 350
8. J. Durbin, and S.J. Koopman Time series analysis of non-Gaussian observations based on state space models from both classical and Bayesian perspectives J. Roy. Statist. Soc. Ser. B 62 2000 3 56
9. J. Durbin, and S.J. Koopman Time Series Analysis by State Space Methods 2001 Oxford University Press Oxford
10. J. Durbin, and S.J. Koopman A simple and efficient simulation smoother for time series models Biometrika 81 2002 341 353
11. D. Easley, and M. O'Hara Price, trade size, and information in securities markets J. Financial Econom. 19 1987 69 90
12. D. Easley, and M. O'Hara Time and the process of security price adjustment J. Finance 47 1992 577 605
13. O. Elerian, S. Chib, and N. Shephard Likelihood inference for discretely observed nonlinear diffusions Econometrica 69 2001 959 993
14. R.F. Engle, and J.R. Russell Autoregressive conditional duration: a new approach for irregularly spaced transaction data Econometrica 66 1998 987 1007
15. D. Feng, J.J. Jiang, and P. Song Stochastic conditional duration models with "Leverage Effect" for financial transaction data J. Financial Econometrics 2 2004 390 421
16. M. Fridman, and L. Harris A maximum likelihood approach for non-Gaussian stochastic volatility models J. Business Econom. Statist. 114 1998 429 434
17. S. Frühwirth-Schnatter Data augmentation and dynamic linear models J. Time Ser. Anal. 15 1994 183 202
18. E. Ghysels, C. Gourieroux, and J. Jasiak Stochastic volatility duration models J. Econometrics 119 2004 413 433
19. E. Jacquier, N.G. Polson, and P.E. Rossi Bayesian analysis of stochastic volatility models J. Business Econom. Statist. 12 1994 69 87
20. E. Jacquier, N.G. Polson, and P.E. Rossi Bayesian analysis of stochastic volatility models with fat-tails and correlated errors J. Econometrics 122 2004 185 212
21. Johnson, N.L., Kotz, S., Balakrishnan, N., 1994. Distributions in Statistics: Continuous Univariate Distributions, vol. 1, second ed. Wiley, New York.
22. R.E. Kass Bayes factors in practice Statistician 2 1993 551 560
23. S. Kim, N. Shephard, and S. Chib Stochastic volatility: likelihood inference and comparison with ARCH models Rev. Econom. Stud. 65 1998 361 393
24. R. Liesenfeld, and J.-F. Richard Univariate and multivariate stochastic volatility models: estimation and diagnostics J. Empirical Finance 10 2003 505 531
25. J. Liu Monte Carlo Strategies in Scientific Computing 2001 Springer New York
26. McCabe, B.P.M., Martin, G.M., Freeland, K.R., 2004. Testing for dependence in non-Gaussian time series data. Department of Econometrics and Business Statistics Working Paper No. 13/04, Monash University.
27. R. Meyer, and J. Yu BUGS for a Bayesian analysis of stochastic volatility models Econometrics J. 3 2000 198 215
28. M. Pitt Discussion of Durbin and Koopman (2000) J. Roy. Statist. Soc. Ser. B 62 2000 30 32
29. T.H. Rydberg, and N. Shephard Dynamics of trade-by-trade price movements: decomposition and models J. Financial Econometrics 1 2003 2 25
30. G. Sandmann, and S.J. Koopman Estimation of stochastic volatility models via Monte Carlo maximum likelihood J. Econometrics 87 1998 271 301
31. N. Shephard Partial non-Gaussian state space Biometrica 81 1994 115 131
32. N. Shephard, and M. Pitt Likelihood analysis of non-Gaussian measurement time series Biometrica 84 1997 653 667
33. I. Verdinelli, and L. Wasserman Computing Bayes factors using a generalization of the Savage-Dickey density ratio J. Amer. Statist. Assoc. 90 1995 614 618
34. Zellner, A., 1996. An Introduction to Bayesian Inference in Econometrics. Wiley, New York.