Inference for adaptive time series models: Stochastic volatility and conditionally Gaussian state space form

Econometric Reviews

Volumn 25, Issue 2-3, 2006, Pages 219-244

  Bos, Charles S ^a   Shephard, Neil ^b  

^a VU UNIVERSITY AMSTERDAM   (Netherlands)

^b NUFFIELD COLLEGE   (United Kingdom)

Author keywords

Markov chain Monte Carlo; Particle filter; State space form; Stochastic volatility

Indexed keywords

EID: 33747791791     PISSN: 07474938     EISSN: 15324168     Source Type: Journal    
DOI: 10.1080/07474930600713275     Document Type: Article

References (49)

1. Aguilar, O., West, M. (2000). Bayesian dynamic factor models and variance matrix discounting for portfolio allocation. Journal of Business and Economic Statistics 18(3):338-357.
2. Aitkin, M. (1991). Posterior Bayes factors. Journal of the Royal Statistical Society, Series B 53(1):111-142.
3. Ameen, P. J. M., Harrison, P. J. (1984). Discounted weighted estimation. Journal of Forecasting 3(3):285-296.
4. Beine, M. (2004). Conditional covariances and direct central bank interventions in the foreign exchange markets. Journal of Banking and Finance 28(6):1385-1411.
5. Beine, M., Laurent, S., Palm, F. C. (2004). Central Bank forex interventions assessed using realized moments. Core Discussion Paper 2004/1, UCL, Louvain-la-Neuve.
6. Bos, C. S. (2002). A comparison of marginal likelihood computation methods. In: Härdle, W., Ronz, B. eds. COMPSTAT 2002 - Proceedings of Computational Statistics. Germany: Physica-Verlag, pp. 111-117.
7. Bos, C. S., Mahieu, R. J., Van Dijk, H. K. (2000). Daily exchange rate behaviour and hedging of currency risk. Journal of Applied Econometrics 15(6):671-696.
8. Carlin, B. P., Polson, N. G., Stoffer, D. (1992). A Monte Carlo approach to nonnormal and nonlinear state-space modelling. Journal of the American Statistical Association 87(418):493-500.
9. Carter, C. K., Kohn, R. (1994). On Gibbs sampling for state space models. Biometrika 81(3):541-553.
10. Chen, R., Liu, J. S. (2000). Mixture Kalman filters. Journal of the Royal Statistical Society, Series B 62(3):493-508.
11. Chib, S., Nardari, F., Shephard, N. (2006). Analysis of high dimensional multivariate stochastic volatility models. Journal of Econometrics. Forthcoming.
12. de Jong, P., Shephard, N. (1995). The simulation smoother for time series models. Biometrika 82(2):339-350.
13. Dominguez, K. M. (1998). Central bank intervention and exchange rate volatility. Journal of International Money and Finance 17(1):161-190.
14. Doornik, J. A. (2001). Ox: Object Oriented Matrix Programming, 3-0. London: Timberlake Consultants Press.
15. Doucet, A., de Freitas, N., Gordon, N. (2001). Sequential Monte Carlo Methods in Practice. New York: Springer-Verlag.
16. Durbin, J., Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press.
17. Durbin, J., Koopman, S. J. (2002). A simple and efficient simulation smoother for state space time series analysis. Biometrika 89(3):603-616.
18. Evans, M. D. D., Lyons, R. K. (2003). How is macro news transmitted to exchange rates? Working paper 9433, NBER.
19. Frühwirth-Schnatter, S. (1994). Data augmentation and dynamic linear models. Journal of Time Series Analysis 15(2):183-202.
20. Frühwirth-Schnatter, S. (2004). Efficient Bayesian parameter estimation for state space models based on reparameterizations. In: Harvey, A. C., Koopman, S. J., Shephard, N. eds. State Space and Unobserved Component Models: Theory and Applications. Cambridge University Press, pp. 123-151.
21. Geweke, J. (1989). Bayesian inference in econometric models using Monte Carlo integration. Econometrica 57(6):1317-1339.
22. Geweke, J. (1994). Comment on Bayesian analysis of stochastic volatility models. Journal of Business and Economic Statistics 12(4):397-399.
23. Ghysels, E., Harvey, A. C., Renault, É. (1996). Stochastic volatility. In: Handbook of Statistics, Statistical Methods in Finance. Vol. 14. Amsterdam: North-Holland, pp. 119-191.
24. Gilks, W. R., Best, N. G., Tan, K. K. C. (1995). Adaptive rejection Metropolis sampling within Gibbs sampling. Applied Statistics 44(4):455-472.
25. Gordon, N. J., Salmond, D. J., Smith, A. F. M. (1993). A novel approach to nonlinear and non-Gaussian Bayesian state estimation. IEEE-Proceedings F 140:107-113.
26. Harvey, A. C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge: Cambridge University Press.
27. Harvey, A. C., Ruiz, E., Sentana, E. (1992). Unobserved component time series models with ARCH disturbances. Journal of Econometrics 52(1-2):129-158.
28. Harvey, A. C., Ruiz, E., Shephard, N. (1994). Multivariate stochastic variance models. Review of Economic Studies 61(2):247-264.
29. Jacquier, E., Polson, N. G., Rossi, P. E. (1994). Bayesian analysis of stochastic volatility models (with discussion). Journal of Business and Economic Statistics 12(4):371-417.
30. Kass, R. E., Raftery, A. E. (1995). Bayes factors. Journal of the American Statistical Association 90(430):773-795.
31. Kim, C.-J., Nelson, C. R. (1999). State-Space Models with Regime Switching. Classical and Gibbs-Sampling Approaches with Applications. Cambridge, MA: MIT Press.
32. Kim, S., Shephard, N., Chib, S. (1998). Stochastic volatility: likelihood inference and comparison with ARCH models. Review of Economic Studies 65(3):361-393.
33. Kitagawa, G., Gersch, W. (1996). Smoothness Priors Analysis of Time Series. Lecture notes in Statistics. New York: Springer Verlag.
34. Koopman, S. J., Bos, C. S. (2004). State space models with a common stochastic variance. Journal of Business and Economic Statistics 22(3):346-357.
35. Koopman, S. J., Shephard, N., Doornik, J. A. (1999). Statistical algorithms for models in state space using SsfPack 2.2. Econometrics Journal 2(1):107-160.
36. Lyons, R. K. (2001). The Microstructure Approach to Exchange Rates. Cambridge, MA: MIT Press.
37. Mahieu, R. J., Schotman, P. C. (1994). Neglected common factors in exchange rate volatility. Journal of Empirical Finance 1(3-4):279-311.
38. Muth, J. F. (1961). Rational expectations and the theory of price movements. Econometrica 29(3):315-335.
39. Pitt, M. K., Shephard, N. (1999a). Analytic convergence rates and parameterisation issues for the Gibbs sampler applied to state space models. Journal of Time Series Analysis 21(1):63-85.
40. Pitt, M. K., Shephard, N. (1999b). Filtering via simulation: auxiliary particle filter. Journal of the American Statistical Association 94(446):590-599.
41. Pitt, M. K., Shephard, N. (1999c). Time varying covariances: a factor stochastic volatility approach (with discussion). In: Bernardo, J. M., Berger, J. O., Dawid, A. P., Smith, A. F. M. eds. Bayesian Statistics. Vol. 6. Oxford: Oxford University Press. pp. 547-570.
42. Shephard, N. (1994a). Local scale model: state space alternative to integrated GARCH processes. Journal of Econometrics 60(1-2):181-202.
43. Shephard, N. (1994b). Partial non-Gaussian state space. Biometrika 81(1):115-131.
44. Shephard, N. (1996). Statistical aspects of ARCH and stochastic volatility. In: Cox, D. R., Hinkley, D. V., Barndorff-Nielsen, O. E. eds. Time Series Models in Econometrics, Finance and Other Fields. London: Chapman & Hall, pp. 1-67.
45. Shephard, N., Pitt, M. K. (1997). Likelihood analysis of non-Gaussian measurement time series. Biometrika 84(3):653-667.
46. Tanner, M. A. (1996). Tools for Statistical Inference: Methods for Exploration of Posterior Distributions and Likelihood Functions. 3rd ed. New York: Springer-Verlag.
47. Uhlig, H. (1997). Bayesian vector autoregressions with stochastic volatility. Econometrica 65(1):59-73.
48. Wecker, W. E., Ansley, C. F. (1983). The signal extraction approach to nonlinear regression and spline smoothing. Journal of the American Statistical Association 78(381):81-89.
49. West, M., Harrison, J. (1997). Bayesian Forecasting and Dynamic Models. 2nd ed. New York: Springer-Verlag.