Monte Carlo likelihood estimation for three multivariate stochastic volatility models

Econometric Reviews

Volumn 25, Issue 2-3, 2006, Pages 385-408

  Jungbacker, Borus ^a   Koopman, Siem Jan ^a  

^a VU UNIVERSITY AMSTERDAM   (Netherlands)

Author keywords

Importance sampling; Monte Carlo likelihood; Stochastic volatility

Indexed keywords

EID: 33747809011     PISSN: 07474938     EISSN: 15324168     Source Type: Journal    
DOI: 10.1080/07474930600712848     Document Type: Article

References (38)

1. Aguilar, O., West, M. (2000). Bayesian dynamic factor models and portfolio allocation. J. Business and Economic Statist. 18:338-357.
2. Asai, M., McAleer, M., Yu, J. (2005). Multivariate stochastic volatility. Technical Report, Tokyo Metropolitan University.
3. Bollerslev, T. (1986). Generalised autoregressive conditional heteroskedasticity. J. Econometrics 51:307-327.
4. Chib, S., Nardari, F., Shephard, N. (2006). Analysis of high dimensional multivariate stochastic volatility models. J. Econometrics, forthcoming.
5. Danielsson, J. (1994). Stochastic volatility in asset prices: estimation with simulated maximum likelihood. J. Econometrics 61:375-400.
6. Danielsson, J. (1998). Multivariate stochastic volatility models: estimation and a comparison with VGARCH models. J. Empirical Finance 5:155-173.
7. Davis, R. A., Rodriguez-Yam, G. (2005). Estimation for state-space models: an approximate likelihood approach. Statistica Sinica 15:381-406.
8. de Jong, P., Shephard, N. (1995). The simulation smoother for time series models. Biometrika 82:339-350.
9. Doornik, J. A. (2001). Object-Oriented Matrix Programming using Ox 3.0. 4th ed. London: Timberlake Consultants. See http://www.doornik.com.
10. Durbin, J., Koopman, S. J. (1997). Monte Carlo maximum likelihood estimation of non-Gaussian state space model. Biometrika 84:669-684.
11. Durbin, J., Koopman, S. J. (2001). Time Series Analysis by State Space Methods. Oxford: Oxford University Press.
12. Durbin, J., Koopman, S. J. (2002). A simple and efficient simulation smoother for state space time series analysis. Biometrika 89:603-616.
13. Durham, A. G., Gallant, A. R. (2001). Numerical techniques for maximum likelihood estimation of continuous-time diffusion processes (with discussion). J. Business and Economic Statist. 20:297-316.
14. Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of the United Kingdom inflation. Econometrica 50:987-1007.
15. Fridman, M., Harris, L. (1998). A maximum likelihood approach for non-gaussian stochastic volatility models. J. Business and Economic Statistics 16:284-291.
16. Geweke, J. (1989). Bayesian inference in econometric models using Monte Carlo integration. Econometrica 57:1317-1339.
17. Hansen, P., Lunde, A. (2005). A forecast comparison of volatility models: does anything beat a GARCH(1,1)? J. Applied Econometrics, forthcoming.
18. Harvey, A. C., Ruiz, E., Shephard, N. (1994). Multivariate stochastic variance models. Rev. Economic Studies 61:247-264.
19. Jungbacker, B., Koopman, S. J. (2005). On importance sampling for state space models. Amsterdam: Mimeo, Vrije Universiteit.
20. Kim, S., Shephard, N., Chib, S. (1998). Stochastic volatility: likelihood inference and comparison with ARCH models. Rev. Economic Studies 65:361-393.
21. Koopman, S. J., Jungbacker, B., Hol, E. (2005). Forecasting the daily variability of the S&P100 stock index using historical, realised and implied volatility measurements. J. Empirical Finance 12:445-475.
22. Koopman, S. J., Bos, C. S. (2004). State space models with a common stochastic variance. J. Business and Economic Statist. 22:346-357.
23. Koopman, S. J., Hol-Uspensky, E. (2002). The stochastic volatility in mean model: empirical evidence from international stock markets. J. Applied Econometrics 17:667-689.
24. Koopman, S. J., Shephard, N. (2004). Estimating the Likelihood of the Stochastic Volatility Model: Testing the Assumptions Behind Importance Sampling. Discussion paper, Amsterdam: Vrije Universiteit.
25. Koopman, S. J., Shephard, N., Doornik, J. A. (1999). Statistical algorithms for models in state space form using SsfPack 2.2. Econometrics Journal 2:113-166. http://www.ssfpack.com/.
26. Lee, K. M., Koopman, S. J. (2004). Estimating stochastic volatility models: a comparison of two importance samplers. Studies in Nonlinear Dynamics and Econometrics 8:Art 5.
27. Liesenfeld, R., Jung, R. (2000). Stochastic volatility models: conditional normality versus heavy-tailed distributions. J. Applied Econometrics 15:137-160.
28. Monahan, J. F. (1993). Testing the behaviour of importance sampling weights. Computer Science and Statistics. In: Proceedings of the 25th Annual Symposium on the Interface, pp. 112-117.
29. Pitt, M. K., Shephard, N. (1999). 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. Oxford: Oxford University Press, pp. 547-570.
30. Quintana, J. M., West, M. (1987). An analysis of international exchange rates using multivariate DLM's. The Statistican 36:275-281.
31. Sandmann, G., Koopman, S. J. (1998). Estimation of stochastic volatility models via Monte Carlo maximum likelihood. J. Econometrics 87:271-301.
32. Shephard, N. (1994). Local scale model: state space alternative to integrated GARCH processes. J. Econometrics 60:181-202.
33. Shephard, N. (1996). Statistical aspects of ARCH and stochastic volatility. In: Cox, D. R., Hinkley, D. V., Barndorff-Nielson, O. E., eds., Time Series Models in Econometrics, Finance and Other Fields. London: Chapman & Hall, pp. 1-67.
34. Shephard, N. (2005). Stochastic Volatility. Oxford: Oxford University Press.
35. Shephard, N., Pitt, M. K. (1997). Likelihood analysis of non-Gaussian measurement time series. Biometrika 84:653-667.
36. Taylor, S. J. (1986). Modelling Financial Time Series. Chichester: John Wiley.
37. Yu, J. (2005). On leverage in a stochastic volatility model. J. Econometrics 44, forthcoming.
38. Yu, J., Meyer, R. (2006). Multivariate stochastic volatility models: Bayesian estimation and model comparison. Econometric Reviews 25(2-3):361-384.