References
- Andersen, T., Sorensen, B. (1996). GMM estimation of a stochastic volatility model: A Monte Carlo study. Journal of Business and Economic Statistics 14:328–352.
- Barndorf-Nielsen, O.E. (1997). Normal inverse Gaussian distributions and stochastic volatility modelling. Scandinavian Journal of Statistics 24:1–13.
- Broto, C., Ruiz, E. (2004). Estimation methods for stochastic volatility models: A survey. Journal of Economic Surveys 18:613–649.
- Harvey, A.C., Ruiz, E., Shephard, N. (1994). Multivariate stochastic variance models. Reviews of Economic Studies 61:247–264.
- Jacquier, E., Polson, N.G., Rossi, P.E. (1994). Bayesian analysis of stochastic volatility models (with discussion). Journal of Business and Economic Statistics 12:371–418.
- Martino, S., Aas, K., Lindqvist, O., Neef, L., Rue, H. (2010). Estimating stochastic volatility models using integrated nested laplace approximations. The European Journal of Finance 17(7):487–503.
- R Development Core Team. (2006). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. ISBN 3-900051-07-0.
- Rue, H., Held, L. (2005). Gaussian Markov Random Fields: Theory and Applications Vol. 104 of Monographs on Statistics and Applied Probability. London:Chapman & Hall.
- Rue, H., Martino, S., Chopin, N. (2009). Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations (with discussion. Journal of the Royal Statistical Society: Series B 71(2):319–392.
- Shephard, N., Andersen, T.G. (2009). Stochastic volatility: Origins and overview. In: Handbook of Financial Time Series. ( T. Mikosch, J.-P. Kreiβ, R. A. Davis, T. G. Andersen, Eds.) Berlin-Heidelberg: Springer, pp. 233–254.
- Taylor, S. (1986). Modelling Financial Time Series. Chichester, UK: Wiley.