Dempster, AP, Laird, NM, Rubin, DB. Maximum likelihood from incomplete data via the EM algorithm (with discussion). J Roy Stat Soc Ser B 1977, 39:1–38.
Tanner, MA, Wong, WH. The calculation of posterior distributions by data augmentation (with discussion). J Am Stat Assoc 1987, 82:528–550.
Tsay, RS. Analysis of Financial Time Series. 3rd ed. Hoboken, NJ: John Wiley %26 Sons; 2010.
Geman, S, Geman, D. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans Pattern Anal Mach Intell 1984, 6:721–741.
Gelfand, AE, Smith, AFM. Sampling‐based approaches to calculating marginal densities. J Am Stat Assoc 1990, 85:398–409.
Tierney, L. Markov chains for exploring posterior distributions (with discussion). Ann Stat 1994, 22:1701–1762.
Liu, J, Wong, WH, Kong, A. Correlation structure and convergence rate of the Gibbs samplers I: applications to the comparison of estimators and augmentation schemes. Biometrika 1994, 81:27–40.
Carlin, BP, Louis, TA. Bayes and Empirical Bayes Methods for Data Analysis. 2nd ed. London: Chapman and Hall; 2000.
Gelman, A, Carlin, JB, Stern, HS, Rubin, DB. Bayesian Data Analysis. 2nd ed. London: Chapman and Hall/CRC Press; 2003.
Metropolis, N, Ulam, S. The Monte Carlo method. J Am Stat Assoc 1949, 44:335–341.
Metropolis, N, Rosenbluth, AW, Rosenbluth, MN, Teller, AH, Teller, E. Equation of state calculations by fast computing machines. J Chem Phys 1953, 21:1087–1092.
Hasting, WK. Monte Carlo sampling methods using Markov chains and their applications. Biometrika 1970, 57:97–109.
Tanner, MA. Tools for Statistical Inference: Methods for the Exploration of Posterior Distributions and Likelihood Functions. 3rd ed. New York: Springer‐Verlag; 1996.
Jacquier, E, Polson, NG, Rossi, PE. Bayesian analysis of stochastic volatility models (with discussion). J Business Econ Stat 1994, 12:371–417.
Box, GEP, Tiao, GC. Bayesian Inference in Statistical Analysis. Reading, MA: Addison‐Wesley; 1973.
Jacquier, E, Polson, NG, Rossi, PE. Bayesian analysis of stochastic volatility models with fat‐tails and correlated errors. J Econ 2004, 122:185–212.
Kim, S, Shephard, N, Chib, S. Stochastic volatility: likelihood inference and comparison with ARCH models. Rev Econ Stud 1998, 65:361–393.
Carter, CK, Kohn, R. On Gibbs sampling for state space models. Biometrika 1994, 81:541–553.
Frühwirth‐Schnatter, S. Data augmentation and dynamic linear models. J Time Ser Anal 1994, 15: 183–202.
Artigas, JC, Tsay, RS. Effective estimation of stochastic diffusion models with leverage effects and jumps, Working paper, Graduate School of Business, University of Chicago, 2004.
McCulloch, RE, Tsay, RS. Statistical analysis of economic time series via Markov switching models. J Time Ser Anal 1994, 15:523–539.
Zhang, MY, Russell, JR, Tsay, RS. Determinants of bid and ask quotes and implications for the cost of trading. J Emp Finan 2008, 15:656–678.
McCulloch, RE, Tsay, RS. Nonlinearity in high‐frequency financial data and hierarchical models. Stud Nonlinear Dyn Econ 2001, 5:1–17.
Eraker, B. Markov Chain Monte Carlo analysis of diffusion with application to finance. J Business Econ Stat 2001, 19:177–191.
Elerian, O, Chib, S, Shephard, N. Likelihood inference for discretely observed nonlinear diffusions. Econometrica 2001, 69:959–993.
Bauwens, L, Laurent, S, Rombouts, JVK. Multivariate GARCH models: a survey. J Appl Econ 2006, 21:79–109.
Engle, RF, Kelly, B. Dynamic equicorrelation, Working paper, Stern School of Business, New York University, 2009.
Tsay, RS. Dynamic structured correlation models, Working paper, Booth School of Business, University of Chicago, 2011.
Engle, RF, Shephard, N, Sheppard, K. Fitting vast dimensional time‐varying covariance models, Working paper, Oxford University. 2008.
Tse, YK, Tsui, AKC. A multivariate GARCH model with time‐varying correlations. J Business Econ Stat 2002, 20:351–362.
Engle, RF. Dynamic conditional correlation: a simple class of multivariate GARCH models. J Business Econ Stat 2002, 20:339–350.
Tsay, RS. %22Multivariate volatility models%22. In: Ho, HC, Ing, CK, Lai, TL, eds. Time Series and Related Topics in memory of C.Z. Wei. Lecture Notes Monograph Series. Institute of Mathematical Statistics; 2006.
Matteson, D, Tsay, RS. Dynamic orthogonal components for multivariate time series, Working paper, Booth School of Business, University of Chicago, 2010.
McNeil, AJ, Frey, R, Embrechts, P. Quantitative Risk Management: Concepts, Techniques, and Tools. Princeton, NJ: Princeton University Press; 2005.
Sklar, A. Fonctions de répartition án dimensions et leurs marges. Publications de l`Institut de Statistique de l`Universite de Paris 1959, 8:229–231.
Nelson, RB. An Introduction to Copulas. New York: Springer; 1999.
Joe, H. Multivariate Models and Dependence Concepts. Boca Raton, FL: Chapman %26 Hall/CRC Press; 1997.
Demarta, S, McNeil, AJ. The t copula and related copulas, Working paper, Department of Mathematics, Federal Institute of Technology, ETH Zentrum, Zurich, 2004.
Creal, D, Koopman, SJ, Lucas, A. A dynamic multivariate heavy‐tailed model for time‐varying volatilities and correlations, Working paper, Booth School of Business, University of Chicago, 2010.