Bayesian analysis of stochastic volatility models with fat-tails and correlated errors

Journal of Econometrics

Volumn 122, Issue 1, 2004, Pages 185-212

  Jacquier, Eric ^a   Polson, Nicholas G ^b   Rossi, Peter E ^b  

^a HEC MONTRÉAL   (Canada)

^b UNIVERSITY OF CHICAGO   (United States)

Author keywords

Bayes factor; Fat tails; GARCH; Gibbs; Leverage effect; MCMC; Metropolis; Stochastic volatility

Indexed keywords

COMPUTER SIMULATION; ERROR ANALYSIS; MARKOV PROCESSES; MATHEMATICAL MODELS; MONTE CARLO METHODS; SAMPLING;

BAYES FACTORS; LEVERAGE EFFECTS; METROPOLIS; STOCHASTIC VOLATILITY;

STOCHASTIC CONTROL SYSTEMS;

EID: 3042777110     PISSN: 03044076     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jeconom.2003.09.001     Document Type: Article

References (36)

1. Black, F., 1976. Studies of stock market volatility changes. Proceedings of the American Statistical Association, Business and Economic Statistics Section, pp. 177-181.
2. Bollerslev T. A conditionally heteroskedastic time-series model for security prices and rates of return. Review of Economics and Statistics. 69:1987;542-547.
3. Bollerslev T., Chou R., Kroner K. ARCH modelling in finance. a review of the theory and empirical evidence Journal of Econometrics. 52:1992;5-59.
4. Carlin B.P., Polson N.G. Inference for non-conjugate Bayesian models using the Gibbs sampler. Canadian Journal of Statistics. 19:1991;399-405.
5. Carter C.K., Kohn R. On Gibbs sampling for state space models. Biometrika. 81:1994;541-553.
6. Chib S., Greenberg Understanding the Metropolis-Hastings algorithm. The American Statistician. 49:1995;327-335.
7. Chib S., Nardari F., Shephard N. Markov chain Monte Carlo methods for stochastic volatility models. Journal of Econometrics. 108:2002;281-316.
8. Christoffersen, P., Diebold, F., 1997. How relevant is volatility forecasting for risk management. Working Paper, Pennsylvania Economics Department.
9. Diaconis P., Strook D. Geometric bounds for eigen values of Markov chains. Annals of Applied Probability. 1:1991;36-61.
10. Dickey J. The weighted likelihood ratio, linear hypotheses on normal location parameters. Annals of Mathematical Statistics. 42:1971;204-224.
11. Dumas B., Fleming J., Whaley R. Implied volatility functions. empirical tests Journal of Finance. 53:1998;2059-2106.
12. Eraker, B., Jacquier, E., Polson, N., 1998. Pitfalls in MCMC algorithms. Department of Econometrics and Statistics. Graduate School of Business, University of Chicago Technical Report, 98-04.
13. Fridman M., Harris L. A maximum likelihood approach for non-Gaussian stochastic volatility models. Journal of Business and Economics Statistics. 16(3):1998;284-291.
14. Gallant A.R., Hsieh D., Tauchen G. Estimation of stochastic volatility models with diagnostics. Journal of Econometrics. 81(1):1997;159-192.
15. Geweke J. Exact predictive densities for linear models with ARCH disturbances. Journal of Econometrics. 40:1989;63-86.
16. Geweke J. Bayesian inference in econometric models using Monte Carlo integration. Econometrica. 57:1989;1317-1339.
17. Geweke, J., 1992. Evaluating the accuracy of sampling-base approaches to the calculation of posterior moments. In: Berger, J.O., Bernardo, J.M., Dawid, A.P., Smith, A.F.M. (Eds.), Bayesian Statistics, Vol. 4. Oxford University Press, Oxford, pp. 169-194.
18. Geweke J. Bayesian treatment of the independent student-. t linear model Journal of Applied Econometrics. 8:1993;S19-S40.
19. Geweke J. Priors for macroeconomic time series and their application. Econometric Theory. 10:1994;609-632.
20. Geweke, J., 1994b. Bayesian comparison of econometric models. Working Paper, Federal Reserve Bank of Minneapolis Research Department.
21. Geweke J. Comment on Bayesian analysis of stochastic volatility. Journal of Business and Economics Statistics. 12(4):1994;371-417.
22. Glosten L., Jagannathan R., Runkle D. On the relation between the expected value and the volatility of the nominal excess return on stocks. Journal of Finance. 48:1993;1779-1801.
23. Hastings W.K. Monte Carlo sampling methods using Markov chains and their applications. Biometrika. 57:1970;97-109.
24. Hull J., White A. The pricing of options on assets with stochastic volatility. Journal of Finance. 3:1987;281-300.
25. Jacquier, E., Polson, N., 2000. Odds ratios for non-nested models: application to stochastic volatility models. Boston College Working Paper.
26. Jacquier E., Polson N., Rossi P. Bayesian analysis of stochastic volatility models (with discussion). Journal of Business and Economic Statistics. 12(4):1994;371-417.
27. Kass R., Raftery A. Bayes factors. Journal of the American Statistical Association. 90:1995;773-795.
28. Kim S., Shephard N., Chib S. Stochastic volatility. likelihood inference and comparison with ARCH models Review of Economic Studies. 65:1998;361-393.
29. Mahieu R., Schotman P. An empirical application of stochastic volatility models. Journal of Applied Econometrics. 13(4):1998;333-360.
30. McCulloch R., Polson N., Rossi P. Bayesian analysis of the multinomial probit with fully identified parameters. Journal of Econometrics. 99:2000;173-193.
31. Meddahi, N., Renault, E., 2000. Temporal aggregation of volatility models. CIRANO Discussion Paper 2000s-22.
32. Nelson D. Conditional heteroskedasticity in asset pricing: a new approach. Econometrica. 59:1991;347-370.
33. Newton M., Raftery A. Approximate Bayesian inference with the weighted likelihood bootstrap. Journal of the Royal Statistical Society Series B. 57:1994;3-48.
34. Shephard N., Kim S. Comment of Bayesian analysis of stochastic volatility by Jacquier, Polson, and Rossi. Journal of Business and Economics Statistics. 12(4):1994;371-417.
35. Tierney L. Markov chains for exploring posterior distributions. Annals of Statistics. 22:1994;1701-1762.
36. Wild P., Gilks W. Adaptive rejection sampling from log-concave densities. Journal of the Royal Statistical Society Series C. 42:1993;701-708.