Full bayesian inference for GARCH and EGARCH models

Journal of Business and Economic Statistics

Volumn 18, Issue 2, 2000, Pages 187-198

  Vrontos, I D ^a   Dellaportas, P ^a   Politis, D N ^b  

^a ATHENS UNIVERSITY OF ECONOMICS AND BUSINESS   (Greece)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Markov chain Monte Carlo; Model averaging; Reversible jump; Volatility prediction

Indexed keywords

EID: 0034403866     PISSN: 07350015     EISSN: 15372707     Source Type: Journal    
DOI: 10.1080/07350015.2000.10524861     Document Type: Article

References (37)

1. Baillie, R., and Boiler slev, T. (1989), “The Message in Daily Exchange Rates: A Conditional Variance Tale,” Journal of Business & Economic Statistics, 1, 297-305.
2. Bartlett, M. S. (1957), Comment on “A Statistical Paradox,” by D. V. Lind-ley, Biometrika, 44, 533-534.
3. Berger, J. O., and Delampady, M. (1987), “Testing Precise Hypotheses” (with discussion), Statistical Science, 2, 317-352.
4. Besag, J., Green, P. J., Higdon, D., and Mengersen, K. (1995), “Bayesian Computation and Stochastic Systems” (with discussion), Statistical Science, 10, 3-66.
5. Bollerslev, T. (1986), “Generalized Autoregressive Conditional Het-eroskedasticity,” Journal of Econometrics, 31, 307-327.
6. Bollerslev, T. (1987), “A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return,” Review of Economics and Statistics, 69, 542-547.
7. Bollerslev, T., Chou, R. Y., and Kroner, K. F. (1992), “ARCH Modeling in Finance-A Review of the Theory and Empirical Evidence,” Journal of Econometrics, 52, 5-59.
8. Chib, S., and Greenberg, E. (1995), “Understanding the MetropolisHastings Algorithm,” The American Statistician, 49, 327-335.
9. Dellaportas, P., and Forster, J. (1999), “Markov Chain Monte Carlo Model Determination for Hierarchical and Graphical Log-Linear Model,” Biometrika, 86, 615-633.
10. Draper, D. (1995), “Assessment and Propagation of Model Uncertainty” (with discussion), Journal of the Royal Statistical Society, Ser. B, 57, 45-97.
11. Dubofsky, D. A. (1992), Options and Financial Futures: Valuation and Uses, Singapore: McGraw-Hill.
12. Engle, R. F. (1982), “Autoregressive Conditional Heteroskedasticity With Estimates of the Variance of U.K. Inflation,” Econometrica, 50, 987-1008.
13. Engle, R. F. (1995), ARCH Selected Readings, New York: Oxford University Press.
14. Engle, R. F., and Ng, V. K. (1993), “Measuring and Testing the Impact of News on Volatility,” Journal of Finance, 48, 1749-1778.
15. Evans, M., and Swartz, T. (1995), “Methods for Approximating Integrals in Statistics With Special Emphasis on Bayesian Integration Problems,” Statistical Science, 3, 254-272.
16. Gelfand, A. E., Smith, A. F. M., and Lee, T. M. (1992), “Bayesian Analysis of Constrained Parameter and Truncated Data Problems Using Gibbs Sampling,” Journal of the American Statistical Association, 873, 523-532.
17. Geweke, J. (1989), “Exact Predictive Densities for Linear Models With ARCH Disturbances,” Journal of Econometrics, 40, 63-86.
18. Geweke, J. (1992), “Evaluating the Accuracy of Sampling-Based Approaches to the Calculation of Posterior Moments,” in Bayesian Statistics 4, eds. J. M. Bernardo, J. O. Berger, A. P. Dawid, and A. F. M. Smith, Oxford, U.K.: Clarendon Press, pp. 169-193.
19. Green, P. J. (1995), “Reversible Jump Markov Chain Monte Carlo Computation and Bayesian Model Determination,” Biometrika, 82, 711-732.
20. Heidelberger, P., and Welch, P. W. (1983), “Simulation Run Length Control in the Presence of an Initial Transient,” Operations Research, 31,1109- 1144.
21. Hills, S. E., and Smith, A. F. M. (1992), “Parameterization Issues in Bayesian Inference,” in Bayesian Statistics 4, eds. J. M. Bernardo, J. O. Berger, A. P. Dawid, and A. F. M. Smith, Oxford, U.K.: Clarendon Press, pp. 227-246.
22. Jacquier, E., Poison, N. G., and Rossi, P. E. (1994), “Bayesian Analysis of Stochastic Volatility Models” (with discussion), Journal of Business & Economic Statistics, 12, 371-417.
23. Jeffreys, H. (1961), Theory of Probability (3rd ed.), London: Oxford University Press.
24. Kass, R. E., and Raftery, A. E. (1995), “Bayes Factors and Model Uncertainty,” Journal of the American Statistical Association, 90, 773-795.
25. Lindley, D. V. (1957), “A Statistical Paradox,” Biometrika, 44, 187-192.
26. Madigan, D., Gavrin, J., and Raftery, A. E. (1995), “Eliciting Prior Information to Enhance the Predictive Performance of Bayesian Graphical Models,” Communications in Statistics, Part A-Theory and Methods, 24, 2271-2292.
27. Muller P., and Pole, A. (in press), “Monte Carlo Posterior Integration in GARCH Models,” Sankhya, 62.
28. Nelson, D. (1991), “Conditional Heteroskedasticity in Asset Returns: A New Approach,” Econometrica, 59, 347-370.
29. Nobile, A., and Green, P. J.(in press), “Bayesian Analysis of Factorial Experiments by Mixture Modelling,” Biometrika, 87.
30. Noh, I., Engle, R. P., and Kane, A. (1994), “Forecasting Volatility and Option Prices of the S&P 500 Index,” Journal of Derivatives, 17-30.
31. Pagan, A. R., and Schwert, G. W. (1990), “Alternative Models for Conditional Stock Volatility,” Journal of Econometrics, 45, 267-290.
32. Raftery, A. E., Madigan, D., and Hoeting, J. (1997), “Model Selection and Accounting for Model Uncertainty in Linear Regression Models,” Journal of the American Statistical Association, 92, 179-191.
33. Richardson, S., and Green, P. J. (1997), “On Bayesian Analysis of Mixtures With an Unknown Number of Components” (with discussion), Journal of the Royal Statistical Society, Ser. B, 59, 731-792.
34. Shephard, N. (1996), “Statistical Aspects of ARCH and Stochastic Volatility,” in Time Series Models in Econometrics, Finance and Other Fields, eds. D. R. Cox, D. V. Hinkley, and O. E. Barndorff-Nielsen, London: Chapman & Hall, pp. 1-55.
35. Smith, A. F. M., and Roberts, G. O. (1993), “Bayesian Computation via the Gibbs Sampler and Related Markov Chain Monte Carlo Methods,” Journal of the Royal Statistical Society, Ser. B, 55, 3-24.
36. Tierney, L. (1994), “Markov Chains for Exploring Posterior Distributions” (with discussion), The Annals of Statistics, 22, 1701-1762.
37. Volinsky, C. T., Madigan, D., Raftery, A. E., and Kronmal, R. A. (1997), “Bayesian Model Averaging in Proportional Hazard Models: Predicting the Risk of a Stroke,” Applied Statistics, 46, 443-448.