Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2008-2009 | 49-50 | 109--143
Tytuł artykułu

Markov Switching in Stochastic Variance : Bayesian Comparision of Two Simple Models

Warianty tytułu
Języki publikacji
In the paper two particular Markov Switching Stochastic Volatility models (MSSV) are under consideration: one with a switching intercept in the log-volatility equation, and the other — with a regime-dependent autoregression parameter. While the former one is fairly common in the literature (as a tool of taking account for regimes of different mean volatility level), the latter has not been paid almost any attention so far. We note the fact, that state-varying mean volatility may arise from switches in the intercept or in the autoregression parameter. Hence, we aim to compare these two models in respect of goodness of fit to the data from the Polish financial market, employing Bayesian techniques of estimation and model comparison. Clear evidence of structural shifts in the volatility pattern is found. Two different regimes of the economy are characterized in terms of the mean volatility level and the variance of volatility. (original abstract)
Opis fizyczny
  • Krakowska Akademia im. Andrzeja Frycza Modrzewskiego w Krakowie
  • Bauwens L., Preminger A., Rombouts J. 2006. Regime Switching GARCH Models, Core Discussion Paper, Département des Sciences Économiques de l'Université catholique de Louvain.
  • Bollerslev T. 1987. Generalised Autoregressive Conditional Heteroskedasticity, Journal of Econometrics 31.
  • Casarin R. 2003. Bayesian Inference for Generalised Markov Switching Stochastic Volatility Models, Conference materials at the 4th International Workshop on Objective Bayesian Methodology, CNRS, Aussois.
  • Carter C.K., Kohn R. 1994. On Gibbs sampling for state space models, Biometrika 81, 3.
  • Carvalho C.M., Lopes H.R 2006. Simulation-based sequential analysis of Markov switching stochastic volatility models, Computational Statistics & Data Analysis, doi: 10.1016/j.csda. 2006.07.019.
  • Diebold EX., Inoue A. 2001. Long Memory and Regime Switching, Journal of Econometrics, 105.
  • Francq C., Zakoian J.-M. 2001. Stationarity of multivariate Markov-switching ARMA models, Journal of Econometrics 102.
  • Frühwirth-Schnatter S. 2001. Markov Chain Monte Carlo estimation of classical and dynamic switching and mixture models, Journal of the American Statistical Association 96.
  • Gartner D. 2007. Why Bayes Rules: A Note on Bayesian vs. Classical Inference in Regime Switching Models, Working Paper No. 0719, University of Zurich.
  • Granger C.W.J., Hyung N. 1999. Occasional Structural Breaks and Long Memory, Discussion Paper 99-14, Department of Economics, University of California, San Diego.
  • Hamilton J.D. 1989. A New approach to the economic analysis of nonstationary time series and the business cycle, Econometrica 57, 2.
  • Hwang S., Satchell S. E., Pereira P. L. V. 2003. Stochastic Volatility Models with Markov Regime Switching State Equations, Journal of Business and Economic Statistics 16, 2.
  • Hwang S., Satchell S. E., Pereira P. L. V. 2004. How Persistent is Volatility? An Answer with Stochastic Volatility Models with Markov Regime Switching State Equations, CEA@Cass Working Paper Series,
  • Kalimipalli M., Susmel R. 2001. Regime-switching stochastic volatility and short-term interest rates, CEMA Working Papers, http://ideas.repec.Org/p/cem/doctra/197.html
  • Krolzig, H.-M. 1997. Markov-Switching Vector Autoregressions: Modelling, Statistical Inference, and Application to Business Cycle Analysis, Lecture Notes in Economics and Mathematical Systems, New York/Berlin/Heidelberg: Springer.
  • Newton M.A., Raftery A.E. 1994. Approximate Bayesian inference by the Weighted Likelihood Bootstrap with Discussion. Journal of the Royal Statistical Society B 56, 1.
  • Nielsen S., Olesen J. O. 2000. Regime-switching stock returns and mean reversion, Working paper 11-2000, Institut for Nationalokonomi,
  • Shibata M., Watanabe T. 2005. Bayesian analysis of a Markov switching stochastic volatility model, Journal of Japan Statistical Society 35, 2.
  • So M. K. P., Lam K., Li W. K. 1998. A stochastic volatility model with Markov switching, Journal of Business and Economic Statistics 16, 2.
  • Pajor A., 2003. Procesy zmienności stochastycznej SV w bayesowskiej analizie finansowych szeregów czasowych (Stochastic Volatility Processes in Bayesian analysis of financial time series), Cracow University of Economics, Cracow 2003.
  • Smith D. R., 2000. Markov-switching and stochastic volatility diffusion models for short-term interest rates,
  • Tanner M.T., Wong W. 1987. The Calculation of Posterior Distributions by Data Augmentation, Journal of the American Statistical Association 82.
Typ dokumentu
Identyfikator YADDA

Zgłoszenie zostało wysłane

Zgłoszenie zostało wysłane

Musisz być zalogowany aby pisać komentarze.
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.