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Particle learning for Bayesian non-parametric Markov Switching Stochastic Volatility model

Listed author(s):
  • Galeano San Miguel, Pedro
  • Ausín Olivera, María Concepción
  • Virbickaite, Audrone
  • Lopes, Hedibert F.

This paper designs a Particle Learning (PL) algorithm for estimation of Bayesian nonparametric Stochastic Volatility (SV) models for financial data. The performance of this particle method is then compared with the standard Markov Chain Monte Carlo (MCMC) methods for non-parametric SV models. PL performs as well as MCMC, and at the same time allows for on-line type inference. The posterior distributions are updated as new data is observed, which is prohibitively costly using MCMC. Further, a new non-parametric SV model is proposed that incorporates Markov switching jumps.The proposed model is estimated by using PL and tested on simulated data. Finally, the performance of the two non-parametric SV models, with and without Markov switching, is compared by using real financial time series. The results show that including a Markov switching specification provides higher predictive power in the tails of the distribution.

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File URL: https://e-archivo.uc3m.es/bitstream/handle/10016/19576/ws142819.pdf?sequence=1
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Paper provided by Universidad Carlos III de Madrid. Departamento de Estadística in its series DES - Working Papers. Statistics and Econometrics. WS with number ws142819.

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Date of creation: Oct 2014
Handle: RePEc:cte:wsrepe:ws142819
Contact details of provider: Web page: http://portal.uc3m.es/portal/page/portal/dpto_estadistica

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  1. Jacquier, Eric & Polson, Nicholas G & Rossi, Peter E, 2002. "Bayesian Analysis of Stochastic Volatility Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 69-87, January.
  2. Jacquier, Eric & Polson, Nicholas G & Rossi, Peter E, 1994. "Bayesian Analysis of Stochastic Volatility Models: Comments: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(4), pages 413-417, October.
  3. Sangjoon Kim & Neil Shephard & Siddhartha Chib, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," Review of Economic Studies, Oxford University Press, vol. 65(3), pages 361-393.
  4. repec:bla:restud:v:65:y:1998:i:3:p:361-93 is not listed on IDEAS
  5. Jensen, Mark J. & Maheu, John M., 2013. "Bayesian semiparametric multivariate GARCH modeling," Journal of Econometrics, Elsevier, vol. 176(1), pages 3-17.
  6. Jensen, Mark J. & Maheu, John M., 2010. "Bayesian semiparametric stochastic volatility modeling," Journal of Econometrics, Elsevier, vol. 157(2), pages 306-316, August.
  7. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
  8. Maria Kalli & Stephen G. Walker & Paul Damien, 2013. "Modeling the Conditional Distribution of Daily Stock Index Returns: An Alternative Bayesian Semiparametric Model," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(4), pages 371-383, October.
  9. Delatola, E.-I. & Griffin, J.E., 2013. "A Bayesian semiparametric model for volatility with a leverage effect," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 97-110.
  10. Carmen Broto & Esther Ruiz, 2004. "Estimation methods for stochastic volatility models: a survey," Journal of Economic Surveys, Wiley Blackwell, vol. 18(5), pages 613-649, December.
  11. Chib, Siddhartha & Nardari, Federico & Shephard, Neil, 2002. "Markov chain Monte Carlo methods for stochastic volatility models," Journal of Econometrics, Elsevier, vol. 108(2), pages 281-316, June.
  12. So, Mike K P & Lam, K & Li, W K, 1998. "A Stochastic Volatility Model with Markov Switching," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 244-253, April.
  13. Hedibert F. Lopes & Ruey S. Tsay, 2011. "Particle filters and Bayesian inference in financial econometrics," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(1), pages 168-209, January.
  14. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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