Bayesian non-parametric signal extraction for Gaussian time series
We consider the problem of unobserved components in time series from a Bayesian non-parametric perspective. The identification conditions are treated as unknown and analyzed in a probabilistic framework. In particular, informative prior distributions force the spectral decomposition to be in an identifiable region. Then, the likelihood function adapts the prior decompositions to the data. A full Bayesian analysis of unobserved components will be presented for financial high frequency data. Particularly, a three component model (long-term, intra-daily and short-term) will be analyzed to emphasize the importance and the potential of this work when dealing with the Value-at-Risk analysis. A second astronomical application will show how to deal with multiple periodicities.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Drost, F.C. & Nijman, T.E., 1992.
"Temporal aggregation of GARCH processes,"
1992-40, Tilburg University, Center for Economic Research.
- Drost, F.C. & Nijman, T.E., 1992. "Temporal Aggregation of Garch Processes," Papers 9240, Tilburg - Center for Economic Research.
- Drost, F.C. & Nijman, T.E., 1990. "Temporal aggregation of GARCH processes," Discussion Paper 1990-66, Tilburg University, Center for Economic Research.
- Drost, F.C. & Nijman, T.E., 1990. "Temporal Aggregation Of Garch Processes," Papers 9066, Tilburg - Center for Economic Research.
- Durbin, J. & Koopman, S.J.M., 1998.
"Time Series Analysis of Non-Gaussian Observations Based on State Space Models from Both Classical and Bayesian Perspectives,"
1998-142, Tilburg University, Center for Economic Research.
- J. Durbin & S. J. Koopman, 2000. "Time series analysis of non-Gaussian observations based on state space models from both classical and Bayesian perspectives," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(1), pages 3-56.
- Drost, F.C. & Nijman, T.E., 1993. "Temporal aggregation of GARCH processes," Other publications TiSEM 0642fb61-c7f4-4281-b484-4, Tilburg University, School of Economics and Management.
- Deo, Rohit & Hurvich, Clifford & Lu, Yi, 2006.
"Forecasting realized volatility using a long-memory stochastic volatility model: estimation, prediction and seasonal adjustment,"
Journal of Econometrics,
Elsevier, vol. 131(1-2), pages 29-58.
- Rohit Deo & Clifford Hurvich & Yi Lu, 2005. "Forecasting Realized Volatility Using a Long Memory Stochastic Volatility Model: Estimation, Prediction and Seasonal Adjustment," Econometrics 0501002, EconWPA.
- Torben G. Andersen, 2001. "Variance-ratio Statistics and High-frequency Data: Testing for Changes in Intraday Volatility Patterns," Journal of Finance, American Finance Association, vol. 56(1), pages 305-327, 02.
- McCoy, E. J. & Stephens, D. A., 2004. "Bayesian time series analysis of periodic behaviour and spectral structure," International Journal of Forecasting, Elsevier, vol. 20(4), pages 713-730.
- Kleijn, R.H. & van Dijk, H.K., 2003.
"Bayes model averaging of cyclical decompositions in economic time series,"
Econometric Institute Research Papers
EI 2003-48, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
- Richard Kleijn & Herman K. van Dijk, 2006. "Bayes model averaging of cyclical decompositions in economic time series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(2), pages 191-212.
- Andersen, Torben G. & Bollerslev, Tim, 1997. "Intraday periodicity and volatility persistence in financial markets," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 115-158, June.
- Sonia Petrone, 1999. "Random Bernstein Polynomials," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 26(3), pages 373-393.
- Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
- Ole E. Barndorff-Nielsen & Neil Shephard, 2000.
"Econometric analysis of realised volatility and its use in estimating stochastic volatility models,"
2001-W4, Economics Group, Nuffield College, University of Oxford, revised 05 Jul 2001.
- Ole E. Barndorff-Nielsen & Shephard, 2002. "Econometric analysis of realized volatility and its use in estimating stochastic volatility models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(2), pages 253-280.
- Lawrence J. Christiano & Terry J. Fitzgerald, 2003.
"The Band Pass Filter,"
International Economic Review,
Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 44(2), pages 435-465, 05.
- Lawrence J. Christiano & Terry J. Fitzgerald, 1999. "The Band Pass Filter," NBER Working Papers 7257, National Bureau of Economic Research, Inc.
- Tom Doan, "undated". "CFFILTER: RATS procedure to perform band pass filter using Christiano-Fitzgerald method," Statistical Software Components RTS00034, Boston College Department of Economics.
- Lawrence J. Christiano & Terry J. Fitzgerald, 1999. "The Band pass filter," Working Paper 9906, Federal Reserve Bank of Cleveland.
- Harvey, A.C. & Trimbur, T.M. & van Dijk, H.K., 2005.
"Trends and cycles in economic time series: A Bayesian approach,"
Econometric Institute Research Papers
EI 2005-27, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
- Harvey, Andrew C. & Trimbur, Thomas M. & Van Dijk, Herman K., 2007. "Trends and cycles in economic time series: A Bayesian approach," Journal of Econometrics, Elsevier, vol. 140(2), pages 618-649, October.
- Andersen, Torben G. & Bollerslev, Tim & Lange, Steve, 1999. "Forecasting financial market volatility: Sample frequency vis-a-vis forecast horizon," Journal of Empirical Finance, Elsevier, vol. 6(5), pages 457-477, December.
- G. Huerta & M. West, 1999. "Priors and component structures in autoregressive time series models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(4), pages 881-899.
- Honaker, James & King, Gary & Blackwell, Matthew, 2011. "Amelia II: A Program for Missing Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i07).
- Jon Wongswan, 2003.
"Transmission of information across international equity markets,"
International Finance Discussion Papers
759, Board of Governors of the Federal Reserve System (U.S.).
- Jon Wongswan, 2006. "Transmission of Information across International Equity Markets," Review of Financial Studies, Society for Financial Studies, vol. 19(4), pages 1157-1189.
- Nidhan Choudhuri & Subhashis Ghosal & Anindya Roy, 2004. "Bayesian Estimation of the Spectral Density of a Time Series," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 1050-1059, December.
- Martin Martens & Yuan-Chen Chang & Stephen J. Taylor, 2002. "A Comparison of Seasonal Adjustment Methods When Forecasting Intraday Volatility," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 25(2), pages 283-299.
- Pollock, D.S.G., 2007. "Wiener Kolmogorov Filtering, Frequency-Selective Filtering, And Polynomial Regression," Econometric Theory, Cambridge University Press, vol. 23(01), pages 71-88, February.
When requesting a correction, please mention this item's handle: RePEc:eee:econom:v:157:y:2010:i:2:p:381-395. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.