European Financial and Accounting Journal 2018, 13(3):5-20 | DOI: 10.18267/j.efaj.211

Use of Adapted Particle Filters in SVJD Models

Milan Fičura1, Jiří Witzany2
1 University of Economics, Prague, Faculty of Finance and Accounting, Department of Banking and Finance, W. Churchill Sq. 4, 130 67 Prague 3, Czech Republic.
2 University of Economics, Prague, Faculty of Finance and Accounting, Department of Banking and Finance, W. Churchill Sq. 4, 130 67 Prague 3, Czech Republic.

Particle Filter algorithms for filtering latent states (volatility and jumps) of Stochastic-Volatility Jump-Diffusion (SVJD) models are being explained. Three versions of the SIR particle filter with adapted proposal distributions to the jump occurrences, jump sizes, and both are derived and their performance is compared in a simulation study to the un-adapted particle filter. The filter adapted to both the jump occurrences and jump sizes achieves the best performance, followed in their respective order by the filter adapted only to the jump occurrences and the filter adapted only to the jump sizes. All adapted particle filters outperformed the unadapted particle filter.

Keywords: Particle Filters, Stochastic Volatility, Price Jumps
JEL classification: C11, C14, C15, C22, G1

Published: December 31, 2018  Show citation

ACS AIP APA ASA Harvard Chicago Chicago Notes IEEE ISO690 MLA NLM Turabian Vancouver
Fičura, M., & Witzany, J. (2018). Use of Adapted Particle Filters in SVJD Models. European Financial and Accounting Journal, 13(3), 5-20. doi: 10.18267/j.efaj.211
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. Andersen, T. G., Bollerslev, T., Christoffersen, P. F., Diebold, F. X., 2005. Volatility Forecasting. National Bureau of Economic Research, NBER Working Paper No. 11188. DOI: 10.3386/w11188. Go to original source...
    2. Bates, D. S., 1996. Jumps and Stochastic Volatility: Exchange Rate Processes Implicit in Deutsche Mark Options. Review of Financial Studies 9, 69-107. DOI: 10.1093/rfs/9.1.69. Go to original source...
    3. Eraker, B., Johannes, M., Polson, N., 2003. The Impact of Jumping Equity Index Volatility and Returns. The Journal of Finance 58, 1269-1300. DOI: 10.1111/1540-6261.00566. Go to original source...
    4. Fičura, M., Witzany, J., 2016. Estimating Stochastic Volatility and Jumps Using High-Frequency Data and Bayesian Methods. Czech Journal of Economics and Finance 66, 279-301. DOI: 10.2139/ssrn.2551807. Go to original source...
    5. Fulop, A., Li, J., 2013. Efficient Learning via Simulation: A Marginalized Resample-Move Approach. Journal of Econometrics 176, 146-161. DOI: 10.1016/j.jeconom.2013.05.002. Go to original source...
    6. Fulop, A., Li, J., Yu, J., 2015. Self-Exciting Jumps, Learning and Asset Pricing Implications. Review of Financial Studies 28, 876-912. DOI: 10.1093/rfs/hhu078. Go to original source...
    7. Gordon, N. J., Salmond, D. J., Smith, A. F. M., 1993. Novel Approach to Nonlinear/Non-Gaussian Bayesian State Estimation. IEE Proceedings F on Radar and Signal Processing 140, 107-113. DOI: 10.1049/ip-f-2.1993.0015. Go to original source...
    8. Jacquier, E., Johannes, M., Polson, N., 2007. MCMC Maximum Likelihood for Latent State Models. Journal of Econometrics 137, 615-640. DOI: 10.1016/j.jeconom.2005.11.017. Go to original source...
    9. Jacquier, E., Polson, N., Rossi, P., 1994. Bayesian Analysis of Stochastic Volatility Models. Journal of Business & Economic Statistics 12, 371-389. DOI: 10.1080/07350015.1994.10524553 Go to original source...
    10. Kim, S., Shephard, N., Chib, S., 1998. Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models. Review of Economic Studies 65, 361-393. DOI: 10.1111/1467-937x.00050. Go to original source...
    11. Pitt, M. K., Shephard, N., 1999. Filtering via Simulation: Auxiliary Particle Filters. Journal of the American Statistical Association 94, 590-599. DOI: 10.1080/01621459.1999.10474153. Go to original source...
    12. Shephard, N., 2005. Stochastic Volatility: Selected Readings (Advanced Texts in Econometrics). Oxford University Press, Oxford. Go to original source...
    13. Witzany, J., 2013. Estimating Correlated Jumps and Stochastic Volatilities. Prague Economic Papers 22, 251-283. DOI: 10.18267/j.pep.451. Go to original source...

    This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.


    Return to the content