European Financial and Accounting Journal 2015, 10(2):5-11 | DOI: 10.18267/j.efaj.138

Estimating the Value-at-Risk from High-frequency Data

Pavol Krasnovský
Pavol Krasnovský; PhD candidate; Department of Monetary Theory and Policy, University of Economics Prague, W. Churchill Sq. 4, 130 67 Prague 3

We present two alternative approaches for estimating VaR. Both approaches are based on the observation that each trading day is very diverse and we can observe K different phases of the trading day. We can not observe from which of the K phases our observations rt are. Therefore, we apply Gibbs sampler to estimate parameters from our data. In the latter approach, we apply Dubins and Schwarz theorem (Kallenberg, 2000), which allows us to re-scale our portfolio returns rt and to get normal distributed returns rJt~N(0,Jt). To verify our approaches, we make an empirical application.

Keywords: Data augmentation, Gibbs sampler, Quadratic variation, Time changed Brownian motion
JEL classification: C15, C53

Published: June 1, 2015  Show citation

ACS AIP APA ASA Harvard Chicago IEEE ISO690 MLA NLM Turabian Vancouver
Krasnovský, P. (2015). Estimating the Value-at-Risk from High-frequency Data. European Financial and Accounting Journal, 10(2), 5-11. doi: 10.18267/j.efaj.138
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., 2001. The Distribution of Realized Stock Returns Volatility. Journal of Financial Economics 61, 43-76, New York. Go to original source...
    2. Bishop, Ch., 2007. Pattern Recognition and Machine Learning. Springer, New York.
    3. Brooks, S., 2011. Time and the Process of Security Price Adjustment. Chapman and Hall/CRC, London.
    4. O'Hara, M., 1992. Time and the Process of Security Price Adjustment. Journal of Finance 47, 577-605, New York. Go to original source...
    5. O'Hara, M., 1998. Market Microstructure Theory. Wiley, New York.
    6. Hendricks, O., 1996. Evaluation of Value-at-Risk Model Using Historical Data. Economic Policy Review, 39-69, New York. Go to original source...
    7. Hasbrouck, J., 2007. Empirical Market Microstructure: The Institutions, Economics, and Econometrics of Securities Trading. Oxford University Press, Oxford.
    8. Kallenberg, O., 2000. Foundations of Probability. Springer, Berlin.
    9. Kupiec, P., 1995. Techniques for Verifying the Accuracy of Risk Management Models. Journal of Derivatives 2, 73-84, New York. Go to original source...
    10. Musiela, M., 2011. Martingale Methods in Financial Modelling. Springer, New York.
    11. Robert, Ch., 2005. Monte Carlo Statistical Methods. Springer, New York. Go to original source...
    12. Rubinstein, R., 2007. Simulation and the Monte Carlo Method. Wiley-Interscience, New York. Go to original source...
    13. Shreve, S., 2013. Stochastic Calculus for Finance II: Continuous-Time Models. Springer, New York.
    14. Vlaar, P., 1998. Value-at-Risk models for Dutch Bond Portfolios. Journal of Banking and Finance, 24-32, Amsterdam. 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