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Non parametric VaR Techniques. Myths and Realities

Author

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  • Giovanni Barone-Adesi
  • Kostas Giannopoulos

Abstract

type="main" xml:lang="en"> VaR (value-at-risk) estimates are currently based on two main techniques: the variance-covariance approach or simulation. Statistical and computational problems affect the reliability of these techniques. We illustrate a new technique – filtered historical simulation (FHS) – designed to remedy some of the shortcomings of the simulation approach. We compare the estimates it produces with traditional bootstrapping estimates. (J.E.L.: G19).

Suggested Citation

  • Giovanni Barone-Adesi & Kostas Giannopoulos, 2001. "Non parametric VaR Techniques. Myths and Realities," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 30(2), pages 167-181, July.
    • Handle: RePEc:bla:ecnote:v:30:y:2001:i:2:p:167-181
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    Citations

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    Cited by:

    1. Nieto, María Rosa, 2008. "Measuring financial risk : comparison of alternative procedures to estimate VaR and ES," DES - Working Papers. Statistics and Econometrics. WS ws087326, Universidad Carlos III de Madrid. Departamento de Estadística.
    2. Escanciano, Juan Carlos & Pei, Pei, 2012. "Pitfalls in backtesting Historical Simulation VaR models," Journal of Banking & Finance, Elsevier, vol. 36(8), pages 2233-2244.
    3. Samuel N. Cohen & Christoph Reisinger & Sheng Wang, 2022. "Estimating risks of option books using neural-SDE market models," Papers 2202.07148, arXiv.org.
    4. Nikola RADIVOJEVIĆ & Luka FILIPOVI & Тomislav D. BRZAKOVIĆ, 2020. "A New Semiparametric Mirrored Historical Simulation Value-At-Risk Model," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 5-21, March.
    5. Wang, Guochang & Zhu, Ke & Li, Guodong & Li, Wai Keung, 2022. "Hybrid quantile estimation for asymmetric power GARCH models," Journal of Econometrics, Elsevier, vol. 227(1), pages 264-284.
    6. Guochang Wang & Ke Zhu & Guodong Li & Wai Keung Li, 2019. "Hybrid quantile estimation for asymmetric power GARCH models," Papers 1911.09343, arXiv.org.
    7. Chlebus Marcin, 2017. "EWS-GARCH: New Regime Switching Approach to Forecast Value-at-Risk," Central European Economic Journal, Sciendo, vol. 3(50), pages 01-25, December.
    8. Agnieszka Surowiec & Tomasz Warowny, 2021. "Covid-19 Death Risk Estimation Using VaR Method," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 2), pages 368-379.
    9. Shaozhen Chen & Bangqian Zhang & Jinjin Deng, 2018. "Research on Risk Measurement in Financial Market Based on GARCH-VaR and FHS¡ª¡ªAn Example of Chinese Bond Market," Applied Economics and Finance, Redfame publishing, vol. 5(4), pages 102-116, July.
    10. Timotheos Angelidis & Alexandros Benos, 2008. "Value-at-Risk for Greek Stocks," Multinational Finance Journal, Multinational Finance Journal, vol. 12(1-2), pages 67-104, March-Jun.
    11. Benjamin R. Auer & Benjamin Mögel, 2016. "How Accurate are Modern Value-at-Risk Estimators Derived from Extreme Value Theory?," CESifo Working Paper Series 6288, CESifo.
    12. Francesco Paolo Natale, 2008. "Optimisation in the presence of tail-dependence and tail risk: A heuristic approach for strategic asset allocation," Journal of Asset Management, Palgrave Macmillan, vol. 8(6), pages 374-400, February.
    13. Giannopoulos, Kostas & Tunaru, Radu, 2005. "Coherent risk measures under filtered historical simulation," Journal of Banking & Finance, Elsevier, vol. 29(4), pages 979-996, April.
    14. Claude Martini & Arianna Mingone, 2023. "A closed form model-free approximation for the Initial Margin of option portfolios," Papers 2306.16346, arXiv.org.
    15. Marimoutou, Velayoudoum & Raggad, Bechir & Trabelsi, Abdelwahed, 2009. "Extreme Value Theory and Value at Risk: Application to oil market," Energy Economics, Elsevier, vol. 31(4), pages 519-530, July.
    16. Emrah Altun & Huseyin Tatlidil & Gamze Ozel & Saralees Nadarajah, 2018. "Does the Assumption on Innovation Process Play an Important Role for Filtered Historical Simulation Model?," JRFM, MDPI, vol. 11(1), pages 1-13, January.
    17. Benjamin Mögel & Benjamin R. Auer, 2018. "How accurate are modern Value-at-Risk estimators derived from extreme value theory?," Review of Quantitative Finance and Accounting, Springer, vol. 50(4), pages 979-1030, May.
    18. Timotheos Angelidis & Alexandros Benos & Stavros Degiannakis, 2007. "A robust VaR model under different time periods and weighting schemes," Review of Quantitative Finance and Accounting, Springer, vol. 28(2), pages 187-201, February.
    19. Chen, Fen-Ying & Liao, Szu-Lang, 2009. "Modelling VaR for foreign-asset portfolios in continuous time," Economic Modelling, Elsevier, vol. 26(1), pages 234-240, January.
    20. Bagher Adabi & Mohsen Mehrara & Shapour Mohammadi, 2015. "Evaluation Approaches of Value at Risk for Tehran Stock Exchange," Iranian Economic Review (IER), Faculty of Economics,University of Tehran.Tehran,Iran, vol. 19(1), pages 41-62, Winter.
    21. Murphy, David, 2023. "What can we expect from a good margin model? Observations from whole-distribution tests of risk-based initial margin models," LSE Research Online Documents on Economics 118281, London School of Economics and Political Science, LSE Library.

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