IDEAS home Printed from https://ideas.repec.org/a/ime/imemes/v18y2000i2p25-48.html
   My bibliography  Save this article

Forecasting Extreme Financial Risk: A Critical Analysis of Practical Methods for the Japanese Market

Author

Listed:
  • Danielsson, Jon

    (London School of Econ)

  • Morimoto, Yuji

    (Morgan Stanley Dean Witter)

Abstract

The various tools for risk measurement and management, especially for value-at-risk (VaR), are compared, with special emphasis on Japanese market data. Traditional Generalized Autoregressive Conditional Heteroskedasticity (GARCH-type methods are compared to extreme value theory (EVT). The distribution of extremes, asymmetry, clustering, and the dynamic structure of VaR all count as criteria for comparison of the various methods. We find that the GARCH class of models is not suitable for VaR forecasting for the sample data, due to both the inaccuracy and the high volatility of the VaR forecasts. In contrast, EVT forecasting of VaR resulted in much better VaR estimates, and more importantly, the EVT forecasts were considerably more stable, enhancing their practical applicability for Japanese market risk forecasts.

Suggested Citation

  • Danielsson, Jon & Morimoto, Yuji, 2000. "Forecasting Extreme Financial Risk: A Critical Analysis of Practical Methods for the Japanese Market," Monetary and Economic Studies, Institute for Monetary and Economic Studies, Bank of Japan, vol. 18(2), pages 25-48, December.
  • Handle: RePEc:ime:imemes:v:18:y:2000:i:2:p:25-48
    as

    Download full text from publisher

    File URL: http://www.imes.boj.or.jp/research/papers/english/me18-2-2.pdf
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sofiane Aboura, 2014. "When the U.S. Stock Market Becomes Extreme?," Risks, MDPI, vol. 2(2), pages 1-15, May.
    2. Dolores Furió & Francisco J. Climent, 2013. "Extreme value theory versus traditional GARCH approaches applied to financial data: a comparative evaluation," Quantitative Finance, Taylor & Francis Journals, vol. 13(1), pages 45-63, January.
    3. Małgorzata Just & Krzysztof Echaust, 2021. "An Optimal Tail Selection in Risk Measurement," Risks, MDPI, vol. 9(4), pages 1-16, April.
    4. Luca Erzegovesi, 2002. "VaR and Liquidity Risk.Impact on Market Behaviour and Measurement Issues," Alea Tech Reports 014, Department of Computer and Management Sciences, University of Trento, Italy, revised 14 Jun 2008.
    5. Ramzi Nekhili & Jahangir Sultan, 2020. "Jump Driven Risk Model Performance in Cryptocurrency Market," IJFS, MDPI, vol. 8(2), pages 1-18, April.
    6. Gencay, Ramazan & Selcuk, Faruk & Ulugulyagci, Abdurrahman, 2003. "High volatility, thick tails and extreme value theory in value-at-risk estimation," Insurance: Mathematics and Economics, Elsevier, vol. 33(2), pages 337-356, October.
    7. 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.
    8. Danielsson, Jon & James, Kevin R. & Valenzuela, Marcela & Zer, Ilknur, 2016. "Model risk of risk models," Journal of Financial Stability, Elsevier, vol. 23(C), pages 79-91.
    9. Nishiyama, N., 2001. "One idea of portfolio risk control for absolute return strategy risk adjustments by signals from correlation behavior," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 301(1), pages 457-472.
    10. 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.
    11. Sang Hoon Kang & Seong-Min Yoon, 2009. "Value-at-Risk Analysis for Asian Emerging Markets: Asymmetry and Fat Tails in Returns Innovation," Korean Economic Review, Korean Economic Association, vol. 25, pages 387-411.
    12. Araújo Santos, P. & Fraga Alves, M.I., 2012. "A new class of independence tests for interval forecasts evaluation," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3366-3380.
    13. Huang, Dashan & Yu, Baimin & Fabozzi, Frank J. & Fukushima, Masao, 2009. "CAViaR-based forecast for oil price risk," Energy Economics, Elsevier, vol. 31(4), pages 511-518, July.
    14. Wagner, Niklas, 2005. "Autoregressive conditional tail behavior and results on Government bond yield spreads," International Review of Financial Analysis, Elsevier, vol. 14(2), pages 247-261.
    15. Degiannakis, Stavros & Floros, Christos & Dent, Pamela, 2013. "Forecasting value-at-risk and expected shortfall using fractionally integrated models of conditional volatility: International evidence," International Review of Financial Analysis, Elsevier, vol. 27(C), pages 21-33.
    16. Szymon Lis & Marcin Chlebus, 2021. "Comparison of the accuracy in VaR forecasting for commodities using different methods of combining forecasts," Working Papers 2021-11, Faculty of Economic Sciences, University of Warsaw.
    17. PICIU, Gabriela Cornelia, 2013. "Internal Rating – An Active Instrument In The Management Of Banking Risks. Case Study Bcr," Studii Financiare (Financial Studies), Centre of Financial and Monetary Research "Victor Slavescu", vol. 17(2), pages 21-30.
    18. Salhi, Khaled & Deaconu, Madalina & Lejay, Antoine & Champagnat, Nicolas & Navet, Nicolas, 2016. "Regime switching model for financial data: Empirical risk analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 148-157.
    19. Raymond Knott & Marco Polenghi, 2006. "Assessing central counterparty margin coverage on futures contracts using GARCH models," Bank of England working papers 287, Bank of England.
    20. Bekiros, Stelios D. & Georgoutsos, Dimitris A., 2005. "Estimation of Value-at-Risk by extreme value and conventional methods: a comparative evaluation of their predictive performance," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 15(3), pages 209-228, July.
    21. Lee, Tae-Hwy & Saltoglu, Burak, 2002. "Assessing the risk forecasts for Japanese stock market," Japan and the World Economy, Elsevier, vol. 14(1), pages 63-85, January.

    More about this item

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G20 - Financial Economics - - Financial Institutions and Services - - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ime:imemes:v:18:y:2000:i:2:p:25-48. See general information about how to correct material in RePEc.

    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.

    We have no bibliographic references for this item. You can help adding them by using 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 RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Kinken (email available below). General contact details of provider: https://edirc.repec.org/data/imegvjp.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.