IDEAS home Printed from https://ideas.repec.org/a/eaa/aeinde/v8y2008i1_4.html
   My bibliography  Save this article

Forecasting Market Crashes: Does Density Specification Matter?

Author

Listed:
  • BRIO, Esther B.
  • PEROTE, Javier

Abstract

The current research examines the capacity of the Edgeworth-Sargan density on forecasting market crashes. Focusing on the 1987 stock market crash the performance of this distribution is compared to the Student’s t concluding that the latter overestimates the risk. In contrast, and due to its flexible parametric structure, the Edgeworth-Sargan density is capable of more accurately forecasting the risk of highly volatile scenarios, especially when intraday data is available. We use daily data from the FTSE and Dow Jones indices (continuously compounded returns).

Suggested Citation

  • BRIO, Esther B. & PEROTE, Javier, 2008. "Forecasting Market Crashes: Does Density Specification Matter?," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 8(1), pages 53-58.
  • Handle: RePEc:eaa:aeinde:v:8:y:2008:i:1_4
    as

    Download full text from publisher

    File URL: http://www.usc.es/economet/reviews/aeid814.pdf
    Download Restriction: No.
    ---><---

    References listed on IDEAS

    as
    1. Nabeel Al-Loughani & David Chappell, 1997. "On the validity of the weak-form efficient markets hypothesis applied to the London stock exchange," Applied Financial Economics, Taylor & Francis Journals, vol. 7(2), pages 173-176.
    2. Orazio P. Attanasio, 1991. "Risk, Time-Varying Second Moments and Market Efficiency," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(3), pages 479-494.
    3. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    4. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
    5. Bollerslev, Tim, 1987. "A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return," The Review of Economics and Statistics, MIT Press, vol. 69(3), pages 542-547, August.
    6. Balvers, Ronald J & Cosimano, Thomas F & McDonald, Bill, 1990. "Predicting Stock Returns in an Efficient Market," Journal of Finance, American Finance Association, vol. 45(4), pages 1109-1128, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. ASGHAR, Zahid, 2008. "Energy–Gdp Relationship: A Causal Analysis For The Five Countries Of South Asia," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 8(1), pages 167-180.
    2. Tim Bollerslev, 2008. "Glossary to ARCH (GARCH)," CREATES Research Papers 2008-49, Department of Economics and Business Economics, Aarhus University.
    3. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654.
    4. Bollerslev, Tim & Engle, Robert F. & Nelson, Daniel B., 1986. "Arch models," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 49, pages 2959-3038, Elsevier.
    5. Koutmos, Gregory, 1997. "Feedback trading and the autocorrelation pattern of stock returns: further empirical evidence," Journal of International Money and Finance, Elsevier, vol. 16(4), pages 625-636, August.
    6. Dongweí Su, 2003. "Risk, Return and Regulation in Chinese Stock Markets," World Scientific Book Chapters, in: Chinese Stock Markets A Research Handbook, chapter 3, pages 75-122, World Scientific Publishing Co. Pte. Ltd..
    7. Eleni Constantinou & Robert Georgiades & Avo Kazandjian & George Kouretas, 2005. "Mean and variance causality between the Cyprus Stock Exchange and major equity markets," Working Papers 0501, University of Crete, Department of Economics.
    8. Brooks, Robert D. & Davidson, Sinclair & Faff, Robert W., 1997. "An examination of the effects of major political change on stock market volatility: the South African experience," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 7(3), pages 255-275, October.
    9. Sébastien Laurent & Luc Bauwens & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109.
    10. Michael Pitt & Sheheryar Malik & Arnaud Doucet, 2014. "Simulated likelihood inference for stochastic volatility models using continuous particle filtering," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(3), pages 527-552, June.
    11. Hu, Michael Y. & Jiang, Christine X. & Tsoukalas, Christos, 1997. "The European exchange rates before and after the establishment of the European Monetary System," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 7(3), pages 235-253, October.
    12. De Santis, Giorgio & imrohoroglu, Selahattin, 1997. "Stock returns and volatility in emerging financial markets," Journal of International Money and Finance, Elsevier, vol. 16(4), pages 561-579, August.
    13. Pandey, Ajay, 2003. "Modeling and Forecasting Volatility in Indian Capital Markets," IIMA Working Papers WP2003-08-03, Indian Institute of Management Ahmedabad, Research and Publication Department.
    14. Angelidis, Timotheos & Benos, Alexandros & Degiannakis, Stavros, 2004. "The Use of GARCH Models in VaR Estimation," MPRA Paper 96332, University Library of Munich, Germany.
    15. Issler, João Victor, 1999. "Estimating and forecasting the volatility of Brazilian finance series using arch models (Preliminary Version)," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 347, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    16. Turan Bali & Panayiotis Theodossiou, 2007. "A conditional-SGT-VaR approach with alternative GARCH models," Annals of Operations Research, Springer, vol. 151(1), pages 241-267, April.
    17. Lütkepohl,Helmut & Krätzig,Markus (ed.), 2004. "Applied Time Series Econometrics," Cambridge Books, Cambridge University Press, number 9780521547871.
    18. Jorge Caiado, 2004. "Modelling And Forecasting The Volatility Of The Portuguese Stock Index Psi-20," Portuguese Journal of Management Studies, ISEG, Universidade de Lisboa, vol. 9(1), pages 3-21.
    19. Antonakakis, Nikolaos & Darby, Julia, 2012. "Forecasting Volatility in Developing Countries' Nominal Exchange Returns," MPRA Paper 40875, University Library of Munich, Germany.
    20. Ntebogang Dinah Moroke, 2015. "An Optimal Generalized Autoregressive Conditional Heteroscedasticity Model for Forecasting the South African Inflation Volatility," Journal of Economics and Behavioral Studies, AMH International, vol. 7(4), pages 134-149.

    More about this item

    Keywords

    Confidence intervals; Edgeworth-Sargan; Student’s t;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    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:eaa:aeinde:v:8:y:2008:i:1_4. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc 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 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: M. Carmen Guisan (email available below). General contact details of provider: http://www.usc.es/economet/eaa.htm .

    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.