IDEAS home Printed from https://ideas.repec.org/p/war/wpaper/2016-01.html
   My bibliography  Save this paper

One-Day Prediction of State of Turbulence for Portfolio. Models for Binary Dependent Variable

Author

Listed:
  • Marcin Chlebus

    (Faculty of Economic Sciences, University of Warsaw)

Abstract

This paper proposes an approach to predict states (states of tranquillity and turbulence) for a current portfolio in a one-day horizon. The prediction is made using 3 different models for a binary variable (LOGIT, PROBIT, CLOGLOG), 4 definitions of a dependent variable (1%, 5%, 10%, 20% of worst realization of returns), 3 sets of independent variables (untransformed data, PCA analysis and factor analysis). Additionally an optimal cut-off point analysis is performed. The evaluation of the models was based on the LR test, Hosmer-Lemeshow test, GINI coefficient analysis and KROC criterion based on the ROC curve. Six combinations of assumptions have been chosen as appropriate (any model for a binary variable, the dependent variable defined as 5% or 10% of worst realization of returns, untransformed data, 5% or 10% cut-off point respectively). Models built on these assumptions meet all the formal requirements and have a high predictive and discriminant ability.

Suggested Citation

  • Marcin Chlebus, 2016. "One-Day Prediction of State of Turbulence for Portfolio. Models for Binary Dependent Variable," Working Papers 2016-01, Faculty of Economic Sciences, University of Warsaw.
    • Handle: RePEc:war:wpaper:2016-01
    as

    Download full text from publisher

    File URL: http://www.wne.uw.edu.pl/index.php/download_file/2295/
    File Function: First version, 2016
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tuomas Komulainen & ) & Johanna Lukkarila, 2003. "What drives financial crises in emerging markets?," Macroeconomics 0304010, University Library of Munich, Germany.
    2. repec:zbw:bofitp:2003_005 is not listed on IDEAS
    3. Carmen M. Reinhart & Graciela L. Kaminsky, 1999. "The Twin Crises: The Causes of Banking and Balance-of-Payments Problems," American Economic Review, American Economic Association, vol. 89(3), pages 473-500, June.
    4. Beckmann, Daniela & Menkhoff, Lukas & Sawischlewski, Katja, 2006. "Robust lessons about practical early warning systems," Journal of Policy Modeling, Elsevier, vol. 28(2), pages 163-193, February.
    5. Frankel, Jeffrey A. & Rose, Andrew K., 1996. "Currency Crashes in Emerging Markets: Empirical Indicators," Center for International and Development Economics Research (CIDER) Working Papers 233424, University of California-Berkeley, Department of Economics.
    6. Andrew Berg & Eduardo Borensztein & Catherine Pattillo, 2005. "Assessing Early Warning Systems: How Have They Worked in Practice?," IMF Staff Papers, Palgrave Macmillan, vol. 52(3), pages 1-5.
    7. Carmen M. Reinhart & Graciela L. Kaminsky, 1999. "The Twin Crises: The Causes of Banking and Balance-of-Payments Problems," American Economic Review, American Economic Association, vol. 89(3), pages 473-500, June.
    8. Kamin, Steven B., 1999. "The current international financial crisis:: how much is new?," Journal of International Money and Finance, Elsevier, vol. 18(4), pages 501-514, August.
    9. Burkart, O. & Coudert, V., 2000. "Leading Indicators of Currency Crises in Emerging Economies," Working papers 74, Banque de France.
    10. King, Gary & Zeng, Langche, 2001. "Logistic Regression in Rare Events Data," Political Analysis, Cambridge University Press, vol. 9(2), pages 137-163, January.
    11. Frankel, Jeffrey A. & Rose, Andrew K., 1996. "Currency crashes in emerging markets: An empirical treatment," Journal of International Economics, Elsevier, vol. 41(3-4), pages 351-366, November.
    12. Barrell, Ray & Davis, E. Philip & Karim, Dilruba & Liadze, Iana, 2010. "Bank regulation, property prices and early warning systems for banking crises in OECD countries," Journal of Banking & Finance, Elsevier, vol. 34(9), pages 2255-2264, September.
    13. George Soros, 1999. "The International Financial Crisis," Challenge, Taylor & Francis Journals, vol. 42(2), pages 58-76, March.
    14. Bussiere, Matthieu & Fratzscher, Marcel, 2008. "Low probability, high impact: Policy making and extreme events," Journal of Policy Modeling, Elsevier, vol. 30(1), pages 111-121.
    15. Engle, Robert F., 1984. "Wald, likelihood ratio, and Lagrange multiplier tests in econometrics," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 13, pages 775-826, Elsevier.
    16. Demirguc, Asli & Detragiache, Enrica, 2000. "Monitoring Banking Sector Fragility: A Multivariate Logit Approach," The World Bank Economic Review, World Bank, vol. 14(2), pages 287-307, May.
    17. Komulainen, Tuomas & Lukkarila, Johanna, 2003. "What drives financial crises in emerging markets?," Emerging Markets Review, Elsevier, vol. 4(3), pages 248-272, September.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. 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.
    2. Marcin Chlebus, 2016. "Can Lognormal, Weibull or Gamma Distributions Improve the EWS-GARCH Value-at-Risk Forecasts?," FindEcon Chapters: Forecasting Financial Markets and Economic Decision-Making, in: Magdalena Osińska (ed.), Statistical Review, vol. 63, 2016, 3, edition 1, volume 63, chapter 4, pages 329-350, University of Lodz.

    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. Ari, Ali, 2012. "Early warning systems for currency crises: The Turkish case," Economic Systems, Elsevier, vol. 36(3), pages 391-410.
    2. Sarlin, Peter & von Schweinitz, Gregor, 2021. "Optimizing Policymakers’ Loss Functions In Crisis Prediction: Before, Within Or After?," Macroeconomic Dynamics, Cambridge University Press, vol. 25(1), pages 100-123, January.
    3. Ari, Ali, 2008. "An Early Warning Signals Approach for Currency Crises: The Turkish Case," MPRA Paper 25858, University Library of Munich, Germany, revised 2009.
    4. Nakatani, Ryota, 2018. "Real and financial shocks, exchange rate regimes and the probability of a currency crisis," Journal of Policy Modeling, Elsevier, vol. 40(1), pages 60-73.
    5. Lo Duca, Marco & Koban, Anne & Basten, Marisa & Bengtsson, Elias & Klaus, Benjamin & Kusmierczyk, Piotr & Lang, Jan Hannes & Detken, Carsten & Peltonen, Tuomas, 2017. "A new database for financial crises in European countries," ESRB Occasional Paper Series 13, European Systemic Risk Board.
    6. Karatas, B., 2014. "Financial crisis and monetary policy," Other publications TiSEM 41e463f0-e122-4379-8db5-6, Tilburg University, School of Economics and Management.
    7. Ali Ari & Raif Cergibozan, 2016. "A Comparison of Currency Crisis Dating Methods: Turkey 1990-2014," Montenegrin Journal of Economics, Economic Laboratory for Transition Research (ELIT), vol. 12(3), pages 19-37.
    8. Eijffinger, Sylvester C.W. & Karataş, Bilge, 2020. "Together or apart? The relationship between currency and banking crises," Journal of Banking & Finance, Elsevier, vol. 119(C).
    9. Bussiere, Matthieu & Fratzscher, Marcel, 2006. "Towards a new early warning system of financial crises," Journal of International Money and Finance, Elsevier, vol. 25(6), pages 953-973, October.
    10. Markus Behn & Carsten Detken & Tuomas Peltonen & Willem Schudel, 2017. "Predicting Vulnerabilities in the EU Banking Sector: The Role of Global and Domestic Factors," International Journal of Central Banking, International Journal of Central Banking, vol. 13(4), pages 147-189, December.
    11. Marcin Chlebus, 2014. "One-day prediction of state of turbulence for financial instrument based on models for binary dependent variable," Ekonomia journal, Faculty of Economic Sciences, University of Warsaw, vol. 37.
    12. Matthieu Bussière, 2013. "Balance of payment crises in emerging markets: how early were the ‘early’ warning signals?," Applied Economics, Taylor & Francis Journals, vol. 45(12), pages 1601-1623, April.
    13. Andre Cartapanis, 2004. "Le declenchement des crises de change : qu'avons-nous appris depuis dix ans ?," Economie Internationale, CEPII research center, issue 97, pages 5-48.
    14. von Schweinitz, Gregor & Sarlin, Peter, 2015. "Signaling Crises: How to Get Good Out-of-Sample Performance Out of the Early Warning System," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112964, Verein für Socialpolitik / German Economic Association.
    15. Balaga Mohana Rao & Puja Padhi, 2020. "Common Determinants of the Likelihood of Currency Crises in BRICS," Global Business Review, International Management Institute, vol. 21(3), pages 698-712, June.
    16. Schudel, Willem, 2015. "Shifting horizons: assessing macro trends before, during, and following systemic banking crises," Working Paper Series 1766, European Central Bank.
    17. Yanping Zhao & Jakob Haan & Bert Scholtens & Haizhen Yang, 2014. "Leading Indicators of Currency Crises: Are They the Same in Different Exchange Rate Regimes?," Open Economies Review, Springer, vol. 25(5), pages 937-957, November.
    18. Stanislav Percic & Constantin-Marius Apostoaie & Vasile Cocris, 2013. "Early Warning Systems For Financial Crises.A Critical Approach," CES Working Papers, Centre for European Studies, Alexandru Ioan Cuza University, vol. 5(1), pages 77-88.
    19. Amaral, Andrea & Abreu, Margarida & Mendes, Victor, 2014. "The spatial Probit model—An application to the study of banking crises at the end of the 1990’s," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 415(C), pages 251-260.
    20. Ryota Nakatani, 2017. "The Effects of Productivity Shocks, Financial Shocks, and Monetary Policy on Exchange Rates: An Application of the Currency Crisis Model and Implications for Emerging Market Crises," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 53(11), pages 2545-2561, November.

    More about this item

    Keywords

    prediction; state of turbulence; regime switching; risk management; risk measure; market risk;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:war:wpaper:2016-01. 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: Marcin Bąba (email available below). General contact details of provider: https://edirc.repec.org/data/fesuwpl.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.