IDEAS home Printed from https://ideas.repec.org/a/rjr/romjef/vy2014i4p49-64.html
   My bibliography  Save this article

Simultaneity of Tail Events for Dynamic Conditional Distributions of Stock Market Index Returns

Author

Listed:
  • Radu Lupu

    (Bucharest University of Economic Studies, Institute for Economic Forecasting, Romanian Academy)

Abstract

The tail events represent a phenomenon long studied in the literature of stock market returns. The dynamical properties of conditional distributions are currently analyzed by means of the first four moments via Gram-Charlier likelihood functions. We propose an analysis of changes in the values of means, volatilities, skewness and kurtosis coefficients for a series of intra-daily frequency of 14 stock market returns to develop a jump detection mechanism based on the estimation of a dynamic threshold that relies on the first four moments of the distribution. Our main objective consists in the estimation of simultaneity of tail values for these moments. We consider the 5% up and 5% down event as jumps in the series of these coefficients and we compare their realizations across the series of different stock markets for simultaneity. Finally we propose an indicator that can show the degree of co-movements in the extreme values of these coefficients for different frequencies.

Suggested Citation

  • Radu Lupu, 2014. "Simultaneity of Tail Events for Dynamic Conditional Distributions of Stock Market Index Returns," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 49-64, December.
    • Handle: RePEc:rjr:romjef:v::y:2014:i:4:p:49-64
    as

    Download full text from publisher

    File URL: http://www.ipe.ro/rjef/rjef4_14/rjef4_2014p49-64.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Knif, Johan & Pynnonen, Seppo, 1999. "Local and global price memory of international stock markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 9(2), pages 129-147, April.
    2. Andrew Ang & Geert Bekaert, 2002. "International Asset Allocation With Regime Shifts," The Review of Financial Studies, Society for Financial Studies, vol. 15(4), pages 1137-1187.
    3. Alexandros Gabrielsen & Axel Kirchner & Zhuoshi Liu & Paolo Zagaglia, 2015. "Forecasting Value-At-Risk With Time-Varying Variance, Skewness And Kurtosis In An Exponential Weighted Moving Average Framework," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 10(01), pages 1-29.
    4. Lucian Liviu Albu & Radu Lupu & Cantemir Adrian Călin & Oana Cristina Popovici, 2014. "Estimating the Impact of Quantitative Easing On Credit Risk through an ARMA-GARCH Model," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 39-50, October.
    5. Chelley-Steeley, Patricia L., 2005. "Modeling equity market integration using smooth transition analysis: A study of Eastern European stock markets," Journal of International Money and Finance, Elsevier, vol. 24(5), pages 818-831, September.
    6. Chernov, Mikhail & Ronald Gallant, A. & Ghysels, Eric & Tauchen, George, 2003. "Alternative models for stock price dynamics," Journal of Econometrics, Elsevier, vol. 116(1-2), pages 225-257.
    7. Francois Chesnay & Eric Jondeau, 2001. "Does Correlation Between Stock Returns Really Increase During Turbulent Periods?," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 30(1), pages 53-80, February.
    8. repec:bla:jfinan:v:59:y:2004:i:2:p:755-793 is not listed on IDEAS
    9. Baele, Lieven, 2005. "Volatility Spillover Effects in European Equity Markets," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 40(2), pages 373-401, June.
    10. Christoffersen, Peter, 2011. "Elements of Financial Risk Management," Elsevier Monographs, Elsevier, edition 2, number 9780123744487.
    11. King, Mervyn A & Wadhwani, Sushil, 1990. "Transmission of Volatility between Stock Markets," The Review of Financial Studies, Society for Financial Studies, vol. 3(1), pages 5-33.
    12. Torben G. Andersen & Luca Benzoni & Jesper Lund, 2002. "An Empirical Investigation of Continuous‐Time Equity Return Models," Journal of Finance, American Finance Association, vol. 57(3), pages 1239-1284, June.
    13. William N. Goetzmann & Lingfeng Li & K. Geert Rouwenhorst, 2005. "Long-Term Global Market Correlations," The Journal of Business, University of Chicago Press, vol. 78(1), pages 1-38, January.
    14. Philippe Jorion, 1988. "On Jump Processes in the Foreign Exchange and Stock Markets," The Review of Financial Studies, Society for Financial Studies, vol. 1(4), pages 427-445.
    15. Bjørn Eraker & Michael Johannes & Nicholas Polson, 2003. "The Impact of Jumps in Volatility and Returns," Journal of Finance, American Finance Association, vol. 58(3), pages 1269-1300, June.
    16. François Longin & Bruno Solnik, 2001. "Extreme Correlation of International Equity Markets," Journal of Finance, American Finance Association, vol. 56(2), pages 649-676, April.
    17. Stijn Claessens & Daniela Klingebiel & Sergio L. Schmukler, 2002. "The Future of Stock Exchanges in Emerging Economies: Evolution and Prosepcts," Center for Financial Institutions Working Papers 02-03, Wharton School Center for Financial Institutions, University of Pennsylvania.
    18. Chib, Siddhartha & Nardari, Federico & Shephard, Neil, 2002. "Markov chain Monte Carlo methods for stochastic volatility models," Journal of Econometrics, Elsevier, vol. 108(2), pages 281-316, June.
    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. LUPU, Radu & MATEESCU, Alexandra, 2016. "Systemic Risk And Cojumps In High Frequency Data," Studii Financiare (Financial Studies), Centre of Financial and Monetary Research "Victor Slavescu", vol. 20(4), pages 6-16.

    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. Aityan, Sergey K. & Ivanov-Schitz, Alexey K. & Izotov, Sergey S., 2010. "Time-shift asymmetric correlation analysis of global stock markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 20(5), pages 590-605, December.
    2. Yeh, Jin-Huei & Yun, Mu-Shu, 2023. "Assessing jump and cojumps in financial asset returns with applications in futures markets," Pacific-Basin Finance Journal, Elsevier, vol. 82(C).
    3. Aslanidis, Nektarios & Dungey, Mardi & Savva, Christos S., 2008. "Progress Towards to Equity Market Integration in Eastern Europe," Working Papers 2072/13265, Universitat Rovira i Virgili, Department of Economics.
    4. Dalkir, Mehmet, 2009. "Revisiting stock market index correlations," Finance Research Letters, Elsevier, vol. 6(1), pages 23-33, March.
    5. Christos Savva & Nektarios Aslanidis, 2010. "Stock market integration between new EU member states and the Euro-zone," Empirical Economics, Springer, vol. 39(2), pages 337-351, October.
    6. John M Maheu & Thomas H McCurdy, 2007. "Modeling foreign exchange rates with jumps," Working Papers tecipa-279, University of Toronto, Department of Economics.
    7. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2005. "Volatility Forecasting," PIER Working Paper Archive 05-011, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    8. Kundu, Srikanta & Sarkar, Nityananda, 2016. "Return and volatility interdependences in up and down markets across developed and emerging countries," Research in International Business and Finance, Elsevier, vol. 36(C), pages 297-311.
    9. Rajan Sruthi & Santhakumar Shijin, 2020. "Investigating liquidity constraints as a channel of contagion: a regime switching approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-21, December.
    10. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2006. "Volatility and Correlation Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 15, pages 777-878, Elsevier.
    11. Christensen, Kim & Oomen, Roel C.A. & Podolskij, Mark, 2014. "Fact or friction: Jumps at ultra high frequency," Journal of Financial Economics, Elsevier, vol. 114(3), pages 576-599.
    12. L. Bauwens & E. Otranto, 2013. "Modeling the Dependence of Conditional Correlations on Volatility," Working Paper CRENoS 201304, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    13. Chernov, Mikhail & Graveline, Jeremy & Zviadadze, Irina, 2018. "Crash Risk in Currency Returns," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 53(1), pages 137-170, February.
    14. Nektarios Aslanidis & Denise R. Osborn & Marianne Sensier, 2008. "Co-movements between US and UK stock prices: the roles of macroeconomic information and time-varying conditional correlations," Centre for Growth and Business Cycle Research Discussion Paper Series 96, Economics, The University of Manchester.
    15. Rodriguez, Juan Carlos, 2007. "Measuring financial contagion: A Copula approach," Journal of Empirical Finance, Elsevier, vol. 14(3), pages 401-423, June.
    16. Jouchi Nakajima, 2008. "EGARCH and Stochastic Volatility: Modeling Jumps and Heavy-tails for Stock Returns," IMES Discussion Paper Series 08-E-23, Institute for Monetary and Economic Studies, Bank of Japan.
    17. Bandi, F.M. & Renò, R., 2016. "Price and volatility co-jumps," Journal of Financial Economics, Elsevier, vol. 119(1), pages 107-146.
    18. Tiwari, Aviral Kumar & Mutascu, Mihai Ioan & Albulescu, Claudiu Tiberiu, 2016. "Continuous wavelet transform and rolling correlation of European stock markets," International Review of Economics & Finance, Elsevier, vol. 42(C), pages 237-256.
    19. Hyde, Stuart J & Bredin, Don P & Nguyen, Nghia, 2007. "Correlation dynamics between Asia-Pacific, EU and US stock returns," MPRA Paper 9681, University Library of Munich, Germany.

    More about this item

    Keywords

    simultaneity indicator; dynamic threshold for jump detection; dynamic skewness and kurtosis; Gram-Charlier likelihood; stock market comovements; extreme events;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    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:rjr:romjef:v::y:2014:i:4:p:49-64. 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: Corina Saman (email available below). General contact details of provider: https://edirc.repec.org/data/ipacaro.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.