Skip to main content
SpringerLink
Log in

The Understanding of Dependent Structure and Co-movement of World Stock Exchanges Under the Economic Cycle

  • Conference paper
  • First Online:
Predictive Econometrics and Big Data (TES 2018)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 753))

Included in the following conference series:

  • 2113 Accesses

Abstract

This study was to focus on the patterns of economic booms (bull markets) and recessions (bear markets) among world stock exchanges such as Europe (Euro Stoxx), USA (S&P 500), Asia (SSE composite index and Nikkei 225 index) and ASEAN (FTSE ASEAN). Monthly data was collected during 2000 to 2016. Econometrically, we employed Markov Switching Bayesian Vector Autoregressive model (MSBVAR) to determine regional switches within these financial data sets as well as CD-Vine copula approaches was used to explore the contagions and patterns of structural dependences. To clarify the connectional details in each type of switching regimes, the results presented the Elliptical copula was chosen and it indicated these monthly collected data contained symmetrical dynamics co-movements. In addition, it implied the stock markets were assumed to have small fluctuations since the governments had stable policies to control the risk and asymmetric information in financial markets efficiently. Base on CD-Vine copula trees, the results indicated Asia and European stock markets had a strongly dependence in economic booms and recessions during the pre-crisis period (2000 to 2008). Conversely, in the post-crisis period, the US stock market and ASEAN stock market became the strong dependence with Europe. This meant that capital flows was mostly transferred between Europe and Asia financial markets during the pre-crisis periods (2009 to 2016). After that, the direction of capital flows were changed dramatically to the US stock market in the post-crisis periods. Predictively, this seems that the capital flows will return to European and US financial market, which these two continents have a strongly long-term financial dependence and deeply positive diplomacy.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Avdulai, K.: The Extreme Value Theory as a Tool to Measure Market Risk. Working paper 26/2011.IES FSV. Charles University (2011). http://ies.fsv.cuni.cz

  2. Behrens, C.N., Lopes, H.F., Gamerman, D.: Bayesian analysis of extreme events with threshold estimation. Stat. Model. 4, 227–244 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  3. Chaithep, K., Sriboonchitta, S., Chaiboonsri, C., Pastpipatkul, P.: Value at risk analysis of gold price return using extreme value theory. EEQEL 1(4), 151–168 (2012)

    Google Scholar 

  4. Christoffersen, P.: Evaluating interval forecasts. Int. Econ. Rev. 39, 841–862 (1998)

    Article  MathSciNet  Google Scholar 

  5. Einmahl, J.H.J., Magnus, J.R.: Records in athletics through extreme-value theory. J. Am. Stat. Assoc. 103, 1382–1391 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  6. Embrechts, T., Resnick, S.T., Samorodnitsky, G.: Extreme value theory as a risk management tool. North Am. Actuar. J. 3(2), 30–41 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  7. Ernst, E., Stockhammer, E.: Macroeconomic Regimes: Business Cycle Theories Reconsidered. Working paper No. 99. Center for Empirical Macroeconomics, Department of Economics, University of Bielefeld (2003). http://www.wiwi.uni-bielefeld.de

  8. Garrido, M.C., Lezaud, P.: Extreme value analysis : an introduction. Journal de la Socit Franaise de Statistique, 66–97 (2013). https://hal-enac.archives-ouvertes.fr/hal-00917995

  9. Hamilton, J.D.: Regime Switching Models. Palgrave Dictionary of Economics (2005)

    Google Scholar 

  10. Jang, J.B.: An extreme value theory approach for analyzing the extreme risk of the gold prices, 97–109 (2007)

    Google Scholar 

  11. King, R.G., Rebelo, S.T.: Resuscitating Real Business Cycles. Working paper 7534, National Bureau of Economic Research (2000). http://www.nber.org/papers/w7534

  12. Kisacik, A.: High volatility, heavy tails and extreme values in value at risk estimation. Institute of Applied Mathematics Financial Mathematics/Life Insurance Option Program Middle East Technical University, Term Project (2006)

    Google Scholar 

  13. Kupiec, P.: Techniques for verifying the accuracy of risk management models. J. Deriv. 3, 73–84 (1995)

    Article  Google Scholar 

  14. Manganelli, S., Engle, F.R.: Value at Risk Model in Finance. Working paper No.75. European Central Bank (2001)

    Google Scholar 

  15. Marimoutou, V., Raggad, B., Trabelsi, A.: Extreme value theory and value at risk: application to oil market (2006). https://halshs.archives-ouvertes.fr/halshs-00410746

  16. Mierlus-Mazilu, I.: On generalized Pareto distribution. Rom. J. Econ. Forecast. 1, 107–117 (2010)

    Google Scholar 

  17. Mwamba, J.W.M., Hammoudeh, S., Gupta, R.: Financial Tail Risks and the Shapes of the Extreme Value Distribution: A Comparison between Conventional and Sharia-Compliant Stock Indexes. Working paper No. 80. Department of Economics Working Paper Series, University of Pretoria (2014)

    Google Scholar 

  18. Neves, C., Alves, M.I.F.: Testing extreme value conditions an overview and recent approaches. Stat. J. REVSTAT 6, 83–100 (2008)

    MathSciNet  MATH  Google Scholar 

  19. Perez, P.G., Murphy, D.: Filtered Historical Simulation Value-at-Risk Models and Their Competitors. Working paper No. 525. Bank of England (2015). http://www.bankofengland.co.uk/research/Pages/workingpapers/default.aspx

  20. Perlin, M.: MS Regress - The MATLAB Package for Markov Regime Switching Models (2010). Available at SSRN: http://ssrn.com/abstract=1714016

  21. Pickands, J.: Statistical inference using extreme order statistics. Ann. Stat. 3, 110–131 (1975)

    MathSciNet  MATH  Google Scholar 

  22. Rockafellar, R.T., Uryasev, S.: Conditional value-at-risk for general loss distributions. J. Bank. Financ. 26, 1443–1471 (2002). http://www.elsevier.com/locate/econbase

    Article  Google Scholar 

  23. Sampara, J.B., Guillen, M., Santolino, M.: Beyond Value-at-Risk: Glue VaR Distortion Risk Measures. Working paper No. 2. Research Institute of Applied Economics, Department of Econometrics, Riskcenter - IREA University of Barcelona (2013)

    Google Scholar 

  24. Taghipour, A.: Banks, stock market and economic growth: the case of Iran. J. Iran. Econ. Rev. 14(23), 19–40 (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Economics, Chiang Mai University, Chiang Mai, Thailand

    Songsak Sriboonchitta, Chukiat Chaiboonsri & Jittima Singvejsakul

Authors
  1. Songsak Sriboonchitta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chukiat Chaiboonsri
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jittima Singvejsakul
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chukiat Chaiboonsri .

Editor information

Editors and Affiliations

  1. Computer Science Department, University of Texas at El Paso, El Paso, Texas, USA

    Vladik Kreinovich

  2. International College, Chiang Mai University, Chiang Mai, Thailand

    Songsak Sriboonchitta

  3. International College, Chiang Mai University, Chiang Mai, Thailand

    Nopasit Chakpitak

Appendix A

Appendix A

See (Tables 4, 5, 6 and 7).

Table 4. C-vine copula testing in bull markets during pre-crisis and post-crisis periods
Full size table
Table 5. C-vine copula testing in bear markets during pre-crisis and post-crisis periods
Full size table
Table 6. D-vine copula testing in bull markets during pre-crisis and post-crisis periods
Full size table
Table 7. D-vine copula testing in bear markets during pre-crisis and post-crisis periods
Full size table

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sriboonchitta, S., Chaiboonsri, C., Singvejsakul, J. (2018). The Understanding of Dependent Structure and Co-movement of World Stock Exchanges Under the Economic Cycle. In: Kreinovich, V., Sriboonchitta, S., Chakpitak, N. (eds) Predictive Econometrics and Big Data. TES 2018. Studies in Computational Intelligence, vol 753. Springer, Cham. https://doi.org/10.1007/978-3-319-70942-0_41

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-70942-0_41

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70941-3

  • Online ISBN: 978-3-319-70942-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation