European Financial and Accounting Journal 2016, 11(3):125-137 | DOI: 10.18267/j.efaj.167

Structural Distress Index: Structural Break Analysis of the Czech and Polish Stock Markets

Michael Princ
Michael Princ; Institute of Economics Studies, Charles University, Department of Macroeconomics and Econometrics, Opletalova 26, 110 00 Prague, Czech Republic, <mp.princ@seznam.cz>.

The estimation of multiple structural break models is usually associated with identification of spurious break points, which are identified by universal algorithms. This leads to overvaluation of structural distress in financial markets represented by data series. The paper is focused on an estimation of the new index, which incorporates results of Student, Bartlett, GLR, Mann-Whitney, Mood, Lepage, Kolmogorov-Smirnov and finally Cramer-von-Mises tests statistics together. The new measure is named Structural Distress Index and evaluates a probability of structural break occurrence based on estimations of proposed models. SDI values show that Czech and Polish stock markets went through more instable period in 1990s than at the beginning of the global financial crisis in 2007. SDI measure is straightforward and can be easily explained, the highest values of SDI can identify the most important break points of the research period, which starts in year 1993 and ends in year 2014. Universality of SDI offers its further extension and application to further research of financial markets.

Keywords: Aggregation, Central Europe, Stability, Stock markets, Structural break analysis
JEL classification: C13, C51, G14, G15

Published: October 1, 2016  Show citation

ACS AIP APA ASA Harvard Chicago IEEE ISO690 MLA NLM Turabian Vancouver
Princ, M. (2016). Structural Distress Index: Structural Break Analysis of the Czech and Polish Stock Markets. European Financial and Accounting Journal11(3), 125-137. doi: 10.18267/j.efaj.167
Download citation

References

  1. Anderson, T. W., 1962. On the distribution of the two sample Cramer-von Mises
  2. Criterion. Annals of Mathematical Statistics 3, 1148-1159. DOI: 10.1214/aoms/ 1177704477.
  3. Andreou, E., Ghysels E., 2002. Detecting Multiple Breaks in Financial Market Volatility Dynamics. Journal of Applied Econometrics 5, 579-600. DOI: 10.1002/jae.684. Go to original source...
  4. Andrews, D. W. K., 1993. Tests for parameter instability and structural change with unknown changepoint. Econometrica 4, 821-856. DOI: 10.2307/2951764. Go to original source...
  5. Andrews, D. W. K., Ploberger W., 1994. Optimal tests when a nuisance parameter is present only under the alternative. Econometrica 6, 1383-1414. DOI: 10.2307/ 2951753. Go to original source...
  6. Brown, R. L., Durbin, J., Evans, J. M., 1975. Techniques for testing the constancy of regression relationships over time. Journal of the Royal Statistical Society B 2, 149- 163. DOI: 10.2307/2984889. Go to original source...
  7. Chow, G. C., 1960. Tests of equality between sets of coefficients in two linear regressions. Econometrica 3, 591-605. DOI: 10.2307/1910133. Go to original source...
  8. Feller, W. E., 1948. On the Kolmogorov-Smirnov Limit Theorems for Empirical Distributions. The Annals of Mathematical Statistics 2, 301-302. DOI: 10.1214/ aoms/1177730243. Go to original source...
  9. Hansen, B. E. 1992. Testing for parameter instability in linear models. Journal of Policy Modeling 4, 517-533. DOI: 10.1016/0161-8938(92)90019-9. Go to original source...
  10. Harvey, D. I., Leybourne, S. J., Newbold, P., 2001. Innovational outlier unit root tests with an endogeneously determined break in level. Oxford Bulletin of Economics and Statistics 5, 559-575. DOI: 10.1111/1468-0084.00235. Go to original source...
  11. Hawkins, D., Qiu, P., Kang, C., 2003. The Changepoint Model for Statistical Process Control. Journal of Quality Technology 4, 355-366. Go to original source...
  12. Hawkins, D., Zamba, K., 2005. Statistical Process Control for Shifts in Mean or Variance Using a Changepoint Formulation. Technometrics 2, 164-173. DOI: 10.1198/004017004000000644. Go to original source...
  13. Hjort, N. L., Koning, A., 2002. Tests for Constancy of Model Parameters Over Time. Nonparametric Statistics 1-2, 113-132. DOI: 10.1080/10485250211394. Go to original source...
  14. Kuan, C. M, Hornik. K., 1995. The generalized fluctuation test: A unifying view. Econometric Reviews 2, 135-161. DOI: 10.1080/07474939508800311. Go to original source...
  15. Lepage, Y., 1971. Combination of Wilcoxians and Ansari-Bradley Statistics. Biometrika 1, 213-217. DOI: 10.2307/2334333. Go to original source...
  16. Mann, H. B., Whitney, D. R., 1947. On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other. Annals of Mathematical Statistics 1, 50-60. DOI: 10.1214/aoms/1177730491. Go to original source...
  17. Mood, A. M., 1954. On the Asymptotic Efficiency of Certain Nonparametric Two- Sample Tests. The Annals of Mathematical Statistics 3, 514-522. DOI: 10.1214/aoms/1177728719. Go to original source...
  18. Muggeo, V. M. R., 2003. Estimating Regression Models with Unknown Break- Points. Statistics in Medicine 19, 3055-3071. DOI: 10.1002/sim.1545. Go to original source...
  19. Page, E. S., 1954. Continuous Inspection Scheme. Biometrika 1/2, 100-115. DOI: 10.1093/biomet/41.1-2.100. Go to original source...
  20. Perron, P., 1997. Further Evidence on Breaking Trend Functions in Macroeconomic Variable. Journal of Econometrics 2, 355-385. DOI: 10.1016/s0304-4076(97)00049- 3. Go to original source...
  21. Piehl, A. M., Cooper, S. J., Braga, A. A., Kennedy, D. M., 2003. Testing for Structural Breaks in the Evaluation of Programs. Review of Economics and Statistics 3, 550-558. DOI: 10.1162/003465303322369713. Go to original source...
  22. Ploberger, W., Kramer, W., 1992. The CUSUM test with OLS residuals. Econometrica 2, 271-285. DOI: 10.2307/2951597. Go to original source...
  23. Princ, M., 2014. Testing Gaussian and Non-Gaussian Break Point Models: V4 Stock Markets, Optimization, Education and Data Mining in Science. Engineering and Risk Management Working Papers vol. 8.
  24. Ross, G. J., Adams, N. M., 2012. Two Nonparametric Control Charts for Detecting Arbitary Distribution Changes. Journal of Quality Technology 2, 102-116. Go to original source...
  25. Ross, G. J., Tasoulis, D. K., Adams, N. M., 2011. Nonparametric Monitoring of Data Streams for Changes in Location and Scale. Technometrics 4, 379-389. DOI: 10.1198/TECH.2011.10069. Go to original source...
  26. Snedecor, G. W., Cochran, W. G., 1989. Statistical Methods. Iowa State University Press.
  27. Stock, J. H., Watson, M. W., 1996. Evidence on Structural Instability in Macroeconomic Time Series Relations. Journal of Business & Economic Statistics 1, 11-30. DOI: 10.2307/1392096. Go to original source...
  28. Wald, A., 1945. Sequential Tests of Statistical Hypotheses. Annals of Mathematical Statistics 2, 117-186. DOI: 10.1214/aoms/1177731118. Go to original source...
  29. Wang, Z., Bovik, A. C., Sheikh, H. R., Simoncelli, E. P., 2004. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing 4, 600-612. DOI: 10.1109/tip.2003.819861. Go to original source...
  30. Zivot, E., Andrews, D. W. K., 1992. Further Evidence on the Great Crash, the Oil- Price Shock, and the Unit-Root Hypothesis. Journal of Business & Economic Statistics 3, 251-270. DOI: 10.1080/07350015.1992.10509904. Go to original source...

This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.