On the Application of SPC in Finance

  • Vasyl Golosnoy
  • Iryna Okhrin
  • Sergiy Ragulin
  • Wolfgang Schmid


A financial analyst is interested in a fast on-line detection of changes in the optimal portfolio composition. Although this is a typical sequential problem the majority of papers in financial literature ignores this fact and handles it in a non-sequential way. This paper deals with the problem of monitoring the weights of the global minimum variance portfolio (GMVP).

We consider several control charts based on the estimated GMVP weights as well as on other closely related characteristic processes. Different types of EWMA and CUSUM control schemes are applied for our purpose. The behavior of the schemes is investigated within an extensive Monte Carlo simulation study. The average run length criterion serves as a comparison measure for the discussed charts.


Control Chart Optimal Portfolio Asset Return Exponentially Weighted Move Average Statistical Process Control 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Banerjee, A. and Urga, G. (2005). Modeling structural breaks, long memory and stock market volatility: an overview. Journal of Econometrics 129, 1-34.Google Scholar
  2. 2.
    Best, M. and Grauer, R. (1991). On the sensitivity of mean-variance-efficient portfolios to changes in asset means: some analytical and computational results. Review of Financial Studies 4, 315-342.CrossRefGoogle Scholar
  3. 3.
    Bodnar, O. and Schmid, W. (2007). Surveillance of the mean behaviour of multivariate time series. Statistica Neerlandica 61, 383-406.MATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Engle, R. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50, 987-1008.MATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Farrell, J. (1997). Portfolio Management. New-York:McGraw-Hill.Google Scholar
  6. 6.
    Golosnoy, V. and Schmid, W. (2007). EWMA control charts for optimal portfolio weights. Sequential Analysis 26, 195-224.MATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Golosnoy, V., Schmid, W., and Okhrin, I. (2007). Sequential monitoring of optimal portfolio weights, in Financial Surveillance, M. Frisen (Ed.), Wiley, New York, (2007), 179-210.Google Scholar
  8. 8.
    Golosnoy, V., Okhrin, I., and Schmid, W. (2008). Statistical methods for the surveillance of portfolio weights. To appear in: Statistics.Google Scholar
  9. 9.
    Härdle, W., Herwartz, H., and Spokoiny, V. (2003). Time inhomogeneous multiple volatility modelling. Journal of Financial Econometrics 1, 55-95.CrossRefGoogle Scholar
  10. 10.
    Hsu, D., Miller, R., and Wichern, D. (1974). On the stable Paretian behaviour of stock market prices. Journal of American Statistical Association 69, 108-113.MATHCrossRefGoogle Scholar
  11. 11.
    Lowry, C. A., Woodall, W.H., Champ, C.W., and Rigdon, S.E. (1992). A multivariate exponentially weighted moving average control chart. Technometrics 34, 46-53.MATHCrossRefGoogle Scholar
  12. 12.
    Markowitz, H. (1952). Portfolio selection. Journal of Finance 7, 77-91.CrossRefGoogle Scholar
  13. 13.
    Merton, R.C. (1980). On estimating the expected return on the market: an exploratory investigation, Journal of Financial Economics 8, 323-361.CrossRefGoogle Scholar
  14. 14.
    Michaud, O. (1998). Efficient Asset Management, Boston, Massachusetts, Harvard Business School Press.Google Scholar
  15. 15.
    Montgomery, D.C. (2005). Introduction to Statistical Quality Control, Wiley.Google Scholar
  16. 16.
    Okhrin, Y. and Schmid, W. (2006). Distributional properties of optimal portfolio weights. Journal of Econometrics 134, 235-256.CrossRefMathSciNetGoogle Scholar
  17. 17.
    Pignatiello, Jr., J. J. and Runger, G.C. (1990). Comparison of multivariate CUSUM charts. Journal of Quality and Technology 22, 173-186.Google Scholar
  18. 18.
    Rachev, S.T. and Mittnik, S. (2000). Stable Paretian Models in Finance, Wiley.Google Scholar

Copyright information

© Physica-Verlag Heidelberg 2010

Authors and Affiliations

  • Vasyl Golosnoy
    • 1
  • Iryna Okhrin
    • 2
  • Sergiy Ragulin
    • 2
  • Wolfgang Schmid
    • 2
  1. 1.Institute of Statistics and EconometricsUniversity of KielKielGermany
  2. 2.Department of StatisticsEuropean University ViadrinaFrankfurt (Oder)Germany

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